The Copernicus Atmosphere Monitoring Service (CAMS), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission, provides daily analyses and 5-day forecasts of atmospheric composition including ozone, carbon monoxide, nitrogen dioxide, sulphur dioxide, methane and aerosols in near-real time as well as a reanalysis of atmospheric composition going back to 2003. This is accomplished by combining a state-of-the art transport and chemistry model with satellite data from a wide range of sensors, among them retrievals from the TROPOMI instrument on the S5P satellite which add valuable information in the CAMS system. Ozone retrievals from TROPOMI have been assimilated routinely by CAMS since December 2018 and were followed by the operational assimilation of other TROPOMI products (e.g. NO2 and SO2). In this talk we present a status update on the use of TROPOMI products in the global CAMS system, with special emphasis on the recently activated assimilation of NO2 and SO2 layer height data, and illustrate the benefits the TROPOMI data bring to the CAMS analysis.

The assimilation of TROPOMI NO2 retrievals in the CAMS system became possible after including a simplified version of the NO2 chemistry in the tangent linear and adjoint routines of the CAMS model. The impact of the NO2 assimilation is assessed by comparison against surface air quality observations and against a control run without data assimilation. CAMS assimilates the operational NRT TROPOMI SO2 retrievals for volcanic eruptions, which are total column products that do not provide any information about the height of volcanic plumes, and also explores the use of the SO2 layer height product produced in the frame of the ESA-funded Sentinel-5P Innovation–SO2 Layer Height Project whose use can lead to improved SO2 forecasts. Furthermore, CAMS is moving closer to the operational assimilation of TROPOMI CO products, retrieved in the SWIR part of the spectrum, that bring extra information into the CAMS CO analysis which already assimilates CO products from IASI and MOPITT, leading to an improved vertical distribution of CO in the CAMS system.

More details about CAMS can be found on http://www.copernicus-atmosphere.eu and CAMS data are freely available from the Atmosphere Data Store (ADS) ads.atmosphere.copernicus.eu.

The Tropospheric Monitoring Instrument (TROPOMI) aboard ESA's Copernicus Sentinel-5 satellite (S5-P) provides the Carbon Monoxide (CO) total column data product with daily global coverage and a spatial resolution of 7x7 km2 (improved to 5.5x7km2 since August 2019). Sources of atmospheric CO are incomplete combustion e.g due to traffic, industrial activity and wild fires. CO is widely used as a tracer to analyse the atmospheric transport of the pollutant in the Earth atmosphere. Although the CO product comprises only total column amount of the trace gas, the data product includes height information because of the vertical column sensitivity of product given by the column averaging kernel.

In this study, we present a scheme to retrieve vertical CO profiles from TROPOMI data over a selected region and/or time range and under the assumption of static vertical distribution of CO, To this end, we make use of the fact that the vertical sensitivity of total column measurements changes in the data ensemble because of different observation geometries and cloud occurrence ins the observed scenes. We show results for three example cases. First, we estimate a vertical CO profile for the "rabbit foot fire" in Canada on 8th of December 2018 that shows elevated CO pollution in an altitude of of about 5 km in agreement with the airborne measurements of BB-Flux team. Second, we estimate the altitude of pollution from biomass burning events in Siberia that was transported over the ocean to Canada on the 17th of August in 2018. Our analysis agrees well with the CAMS-IFS model simulation showing an altitude of the smoke layer of about 7.5 km. Last but not least, we analyse half month of TROPOMI CO observations over the Amazon region before and during the burning season. This example demonstrates the numerical efficiency of our assimilation scheme that can be used to combine large amount of individual TROPOMI measurements. Again our findings agree well with the calculation of CAMS-IFS. Hence, our study demonstrate the validity of CO height information in the TROPOMI CO data product due to different observation geometries, and clear-sky and cloudy conditions. Our study paves the way for more elaborated assimilation schemes like the CAMS-IFS model to fully explore the wealth of information presented in the S5P CO data product.

Sentinel-5 Precursor (S5P) is the first atmospheric composition mission of EU's Copernicus Earth Observation programme. Launched in October 2017, its unique payload TROPOMI (TROPOspheric Monitoring Instrument) measures on the global scale, on a daily basis, and at unprecedented horizontal resolution, the atmospheric abundance of species related to air quality, ozone, climate forcing, UV radiation and volcanic hazards, namely: the vertical profile and column of tropospheric and stratospheric O₃, the column of tropospheric, stratospheric and total NO₂, the total column of HCHO, SO₂, CO and CH₄, cloud properties, and aerosol UV absorbing index and layer height. Additional data products are in development and verification phase. S5P contributes observational data to operational information services on our environment, e.g., the Copernicus Atmosphere Monitoring Service (CAMS) and the Copernicus Climate Change Service (C3S), both run at ECMWF on behalf of the EC. Therefore, fitness-for-purpose of the data is essential and its quality must be monitored operationally over the mission lifetime.

Procured by an international consortium supported by ESA and national agencies of The Netherlands, Germany and Belgium, the S5P Mission Performance Centre (S5P-MPC) ensures the necessary routine validation for all S5P operational data products available to the Copernicus operational services and to the public. The S5P-MPC Routine Operations Validation Service integrates several complementary validation approaches, including comparisons of S5P data to fiducial reference measurements, to ground-based monitoring networks, to other satellites, and to alternative retrievals. Routine monitoring of S5P data quality relies in large parts on the Validation Data Analysis Facility (VDAF) and its Automated Validation Server (AVS). The latter ingests ground-based data collected from ESA’s FRM projects and from monitoring networks contributing to WMO’s Global Atmosphere Watch, performs data comparisons according to state-of-the-art protocols and validation metrics, and reports S5P data quality indicators automatically on the VDAF-AVS website. Baseline quality monitoring with the VDAF-AVS is complemented with in-depth validation carried out by dedicated teams to produce consolidated validation results. The latter are published every three months in the Routine Operations Consolidated Validation Report (ROCVR), available publicly as well. Ad hoc validation support is also provided to the teams working on the evolution of S5P retrieval algorithms and data processors. The approaches developed and implemented, the successful operation of the service, and the continuous evolution of the VDAF-AVS over the last 5 years constitute a pathfinder for the operational validation of the upcoming constellation of atmospheric composition Sentinels.

In this contribution, after a brief introduction to the S5P-MPC Routine Operations Validation Service, we report the latest validation results for each of the aforementioned TROPOMI atmospheric data products generated operationally and freely available to the public. We conclude that all data products meet mission requirements, and that for several data products recent reprocessings of Level-1b and Level-2 data improve their agreement with independent measurements. We also raise particular quality features to the attention of potential users so that they can judge the fitness of S5P data for their own applications. Latest updates of consolidated validation results and other S5P validation resources can be accessed through the portal of the TROPOMI Validation Data Analysis Facility at http://mpc-vdaf.tropomi.eu .

Introduction
The Tropospheric Monitoring Instrument (TROPOMI) on board Sentinel 5 precursor(S5P) features a new aerosol product that is dedicated to retrieval of the height of absorbing tropospheric aerosols. At present, daily global observations of aerosol height are not available on an operational basis. Aerosol profiles are provided by active sensors, particularly by ground-based lidar systems or by the space-borne Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP) with a high vertical resolution, but they observe at specific locations or in narrow tracks only. Passive sensors, such as TROPOMI, can cover the entire earth in a single day and therefore could become in the future a great choice for many scientific applications like radiative forcing studies, long-range transport, modeling and studies of cloud formation processes. Therefore, validation of the Aerosol Layer Height (ALH) product is essential to understand the quality and performance of the sensor and algorithm. The ground based lidar networks (e.g., EARLINET/ACTRIS in Europe) along with ceilometers (e-profile) are designed with long-term observation in mind, and therefore a suitable source for a long-term validation for the TROPOMI ALH data. The goal of this study is to develop a NRT (Near Real Time) procedure for comparison between Aerosol layer heights derived from satellite passive remote sensing measurements(S5P/TROPOMI) and from ground based active remote sensing measurements.
Validation methodology and collocation criteria
The validation of products with a typical resolution of several kilometres against point-like ground-based measurements involves uncertainties. A key question is how well the ground-based observation represents a larger area around the measurement site and to a large extent depends on the characteristics of the station location (urban, sub-urban, mountains, valleys, etc).
For the comparison of TROPOMI/ ALH against aerosol height from ground based active remote sensing measurements, the coincidence criteria have been checked, by setting a search of the satellite data base within 50, 100, 150 km radius circles centred in the geolocation of the ground-based station and then average the values found.
The ground based measurements nearest to the S5P overpass time within 30 minutes and also within a 1,2,3 hours temporal interval have been considered during the sensitivity studies to select every available day of measurement, ensuring a sufficiently large collocation database.
The scientific community is making remarkable efforts in developing automatic ALH retrieval algorithms applied to lidar observations. In this study, we present the results of the validation analysis of the ALH satellite-based product using independent, ground-based cloud-free observations taken at 18 sites in the frame of the European Aerosol Research Lidar Network (EARLINET) during September 2017- October 2021 and NATALI (Neural network Aerosol Typing Algorithm based on LIdar data) software (https://doi.org/10.5194/acp-18-14511-2018).
EARLINET data was processed in a centralized way using the Single Calculus Chain (SCC), with specific configurations and settings; Level 2 data is compliant to all the QC procedures currently working on the ACTRIS/EARLINET database; also, the ACTRIS COVID-19 pandemic data collection https://doi.org/10.21336/gen.682q-8163 was used. Geometrical features of lofted aerosol layers (top and bottom) are calculated by applying the gradient method on the 1064 nm backscatter coefficient profile. Backscattering, extinction and depolarization information (one hour averaged) within the layers with thicknesses of more than 300 m are considered further and analysed using NATALI software to determine the type of aerosol predominant in the layer. For overpass comparisons, ALH satellite pixels are average within 0.5 degrees radius around ground-based lidar location. The figure shows an example the comparison of the satellite ALH data and the aerosol layer heights retrieved from multiwavelength Raman lidar measurements, station (INOE) in Magurele, Romania, 6km south of Bucharest. The overpass time is once or twice per day around noon local time. The vertical bars for each data point highlight the variability of the values within the averaged subset.

The Copernicus mission Sentinel-5 Precursor (S5P) is designed to investigate atmospheric composition and to perform trace gas and greenhouse gas retrievals for air quality monitoring. Spectral information covering the UV/VIS/NIR region also allows to retrieve basic cloud information, which is an important input parameter to an accurate trace gas retrieval because of potential albedo and/or shielding effects of the clouds. Operational cloud products have been provided by the German Aerospace Center (DLR) for heritage instruments like GOME, SCIAMACHY and GOME-2, are being provided for the polar orbiting S5P (operational since 2018) and will be provided for the geostationary Sentinel-4 (to be launched in early 2024). The cloud retrieval algorithms applied are called OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks). OCRA applies a UV/VIS broad band color space approach to determine a radiometric cloud fraction while ROCINN retrieves cloud-top height, cloud optical thickness and cloud albedo from measurements in the near infrared in and around the oxygen A-band. The cloud parameters retrieved by ROCINN are provided for two different cloud models: One which treats clouds more realistically as layers of scattering water droplets (clouds-as-layers, CAL), and one which treats clouds as simple Lambertian reflectors (clouds-as-reflecting boundaries, CRB). While the operational S5P cloud products are continuously and routinely validated with ground-based observations from CLOUDNET, this contribution will focus on satellite-to-satellite comparisons. Cloud retrievals with the ROCINN_CAL model are compared to cloud products from the VIIRS instrument on-board the Suomi-NPP platform which is flying in close formation with S5P.

The TOPAS (Tikhonov regularized Ozone Profile retrievAl with SCIATRAN) algorithm to retrieve vertical ozone profiles from space-borne nadir UV radiance measurements was developed and applied to TROPOMI L1B version 2 spectral data. Spectral measurements from 270 to 329 nm (band UV1 and UV2) were used after they were spectrally re-calibrated using comparisons to simulated radiances with collocated ozone profiles from MLS/Aura as input.

The TOPAS retrieved ozone profiles from TROPOMI have a vertical resolution varying between 6 and 9 km in the stratosphere (Mettig et al. 2021). Below 18 km the sensitivity is limited and the vertical resolution is reduced.
The TOPAS ozone profiles were validated using collocated stratospheric ozone lidar and ozonesonde measurements. The validation with stratospheric ozone profiles shows very good agreement with a mean bias of to within ± 5% in the 18 - 45 km altitude range and a standard deviation of 10%. In the troposphere, the validation with ozonesonde profiles shows a larger bias with up to +40% between 10 and 15 km and a wider spread of results as well. The operational ozone profile product developed by the KNMI is available since November 2021. First results of the validation of TOPAS ozone profiles and the operational TROPOMI profiles with TOPAS results will be presented.

To increase the information content and the vertical resolution below 30 km, CrIS (on Suomi NPP) measurements in the infrared spectral range between 9.35 and 9.9 μm were included in the TOPAS retrieval. The combination of collocated UV TROPOMI and IR CrIS pixels, which have a time difference of only 3 minutes, improves the vertical resolution in the troposphere (above 3 km) to about 10 km. Validation of the combined ozone profiles with tropospheric lidars and ozonesondes shows improved tropospheric profiles and tropospheric ozone column values.


References:
Mettig, N., Weber, M., Rozanov, A., Arosio, C., Burrows, J. P., Veefkind, P., Thompson, A. M., Querel, R., Leblanc, T., Godin-Beekmann, S., Kivi, R., and Tully, M. B.: Ozone profile retrieval from nadir TROPOMI measurements in the UV range, Atmos. Meas. Tech., 14, 6057–6082, https://doi.org/10.5194/amt-14-6057-2021, 2021.

CryoSat-2 was launched twelve years ago, on the 8th April 2010. It was the first European mission dedicated to the cryosphere with the objectives to monitor precise changes in the thickness of polar ice sheets and floating sea ice. The satellite carries a radar altimeter called the Synthetic Aperture Interferometric Altimeter (SIRAL) which still remains one of the most innovative altimeters in space although newer versions are boarded on different satellites. It’s still the only mission that flies an interferometer. In future CRISTAL will have similar capabilities.

In its first twelve years in space, the CryoSat mission has systematically witnessed the changes that are affecting the ice fields of our planet due to global warming. CryoSat has been able to provide global sea-ice thickness and land ice mass balance, particularly over areas where the most dynamic changes are occurring

Beyond its originally scope, the mission supports wider scientific and operational applications in several domains. The mission has been recently extended until the end of 2025 with the scope to achieve new important goals, to extend the synergy with ICESat-2 and its unequalled data set and to secure a unique long-term climate record as long as possible. This will be possible thanks to well established international collaborations with many important worldwide institutions and agencies.
Scope of this paper is to describe the current mission status, show its main achievements and provide programmatic highlights for the extended period 2023-2025.

Launched in 2010, the European Space Agency’s (ESA) polar-orbiting CryoSat satellite was the first SAR/SARIn altimeter specifically designed to measure changes in the thickness of the sea ice and the elevation of the ice sheets and mountain glaciers. After 12 years in orbit, CryoSat continues to deliver high-quality data, providing unique contributions to several Earth Science and applications domains. Going beyond its ice-monitoring objective, CryoSat has also demonstrated to be a valuable source of observations for geodesic, hydrologic and oceanographic applications by measuring multi- thematic and high-resolution geophysical parameters. Being the only satellite flying an interferometric radar altimeter, CryoSat is also an important flying laboratory providing insights which are vital to prepare for future polar altimetry missions (e.g. Copernicus CRISTAL). To enable their full scientific and operational exploitation, the CryoSat products are routinely Quality-Controlled and thoroughly Validated (QCV) by ESA with the support of external scientific and technical teams. Based on the QCV results and feedback from the user community, the data products continuously evolve in order to accommodate a growing range of users and operational services over the sea ice, the land ice and the ocean domains. In this talk, we introduce the evolutions taken on board by the latest core baselines and give an overview of the quality assessment of the CryoSat data products, with an outlook to the new processing algorithm upgrades to be implemented in the ESA operational processing chains.

The ESA Earth Explorer CryoSat-2 was launched on 8 April 2010 and initially designed to contribute to the investigation of many debatable environmental issues related to global warming and its effect on the Cryosphere. Going beyond its ice monitoring objectives, CryoSat-2 has also demonstrated to provide a valuable source of observations over the ocean as well as for inland water level monitoring. Numerous CryoSat-based studies have been carried out by ESA since the beginning of the mission in order to optimize processing algorithms for these specific applications. A major step forward has consisted to build on these R&D outcomes and transpose them into an operational framework by generating new thematic products.

Beside “Core” Level-1 and Level-2 data evolution, the product portfolio has been recently enhanced, with the development of the Cryo-TEMPO (CryoSat’s ThEMatic PrOducts) concept which is now becoming a new standard for altimetry missions. These user-friendly and rapidly evolving products build on outcomes from the science literature and R&D projects mainly funded by the ESA Science for Society programme. The first pillar of Cryo-TEMPO was the development of the CryoSat EOLIS processor, from which swath thematic data are now produced operationally, both along track and gridded, for Antarctica and Greenland in addition to selected glaciers and ice caps. The second pillar consists of generating Sea Ice, Polar Sea, Land Ice, Hydrology and Coastal thematic data products, based on state-of-the-art processing methods, including CEOS-compliant uncertainty parameters which are necessary to assimilate data into forecasting systems and constrain reanalysis of climate models. More surprisingly, magnetic data measured by the three “platform magnetometers” on-board the CryoSat satellite have also become a systematic product (Cryo-MAGNET) which are now widely used by the Swarm science community for studying multi-scale variations of the Earth’s core magnetic field.

The purpose of this paper is to provide a general overview of the Cryo-TEMPO portfolio and related R&D projects which will aim at addressing new operational and science challenges for CryoSat while paving the way for the development of the CRISTAL Sentinel Expansion mission.

After more than 11 years of operational activity of the CryoSat satellite, the performance of the SIRAL instrument is in line or better than expected. The internal calibration measurements, designed to model a wide range of SIRAL’s imperfections, can be analyzed to highlight whether/how the instrument is changing over time and as function of its thermal status. Each variation of the instrument measured by the calibration data is compensated in the Level-1 processing. Inspection of the temporal evolution of the calibration data shows that SIRAL has been stable during its lifetime. The performance of SIRAL will be presented together with the results of the stability analysis on the calibration data, to verify that the instrument fulfills the requirements and that is maintaining the performance over its lifetime. To calibrate the SIRAL interferometer, an end-to-end calibration strategy using the ocean surface as the known external target, is exploited. The interferometer can be used to determine the across-track slope of the overflown surface and the slope of the ocean surface can be considered as known, starting from the geoid. In our analysis, the accuracy and the precision of the CryoSat interferometer will be presented starting from the residual errors on the measured angle of arrival obtained after compensation of the calibration function.

Simultaneous to the calibration activities, and in the framework of its FutureEO Programme the European Space Agency (ESA) regularly carries out ground-based and airborne campaigns to support geophysical algorithm development, calibration/validation, simulation of future spaceborne Earth observation missions, and applications development related to land, oceans and atmosphere. Under such framework, ESA has been conducting airborne and ground measurements campaigns in the polar regions since 2002 supporting all phases of the CryoSat mission. In particular, an overview of the validation campaigns, how these have evolved following the mission lifetime together with the latest results will be presented.

In August 2020 the CryoSat-2 satellite started a new mission chapter, raising its operational altitude by 887 meters. This change, however small compared to the total height (719 km), enabled an orbital resonance with NASA’s ICESat-2 spacecraft. As a consequence of this the instruments of these two satellites undergo recurring coincidences over the areas scientific interest every 1.3 days.

The main instrument of CryoSat 2 is the SAR/Interferometric Radar Altimeter (SIRAL), a single frequency Ku-band radar; whereas ICESat-2 is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), which consists of six laser beams organized in three pairs: central, right, and left, as seen from the flight direction. The combined operation of these two instruments offer numerous opportunities of high scientific importance, especially because of their differences in snow penetration. Combined data of this kind has a scientific value which was already acknowledged in the past by the numerous airborne campaigns of NASA’s IceBridge mission. The current CryoSat - ICEsat configuration allows exploiting the benefits of this, while at the same time paving the way to future dual-frequency missions, such as CRISTAL.

Long coincidences occur every 19 CryoSat-2 orbital revolutions, with ICESat-2 completing 20 revolutions in the same time. But even under conditions of orbital resonance the overlap of both instruments cannot be maintained through a long geographical area. The reason for this is the large difference in orbital altitude, and therefore orbital period, between the two satellites. By tuning the target in orbit position for CryoSat 2, i.e. its orbital phase, the length of the coincidences can be maximized in the areas close to the polar regions. This leads to instrument overlap tracks that, in the best cases, can be of the order of 1000 km in length. Unfortunately, optimizing the conditions for one of the poles comes at the expense of reducing them for the opposite one.

The orbital change achieved in August 2020 was performed by means of a manoeuvre campaign with the following objectives: a) to raise the orbit up to the resonance altitude with ICESat-2; and b) to leave the spacecraft in an orbital phase such that coincidences with the ICESat-2 instrument are maximized in the Arctic. The operational concept, however, was already foreseeing potential re phasing campaigns in the future, which would optimize the conditions for the Antarctic region. The manoeuvre campaign lasted 16 days, in which a total of 14 manoeuvres were executed. This left enough time during the inter-manoeuvre periods to keep the SIRAL instrument in operation. For this purpose a mission planning orbit was created to support the transitory period between the former mission’s nominal orbit and the new one. After the orbital change CryoSat 2 has been operated in its new altitude and position, which yield periodic coincidences with the ICESat-2 instrument in the Northern hemisphere.

The CryoSat-2 mission foresees a major Antarctic campaign in November 2022. This presents an excellent opportunity to change the orbital phase, such that coincidences with the ICESat-2 instrument are switched over to the Southern hemisphere. This poster describes the details of the manoeuvre campaign necessary to achieve this, which is currently scheduled for the second quarter of 2022. The target phase is located approximately 6 degrees ahead of the current orbital position; this is equivalent to a node crossing time 100 seconds earlier than the current one. All other orbital parameters remain the same; in particular, the altitude (orbital period) that enables the resonance with ICESat 2. The orbital change consists, firstly, of a decrease in semi-major axis (altitude), which introduces an along track drift towards orbital positions ahead of the current one. This is then followed by another set of manoeuvres to recover the original altitude, stopping the along track drift, and reaching its final position. The duration of the manoeuvre campaign is a parameter inversely linked to the cost of the orbital change in terms of propellant mass, which implies a trade off between these two variables. Following the same approach as in the latest orbital change, a reference orbit dedicated to mission planning will be generated for the transitory period. This will allow keeping the SIRAL instrument active during most of the manoeuvre campaign.

CryoSat-2 is ESA's first satellite mission dedicated to measuring changes in the cryosphere and its measurements have transformed our capacity to study the polar regions. Thanks to CryoSat-2, we now have an altogether new appreciation of how Earth’s ice sheets, ice shelves, sea ice, glaciers, and polar oceans are evolving. As global temperatures have risen, so too have rates of snowfall, ice melting, and sea level rise, and each of these changes impacts upon the neighbouring land, marine, and atmospheric environments. CryoSat-2 measurements are now central to our awareness and understanding of Arctic and Antarctic environmental change; a case in point is the marine ice sheet instability that is underway in West Antarctica, widely understood to be among the greatest contemporary imbalances in the climate system, whose evolution has been charted in satellite altimeter data since its onset. In this presentation I will introduce the CryoSat-2 mission concept, describe the technical advances that have improved our capability to monitor land ice, sea ice, and the polar oceans, and review a series of flagship studies that have allowed both long-standing and unanticipated scientific problems in cryospheric research to be solved.

Sea Surface Salinity (SSS) is an Essential Climate Variable (ECV) that plays a fundamental role in the density-driven global ocean circulation, the water cycle, and climate. The satellite SSS observations from the Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) missions have provided an unprecedented opportunity to map SSS over the global ocean since 2010 at 40-150km scale with a revisit period of 2 to 3 days. This observation capability has yielded new findings concerning the SSS variations related to climate variability such as El Niño-Southern Oscillation, Indian Ocean Dipole and Madden-Julian Oscillation, and the linkage of the ocean to different mechanisms of the water cycle such as evaporation, precipitation and continental runoff. This has enhanced the understanding of various upper-ocean processes such as tropical instability waves, Rossby waves, mesoscale eddies and related salt transport, salinity fronts, hurricane haline wake, river plume variability, cross-shelf exchanges. There is also an emerging use of satellite SSS in the study of ocean biogeochemistry processes, e.g. linked to air-sea CO2 fluxes.

Following the success of the initial oceanographic studies on the retrieval and monitoring of this new variable, the European Space Agency (ESA) Climate Change Initiative CCI+SSS project (2018-2021) has aimed at generating improved calibrated global SSS fields over a 10-year period (2010-2020) from all available satellite L-band radiometer measurements (Boutin et al, 2021a, 2021b), currently being extended to 2002 from C-band radiometer measurements at regional scale. It fully exploits the ESA/Earth explorer SMOS mission complemented with SMAP and AQUARIUS satellite missions for the L-Band, and the AMSR-E mission for the C-band. The project gathers teams involved in earth observation remote sensing, in the validation of satellite data and in climate variability study.

Scientific user requirements and product specifications drive the technical developments of the CCI+SSS datasets that are exploited in ocean and climate science research. Requirements of performance, coverage, spatial and temporal resolutions, uncertainty limits, provision, format, data latency, etc... as specified by Global Climate Observing System (GCOS), are iteratively assessed during each cycle of the project, through regular engagement with user groups such as the ESA Climate Modelling User Group (CMUG), and through interaction with European and national projects and international bodies, as well as individual or institutional bilateral contacts.
To build the L-band CCI+SSS product, the merging of the three existing satellite data sets is performed after standardizing the error estimation by comparing SSS acquired at the same time by different sensors, and by extracting systematic uncertainties and random errors on SSS from each satellite mission. They are used to correct individual SSS before merging using a temporal optimum interpolation and to estimate the final uncertainty on level 4 (L4) SSS.
The comparisons of the CCI merged 3-sensor product with in situ ground truth indicate much better performances than the ones obtained with a single sensor data product, with global robust standard deviation of difference with respect to in situ references (ARGO) of 0.15 pss. Large scale interannual variability is successfully represented, and SSS variability in highly variable regions like the Bay of Bengale and in river plumes in the Atlantic Ocean is well marked, confirming the usefulness of the merged product for scientific studies.

This presentation aims at reviewing recent results obtained within the CCI+SSS project, as well as ongoing studies currently addressed in the phase 2 of the CCI+SSS project (2022-2024). In view of the results obtained, we will discuss the needs for future CCI+SSS products as considered with various data users and in view of diverse applications. For example, surface salinity distribution has a crucial impact on marine ecosystems and ocean health, and notably has been shown to be an indicator, and even, an active factor in water eutrophication mechanisms. SSS distribution is much influenced by climate variations, thus CCI+SSS monitoring capability can be a cutting-edge ingredient for the investigation of marine biogeochemistry changes in different regions over the years.

It is now recognised that climate change is accelerating the frequency and intensity of algal blooms, marine heatwaves and hypoxia events with occasionally devastating effects on the marine ecosystems. These changes impact not only the natural wildlife but affect tourism, fisheries and the population’s economies dependent on the sea. Sea surface salinity (SSS) and temperature (SST) are essential variables for monitoring and understanding ocean health. The ability to determine SSS and SST enables key measurements in understanding acidification and de-oxygenation, in response to the changing climate. These are closely related to Ocean Primary Production (OPP), which influences ocean-carbon uptake and is the base of the Ocean food web.

Coastal zones are subject to a mixture of complex natural and anthropogenic influences. River plumes provide influxes of organic material, increasing ecological complexity by the introduction of nutrients, upwelling brings cooler nutrient rich water and reefs and mangroves can protect the shore from erosion. The complexity of processes in these regions requires high resolution differentiation of different temperate and saline zones.

Typically, thermal and microwave remote sensing is employed to retrieve SST and SSS in aquatic systems. Satellite-derived ocean colour provides the opportunity to process data at a higher temporal and spatial resolution than traditional microwave remote sensing, with ocean colour satellites having pixel resolution on the tens to hundreds metre scale compared to microwave km scale.

This paper explores a new methodology to extract SST and SSS from hyper/ multispectral ocean radiance. Water leaving radiance is linked to the inherent optical properties of the water column, effected by the constituent parts for example from coloured dissolved organic matter, chlorophyll and total suspended matter. These parts contribute to the backscatter, absorption and reflectance coefficients, influencing the spectral signature of the radiance.

This paper considers two sources of spectral information, first hyperspectral water leaving radiance measured at 1nm spectral bands from 350-900nm using ground based radiometers. This was collected on a research cruise in Patagonia at the same time as a thermosalinograph collected in-water temperature and salinity. The second is multispectral satellite images from Sentinel 2, located in the sites of 4 UK Rivers, the Thames, West Gab, Liverpool Bay and Dowsing bay. The satellite images were matched with UK Smart buoys who have temperature and salinity records in 30 minute increments from 2015-2021 matching with the Sentinel 2 satellite timeframe.
The method matched both temporally and spatially, the in-water data of SSS and SST with the hyper and multispectral water leaving radiance, at the satellite or ground level.

This ground based hyperspectral matched data was used to train a linear regression model, with the inputs being the normalised remote sensed reflectance values and output either salinity or temperature, salinity estimations therefore don’t depend on temperature (but there is a correlation). The model was then able to learn the relationship between the spectral input and SSS/SST values and accurately estimates SSS and SST in test regions using just the hyperspectral signature as input.

Further work is also done on using principle component analysis (PCA) to reduce the dimensionality of the inputs whilst maintaining predictive accuracy, 0.99 of the variance was kept just from 5 input variables in the new orthogonal space, with temperature test RMSE 0.63. Feature selection was also examined to understand the spectral bands with the strongest contribution to either the salinity or temperature values, without changing the orthogonal set as PCA does. Feature selection using the Python package sklearn f_regression to select the bands with the largest coefficient values showed the importance of the 400-600nm band region in the prediction of temperature values.

Multispectral Sentinel-2 (S-2) MSI images over the UK smart buoys were used, with the benefit of the 10 m resolution, which enables to monitor smaller aquatic systems. The matched S-2 band information was applied with the same methodology as the hyperspectral ground water leaving radiance, first with a linear regression model, then using feature selection to identify important bands and inputs. The paper then looks at increasing the complexity of the model, and training machine learning algorithms such as neural networks to be able to learn more complex relationships and predict values in different optical water types, with less inputs available as typically found in satellite multispectral images.

This paper demonstrates a link between the ocean colour/ radiance values and the sea surface physical properties temperature and salinity, with a model accurately estimating SST and SSS from just radiance. This relationship between sea surface properties and spectral signatures is then tested in satellite ocean colour images which can be used to monitor these environmental properties with more regular temporal sampling, better spatial coverage and in previously inaccessible locations.
Acknowledgements: Jose Luis Iriarte for in-situ SST/SST data from Patagonia.

The present work is developed within the MedEOS project, an application development project funded by the European Space Agency (ESA) as part of the Mediterranean Regional Initiative. Its main objective is to develop and produce daily high-resolution, gap-free water quality products based on Earth observation (EO) data for the whole Mediterranean coastline. This is achieved through combining the high temporal resolution of Sentinel-3 Ocean and Land Colour Imager (S3 OLCI) and the high spatial resolution of Sentinel-2 Multispectral Instrument (S2 MSI) in a process of data fusion.
Within MedEOS, five EO directly derived water quality products are to be developed: Total Suspended Matter, Turbidity, Chlorophyll-a Concentration, Secchi Depth and Colored Dissolved Organic Matter. In addition, EO indirectly derived water quality products shall also be produced, largely relying on combination of satellite and in situ data with numerical model results: Faecal Bacterial Contamination Indicators, Eutrophication Indicators, Harmful Algal Blooms, and Global Environmental Anomaly Detection. Finally, a river plume monitoring algorithm will provide a systematic detection of plumes related to major rivers discharging freshwater into the Mediterranean basin.
Given state-of-the-art approaches for spatiotemporal data fusion available in the litterature (see review in [1]), the proposed strategy for data fusion on EO derived water quality products is a combination of reconstruction-based and learning-based approaches.
The reconstruction-based STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model) [2] is widely used in the litterature for spatiotemporal data fusion for land applications. Reconstruction-based approaches such as the STARFM relies on the hypothesis of reflectance similarity between both high and coarse-resolution sensors. If this hypothesis is often verified for land applications, it is less true on water. Lower values of water surface reflectances in comparison to land reflectances makes the reflectance similarity much more delicate to reach between two optical sensors. The higher sensitivity to atmospheric correction, higher impact of BRDF effects, heterogeneous sunglint effects and differences in spectral response for the same spectral band are all sources of discrepancies between water reflectance levels estimated from each sensor. It was therefore decided not to apply STARFM at the reflectance level but rather at the water quality products level to ensure a better consistency between products derived from both optical sensors.
Besides, STARFM is also strongly based on the assumption that objects do not drastically change between the date at which a pair of S2/S3 images exists (T0) and the date for which only a S3 image is available (T1). As it is most of the time not the case on highly dynamic ocean waters, an alternative approach is needed to enable the use of algorithms such as STARFM on water applications.
Our proposed strategy is to trick the STARFM algorithm by feeding it with an artificial pair of S2 and S3 data. In order to do so, a reference database made of small image patches is built from a S2/S3 water quality product database derived from a large time series of data. The S2/S3 pair is then created artificially through a coarse and fine resolution matching analysis between the reference database and the S3 water quality product observed on the targeted day. The matching analysis is first carried at the coarse resolution and further refined at the fine resolution. Each matching patch of coarse S3 pixels and its corresponding group of fine S2 pixels are further used to create an artificial S3 T0 product at a coarse resolution and an artificial S2 T0 product at fine resolution. Both the pair of S2/S3 artificial data at T0 and the S3 gap-filled product at T1 are given as input to the STARFM algorithm to produce the simulated product at S2 fine resolution.
The gap-filling part of the process is performed using the DINEOF algorithm [3]. The database of water quality products derived from Sentinel-3 images are combined into multiple overlapping time series of approximately 2 months. Masks are extracted from each daily data to differentiate clouds from land pixels and perform the gap-filling only on desired pixels. The daily S3 products are also subsetted to fit the extent of a targeted S2 tile.
A first run of DINEOF is performed on the target time series. Using an approach proposed in [4], outlier pixels, mainly located near the edges of clouds, are detected and further removed from the dataset before applying the second round of DINEOF. The target day gap-free product is further extracted from the gap-free time series and used as input to the data fusion part of the processing chain.
An example of results of data fusion is given in Figure 1.
Next steps in the MedEOS project will be dedicated to the integration of all the different algorithms, developed by different service providers, in a single EO Exploitation Platform solution. This solution allows for the deployment of different components of the services supply chain in separated ICT cloud providers, making all service outputs available in a unique archive and catalogue module and visible in tailor made web-based geoportal. MedEOS implementation, therefore, follows the latest guidelines from the data science community, setting up the service processors where the input data is readily available, thus avoiding unnecessary download and transfer of data and increasing the performance of the services.
Production of water quality products and application of the proposed data fusion approach shall be performed initially for one complete year (2020) within five Pilot Areas in Egypt, Greece, France, Spain and Tunisia. Validation of these delivered products will be performed by comparing results with CMEMS products OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205 [5] and with in-situ data collected by engaged users from each pilot. In a second phase, those products shall be derived and validated over the entire Mediterranean Sea coasts for a 3,5 year period, from March 2019 to September 2022.
For more information please visit the MedEOS website: https://medeos.deimos.pt/

[1] Belgiu, M., & Stein, A. (2019). Spatiotemporal image fusion in remote sensing. Remote sensing, 11(7), 818.
[2] Gao, F., Masek, J., Schwaller, M., & Hall, F. (2006). On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote sensing, 44(8), 2207-2218.
[3] Beckers, J. M., & Rixen, M. (2003). EOF calculations and data filling from incomplete oceanographic datasets. Journal of Atmospheric and oceanic technology, 20(12), 1839-1856.
[4] Alvera-Azcárate, A., Sirjacobs, D., Barth, A., & Beckers, J. M. (2012). Outlier detection in satellite data using spatial coherence. Remote Sensing of Environment, 119, 84-91.
[5] https://resources.marine.copernicus.eu/product-detail/OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205/

The Black Sea is a small enclosed basin where coastal regions have a large influence and mesoscale signals dominate the dynamics (the Rossby radius of deformation is about 20km). Large riverine inputs, mainly on the northwestern shelf, induce well-marked horizontal gradients in the distribution of the Black Sea salinity and optical characteristics: coastal and shelf waters have very low salinity and contain large amounts of optically active materials (e.g. coloured dissolved organic matter) and its oligotrophic deep sea has a salinity around 18.2. The presence of these contrasting water characteristics in a relatively small enclosed environment, combined with land contamination and the specificities of its atmospheric composition(e.g. high cloud coverage, aerosols) make the Black Sea a challenging area for the development of high quality satellite products.

We present results from the ESA EO4SIBS (Earth Observation for Science and Innovation in the Black Sea) project dedicated to the development, and subsequent scientific analysis, of new algorithms for the development of satellite products in the Black Sea. In particular, ocean colour products (chlorophyll-a, total suspended matter concentrations, turbidity) are produced from Sentinel 3 (S3) OLCI data combining different algorithms and then, a classification of water masses is proposed. A revised gridded altimetry product based on 5-Hz along track data (combining Cryosat and S3 SAR over 2011-2019) is produced and validated in the coastal zone with tide gauge data. L2, L3 and L4 Sea Surface Salinity is derived over 2011-2020 from the L-Band measured by SMOS and compared with in-situ surface salinity data from field sampling and Argo. In the presentation, we will describe the technical development that are needed to obtain high quality products in the Black Sea. An experimental CDOM product is also proposed.

All these products are integrated to further understand the Black Sea physical and biogeochemical functioning. For instance, the Black Sea mesoscale dynamics are inferred from the 5-Hz altimetry product using an eddy detection and tracking algorithm. The benefit of assimilating ocean colour data in the Black Sea operational model (also known as CMEMS BS-MFC BIO) for the prediction of the Black Sea ecosystem is illustrated as for instance the simulation of particularly intense blooms in June 2017.

EO4SIBS Gridded products are archived as CF-compliant NetCDF files and disseminated through ncWMS protocol.
We conclude with a set of recommendations for observational requirements needed to increase the quality of satellite products in the Black Sea and to be able to use the full potential of current and new information provided by satellites.

Dissolved oxygen (DO) is one of the most critical parameters for aquaculture, as it is vital for all organisms living in water. The survival, growth and food intake of fish is directly affected by changes in DO concentration. Therefore, the systematic and continuous monitoring of DO is of crucial importance for proper production management.
Remote sensing is widely used for the detection of optically active parameters such as chlorophyll-a, total suspended matter, and more. DO does not change the optical properties of water, so it is impossible to estimate its concentration directly from the reflection values of satellites. Literature has shown that the concentration of DO can be estimated indirectly as a result of its correlation with other parameters (e.g. temperature). Nevertheless, only few studies have addressed the issue using remote sensing data [1]–[4]. These studies have limitations, either because they are applied in a small area, thus, giving only local information which is difficult to transfer in a different region, or because the available in-situ data are limited. Our work aims to integrate satellite data, along with in-situ observations using machine learning techniques in order to bring forth innovative approaches on how DO can be estimated and monitored on large scale across the Aegean Sea.
Most recent satellite sensors offer a wide range of data products useful for estimating water quality parameters at large scale temporally, however, they are not suitable to be adopted as a standalone solution in real case scenarios. Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic biogeochemical and physical information on the marine environment. In the framework of our study we exploit Level-4, daily, gap-free satellite observations for chlorophyll-a concentration and sea surface temperature from multi-platform observations. We use CMEMS data to describe environmental conditions and study their correlation to the concentration of DO. Knowing this relation, in-situ observations of DO are used to train a machine learning regression model.
For the present study, we developed an approach for DO estimation based on a Support Vector Regression machine learning model, using CMEMS data and in-situ observations. A similar approach was presented by Guo et al. [2], however the study was focused on monthly and annual data, which does not correspond to aquaculture needs. Our in-situ dataset includes daily measures of DO from 3 different aquaculture sites (Agrilia Lesvos, Souda Crete, Astakos Ionian Sea), collected from May 2021 to October 2021. 80% of the dataset was used as training sample and the remaining 20% was used as test sample. For every set of coordinates an array was created including the values of chlorophyll-a and sea surface temperature, as well as the values of one, two and three days prior to the sampling.
Our preliminary results are promising and indicate a strong correlation between sea surface temperature and dissolved oxygen. The predicted and the measured values differ less than 3% in most cases. The lack of extreme incidents during the study period is more challenging for the prediction, since the algorithm is only trained for a limited range of values. When the values exceed the normal range the differences between the predicted and measured values are higher. Expanding the dataset to include more cases is essential for proper training of the algorithm and, consequently, for improving our results.

[1] E. Batur and D. Maktav, “Assessment of Surface Water Quality by Using Satellite Images Fusion Based on PCA Method in the Lake Gala, Turkey,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 5, pp. 2983–2989, May 2019.
[2] H. Guo et al., “A generalized machine learning approach for dissolved oxygen estimation at multiple spatiotemporal scales using remote sensing,” Environ. Pollut., vol. 288, p. 117734, Nov. 2021.
[3] N. Karakaya and F. Evrendilek, “Monitoring and validating spatio-temporal dynamics of biogeochemical properties in Mersin Bay (Turkey) using Landsat ETM+,” Environ. Monit. Assess., vol. 181, no. 1–4, pp. 457–464, Oct. 2011.
[4] Y. H. Kim et al., “Application of satellite remote sensing in monitoring dissolved oxygen variabilities: A case study for coastal waters in Korea,” Environ. Int., vol. 134, no. October 2019, p. 105301, 2020.

Ocean fronts are moving ephemeral biological hotspots forming at the interface of cooler and warmer waters. In the open ocean, this is where marine organisms, ranging from plankton to mesopelagic fish up to megafauna, gather as well as where most fishing activities concentrate, generating bycatches and conflicts such as depredation. Fronts are critical ecosystems and understanding their spatio-temporal variability is essential not only for conservation goals but also to ensure sustainable fisheries. Frontal activity is thus an integrated indicator of the health status of oligotrophic oceanic systems.
The pelagic realm of the Mozambique Channel (MC) is an ideal laboratory to study ocean front variability due to its energetic turbulent flow and its high biodiversity, currently under consideration of protection (marine protected areas).
We use satellite Sea Surface Temperature (SST) from GHRSST Level 4 Multi-scale Ultra-high Resolution (MUR) 0.25deg Global Foundation Sea Surface Temperature Analysis v4.2 and Sea Surface Height (SSH) from Global Total Surface and 15m Current (COPERNICUS-GLOBCURRENT) from Altimetric Geostrophic Current and Modeled Ekman Current Reprocessing. The surface perspective provided by these remotely-sensed data is complemented by analyses at greater depths using high-resolution nested ROMS model output in the MC. Based on horizontal thermal gradients computed with the Belkin and O’Reilly Algorithm and dynamical frontal structures derived from the Finite Size Lyapunov Exponents (FSLE), we define strong and moderate fronts based on their intensity. Moderate thermal fronts are uniformly spread across the MC, whereas stronger thermal fronts are more prevalent in the southern MC and along continental shelves, shedding light on frontogenesis processes. The total front count in the MC does not exhibit much seasonal change, but moderate fronts are more frequent in austral summer and autumn while stronger fronts occur during austral winter and spring. Using Empirical Orthogonal Functions and searching for relationships between fronts statistics and climatic indices (e.g. Indian Ocean Dipole, El Niño Southern Oscillation, etc.), our results suggest that a substantial part of MC frontal activity is controlled by basin-scale teleconnections while local dynamics do also play a substantive role.
Additionally, our results suggest that current satellite SST products do not reveal as much frontal activity as the one estimated by dynamical structures derived from satellite SSH. This difference apparently stems from the artificial lack of small-scale structures in oceanic temperature fields due to both blending procedure and cloud-cover-induced corrections. Improved SST products (e.g. using smaller/shorter length/time scales for the merging procedure and improving the flagging criteria) would allow a finer comparison between tracer-based and dynamically-based front detection methods.
Ongoing analyses combining our multidecadal front database with a compilation of historical biological data in the region will bring critical insight to understand how marine life interact with ocean fronts in a context of global change and how frontal variability can be used as an ocean health indicator worldwide.

Disruptive events such as conflicts or the COVID-19 pandemic can have adverse effects on regional agricultural production and food security, while at the same time rendering those regions inaccessible to organizations that aim to measure the impact of the events on food security to inform humanitarian support. Satellite Earth observation (EO) data can provide a remote look into crop planting and conditions in regions not accessible by traditional means of assessment on the ground (e.g., windshield or household surveys), while machine learning (ML) enables rapid analysis of large EO datasets. Here, we present our approach for estimating annual planted area and inter-annual planted area change using Earth observations and machine learning, which we implemented in support of the USAID Famine Early Warning Systems Network (FEWS NET).

The first step in our approach is to generate a cropland probability map where each pixel contains the probability of a pixel containing active cropland as predicted by our ML classifier (Kerner & Tseng et al., 2020). Our classification can be applied to past seasons or within an ongoing growing season. The ML classifier is trained using labeled samples located in the study region as well as globally and may come from a variety of data sources. To ensure the resulting map will have sufficient quality for downstream analysis, we iteratively improve the model classification performance using a held-out validation set of 525 samples with locations chosen using a random uniform sampling strategy in the study region. These validation samples are labeled as either planted crop or not-planted/non-crop by manually inspecting monthly high-resolution PlanetScope basemaps from the mapping year; multiple individuals label every point so that we can assess label confidence and review non-consensus labels. When estimating inter-annual change, we then difference the maps from each year to obtain a change map with four classes: stable planted, stable not-planted, planted gain, and planted loss.

Reliable area estimates cannot be obtained by pixel counting (i.e., summing the pixels in each class and multiplying by the area covered by each pixel) from predicted maps. We use the best practices outlined in Olofsson et al., 2015 to adjust our map-based areas using a reference sample drawn from the predicted maps using a random sample stratified by the map classes. We label these reference samples using the same procedure described for the validation samples, taking care to increase label confidence through consensus and thorough review. Finally, we adjust the map-based areas for each class using the confusion matrix between the predicted map classes and reference sample labels. This results in class-wise annual or inter-annual change estimates of planted area with associated standard error for a 95% confidence interval.

We present the results from applying this approach to estimate planted area change in multiple regions, including the Tigray region of Ethiopia where the ongoing civil war has been reported to have disrupted crop production and put residents at risk of famine. We estimated the change in planted area between 2020 and 2021 and found that there was some planted area loss in 2021, particularly in localized regions that may have been impacted by conflict events, but there was also significant planted area gain, likely due to lower crop production in 2020 due to drought conditions that improved in 2021.

References
- Kerner, H. R., Tseng, G., Becker-Reshef, I., Barker, B., Munshell, B., Paliyam, M., Hosseini, M. (2020). Rapid Response Crop Maps in Data Sparse Regions. Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining Workshops.
- Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57.

# Abstract
Thanks to its primary ability of restoring soil fertility, fallow practices are an integral part in cropping systems worldwide. More recently, their importance has been further pointed out, considering the related implications on climate change mitigation through carbon sequestration and biodiversity conservation (Dayamba et al., 2016; Ringius, 2002). Mapping fallow land has hence become a key challenge to assess the impact of such practices on the sustainability of agricultural systems.

Sentinel-2 missions have enhanced the ability for mapping agricultural land due to its high revisit time (5-day) and spatial resolution (10-meter pixels), easing the task of monitoring cropping practices and allowing land mapping in areas with relatively small plots. This has an even greater importance in West African countries where political and socioeconomic instability renders traditional methods for land mapping/inventory expensive and dangerous (Nakalembe, 2021; Sahajpal et al., 2020). Nonetheless, fallow land mapping has been, for the most part, overlooked in mainstream land cover products with little to no discrimination between active cropped land and fallowed land. Still, these global/regional land cover products are widely used as a basic information for many crop monitoring tasks, including yield estimation and forecasting in food security early warning systems (Nakalembe et al., 2021).

Few studies have proposed a land cover mapping methodology specifically for fallow fields and, to the best of our knowledge, only one has provided tests of such a method across the Sahel, reporting that more than 50% of cropland class in most of the global land cover products are fallow fields (Tong et al., 2020). However, the unsupervised methodology they used relies on a strong hypothesis on seasonal NDVI profiles (i.e. cropped fields have in general a lower NDVI compare to fallow fields across the cropping season) whose pertinence at both local scales and outside the Sahelian area may be questionable.

In this study, we present the outcomes of a first exploratory analysis on fine scale, remote sensing based characterization of fallow practices, carried out over a study case located in the Sudanian region of Burkina Faso. Leveraged data consist of a Sentinel-2 multi-year image time series, appropriately pre-processed and coupled with detailed, small scale in-situ data derived from a recently published agricultural land cover database for the 2015-2021 period (Jolivot et al., 2021), built up during the JECAM experiment of the GEOGLAM network (http://jecam.org/). In order to test the suitability of the Tong et al. (2020) underlying NDVI hypothesis for our study area, we first replicated the aforementioned reference methodology, but did not reach satisfying accuracies, with both producer and user accuracy below 50% and highly overestimating the proportion of fallow land when validating with JECAM database.

We then provide an expertise-based exploratory analysis of fallow-field NDVI profiles in order to come up with a more suitable set of hypotheses which could be used in the definition of a novel methodology for remote sensing based fallow mapping. Our preliminary results highlight that seasonal NDVI-based fallow discrimination approaches are not sufficient for discriminating fallow fields from other cropped areas. Conversely, we come out with several evidences that multi-year NDVI fallow characterization might a more suitable approach, for example by showing that transitions of fields from cropped to fallow and vice versa may have a measurable impact on vegetation index dynamics over multiple years (see attach figures).

In parallel, we also performed a data-driven analysis in which we use common machine learning techniques to provide automatic fallow mapping through supervised image classification. The rationale of this part of the study is two-fold : (1) assess the potential of supervised classification and build a “baseline” set of fallow maps for the period covered by the reference database, and (2) exploit a larger sets of variables, radiometric (i.e. derived from Sentinel-2 time series - vegetation, water and soil indices) as well as other types of geo-spatial data (such as soil type or rainfall data), and explore their correlation with the dynamics of agricultural practices and crop vegetation development. Although no clear hypothesis can be made yet for the design of a novel methodology suitable for upscaling, we come up with further clues that integrating multi-year strategies into state-of-the-art land cover mapping techniques may be a promising approach in tackling the complex, yet key task of fallow monitoring in West African agrosystems.

# References
Dayamba, S. D., Djoudi, H., Zida, M., Sawadogo, L., & Verchot, L. (2016). Biodiversity and carbon stocks in different land use types in the Sudanian Zone of Burkina Faso, West Africa. Agriculture, Ecosystems & Environment, 216, 61–72. https://doi.org/10.1016/j.agee.2015.09.023

Jolivot, A., […], Gaetano, R., […] Leroux, L., […] Bégué, A. (2021). Harmonized in situ datasets for agricultural land use mapping and monitoring in tropical countries. Earth System Science Data [Preprint]. https://doi.org/10.5194/essd-2021-125
Nakalembe C et al. (2021). A review of satellite-based global agricultural monitoring systems available for Africa, Global Food Security, 29 : 100543. https://doi.org/10.1016/j.gfs.2021.100543

Ringius, L. (2002). Soil carbon sequestration and the CDM: Opportunities and challenges for Africa. Climatic Change, 54(4), 471–495.
Sahajpal, R., Fontana, L., Lafluf, P., Leale, G., Puricelli, E., O’Neill, D., Hosseini, M., Varela, M., & Reshef, I. (2020). Using machine-learning models for field-scale crop yield and condition modeling in Argentina. 49º Jornadas Argentinas InformáTica, Congr. Argentino AgroinformáTica, 1–6.

Tong, X., Brandt, M., Hiernaux, P., Herrmann, S., Rasmussen, L. V., Rasmussen, K., Tian, F., Tagesson, T., Zhang, W., & Fensholt, R. (2020). The forgotten land use class: Mapping of fallow fields across the Sahel using Sentinel-2. Remote Sensing of Environment, 239, 111598. https://doi.org/10.1016/j.rse.2019.111598

The United States (US) Department of Agriculture has been producing 30-meter annual crop cover classifications over the conterminous US dating back to 2008. The product, coined the Cropland Data Layer (CDL), has been derived from a variety of satellite imagery sources but primarily includes Landsat and, since 2016, Sentinel-2. The CDL has provided the US agricultural community a rich dataset for exploring topics such as crop extent monitoring, rotation pattern modeling, and small area estimation. The now 13-year CDL archive has also provided a robust “ground truth” dataset to help extrapolate classifiers forward in time for use in real-time US crop type identification or, conversely, applied retrospectively to generate CDL-like maps prior to 2008.

What has not been explored is the potential use of spectral-temporal training signatures derived from the CDL to be applied to crop areas internationally. The idea being the development of generic training datasets, or “spectral libraries”, in the US from the CDL that can be applied, or “transfer learned”, to similar crop regimes globally. These classifications are highly desirable because there are no systematic crop type maps are being generated annually outside of North America.

To test this concept, archived Landsat 7/8 and Sentinel-2a/b imagery was used as a basis in parallel with the CDL. In short, cloud-free imagery composites were built consistently throughout each of the past five growing seasons, stacked by year, and then intersected with the corresponding CDL. Next, training signatures were randomly drawn from each of the five years, using the CDL as the label information. Then all years’ samples were merged as one to better captures across year cropping and imagery quality variability. Next, the samples were sent to decision tree style classifiers. Tested were the common machine learning tools of classification and regression tree (CART) and random forests (RF). Finally, the derived rulesets outputs from those classifiers were applied across space to the similarly composited predictor imagery for the years 2018, 2019, and 2020 and maps generated.

Crop intensive areas of the US were used to generate the training data with the focus on the ubiquitous commodities of maize, soybeans, wheat, and cotton. Example CART and RF based classifications were generated in a sampling of industrialized cropping areas around the world, including regions from both the northern and southern hemispheres. Quantitative map accuracies were unable to be produced since there is no validation data in the foreign test cases. However, results were subjectively inspected and often look quite reasonable, particularly for the RF cases. Perceived robustness and utility of the methodology will be shown as well.

Detailed parcel-level crop type mapping for the whole European Union (EU) is necessary for the evaluation of agricultural policies. The Copernicus program, and Sentinel-1 (S1) in particular, offers the opportunity to monitor agricultural land at a continental scale and in a timely manner.

However, so far, the potential of S1 has not been explored at such a scale. Capitalizing on the unique LUCAS 2018 Copernicus in-situ survey, we present the first continental crop type map at 10-m spatial resolution for the EU based on S1A and S1B Synthetic Aperture Radar observations for the year 2018. Random Forest (RF) classification algorithms are tuned to detect 3 land cover types and 19 different crop types. We assess the accuracy of this EU crop map with three approaches. First, the accuracy is assessed with independent LUCAS core in-situ observations over the continent. Second, an accuracy assessment is done specifically for main crop types from farmers declarations from 6 EU member countries or regions totaling more than 3 million parcels and 8.21 Mha. Finally, the crop areas derived by classification are compared to the subnational (NUTS 2) area statistics reported by Eurostat.

The overall accuracy for the crop type map is reported as 80.3% when grouping main crop classes and 76% when considering all 19 crop type classes separately. Highest accuracies are obtained for rape and turnip rape with user and produced accuracies higher than 96%. The correlation between the remotely sensed estimated and Eurostat reported crop area ranges from 0.93 (potatoes) to 0.99 (rape and turnip rape). We discuss how the framework presented here can underpin the operational delivery of in season high-resolution crop mapping.

The perspectives of the EU crop map are multifold, both using the final product to extract further information on the agricultural landscape and investigating future developments. We are currently evaluating the fit for purpose of the EU crop map to derive indices of pesticide risk exposure and crop diversity in space. Based on the final map, we are also investigating how to use crop area statistics at the subnational level and RF class-specific probabilities to improve the accuracy of the EU crop map. In terms of future development, the main efforts are on developing multi-year mapping approaches on specific areas.

Satellite imagery has shown large capabilities in agriculture for monitoring crop conditions. Various vegetation indices are in use for the purpose of better decision taking in agricultural practices regarding irrigation, agricultural inputs, seeding and harvesting. Most common is the use of NDVI. Though being a proven index for monitoring photosynthetic activity in the field, the possibility of consistent and continuous monitoring of crops is greatly hampered by the presence of clouds. This is why others have engaged in deriving vegetation indices based on Synthetic Aperture Radar (SAR) observations.
By being able to penetrate clouds, SAR has the potential to be a temporally consistent source of information for crop health monitoring. However, SAR vegetation monitoring comes with its own challenges, mostly being connected to high noise-to-signal ratios causing accuracy of spatial patterns to be low when compared to optical remote sensing.

The Biomass Proxy algorithm is made to integrate the temporal consistency of SAR with the spatial accuracy of optical remote sensing. A novel fusion methodology has been developed into an operational near real time product that now serves more than 20 countries spread over the globe. This methodology uses a dynamic fusion of both temporal and spatial signals of NDVI and SAR derived vegetation index at field level and produces daily biomass indications at a 10m x 10m resolution. It has been evaluated and tested in Germany, Ukraine, United Kingdom, Canada and Brazil where there are various conditions in terms of cloud cover, agricultural practices as well as satellite coverage.

As expected, performance is directly linked to the number of satellite coverages available. However, the algorithm demonstrated to be able to mitigate biomass indications with potentially lower quality, both spatially and temporally, in periods in which no optical information was available by making effective use of SAR. It also showed that noise in SAR observations did hardly propagate into temporal and spatial output using the built-in dynamic fusion weights mechanism of the algorithm. The biomass proxy has shown to be robust under various climatic conditions such as heavy rainfall, snow and drought, and consistent values have been observed for various crops such as cereals, sugarcane, potatoes, sunflowers and alfalfa. Furthermore, the methodology has been designed to be independent of the input signal source and is able to incorporate future sources and therefore different overpass frequencies such as the inclusion of Sentinel-1C and -D and Sentinel-2C and -D or other optical sources such as Planet scope.

The growing world population and increasing weather extremes pose a serious threat to food security. To meet the requirements set by SDG2 Zero Hunger, we need more than ever a timely and global view of crop conditions. To do so, we need a good understanding of where these crops are planted. Satellite remote sensing has nowadays become the most important data source to automate the mapping of agricultural areas and identification of crop types. Existing initiatives are however often characterized by one or more of the following limitations: (i) they provide only a one-time product; (ii) they do not generalize well across space and time; (iii) they do not explicitly account for local growing seasons; (iv) they do not scale well in cloud environments.

Within the ESA WorldCereal project, all these limitations are addressed in order to build the first global classification system that can produce seasonal crop mapping products at field scale within one month after the end of the growing season. The focus products of WorldCereal are: (i) 10m annual cropland extent maps, generated at the end of the major agricultural season; (ii) 10m seasonal wheat and maize maps; (iii) 10m seasonal active cropland maps; (iv) 10m seasonal active irrigation maps.

Making use of the WorldCereal community-based open reference data repository, newly created global crop calendars for summer and winter cereal crops and derived agro-ecological zones (AEZ), a total of more than 170,000 globally distributed reference samples were used to benchmark a variety of classification algorithms with a focus on spatiotemporal robustness and transferability. Inputs consist of Sentinel-1, Sentinel-2, Landsat 8, Copernicus 30m DEM and AgERA5 meteorological variables, subsequently processed into robust and normalized temporal features for further classification. The winning algorithm, a CatBoost classifier, was deployed in a hierarchical setup with a parent model trained on the entire global reference database and subsequent continued training towards local models per AEZ.

While extensive validation is still ongoing, preliminary test results over the priority areas show robust cropland (F1 0.88), wheat (F1 0.80) and maize (F1 0.85) detectors. The next step is to run the system globally for a full year, taking into account the different growing seasons and generating all above mentioned products. Results of this demonstration will be presented during LPS22. Finally, the modular nature of the WorldCereal classification module – which will become available as open-source software – allows to plugin a wide range of classification algorithms, ranging from pixel-based decision trees to convolutional deep neural networks. This ensures the future-proof nature of the system in many diverse applications.

The Copernicus Sentinel-1 (S-1) mission ensures the continuity of C-band SAR observations over Europe. The routine operations of the constellation are on-going and performed at full mission capacity, with data being acquired with both Sentinel-1 A and Sentinel-1 B units. The mission is characterized by large-scale and repetitive observations, systematic production and free and open data policy. Sentinel-1 data are routinely used by Copernicus and many operational services, as well as in the scientific and commercial domain.
The Sentinel-1 SAR Mission Performance Cluster Service (SAR-MPC) is an international consortium of SAR experts and is in charge of the continuous monitoring of the S-1 instruments status, as well as the monitoring of the quality of the L1 and L2 products. This is typically done by analyzing the variation of key parameters over time using dedicated auxiliary products or standard data available to the public. The MPC is also responsible to implement any actions necessary to ensure that no data quality degradation occurs. This includes the update of processor configuration files and updates of the S-1 Instrument Processing Facility (IPF) algorithms and/or their implementation [1].
The monitoring of both the SAR antenna health status and of the SAR instrument is carried out exploiting the dedicated auxiliary products and ensures that no degradation of SAR data quality is originated by instrument aging or element’s failures. In the case of the antenna health, the analysis is performed using the RF Characterization (RFC) products that allow to assess the status of the 280 TRMs composing the SAR antenna. The monitoring shows that there is no considerable degradation or TRM failures since 2017 for neither Sentinel-1 A or Sentinel-1 B. A small degradation of one element in H pol of S-1A has been observed since January 2021 (loss of about 3 dB gain in Rx and 1 dB gain in Tx), but with no impact in the data quality at the moment. The instrument status is monitored through the internal calibration and noise products, which can be used, for example, to generate time series of the PG product. Currently analysis shows that the overall behavior of both instruments is quite stable, with the slope of the PG gain trend below 0.1 dB/year for both units.
The radiometric and geolocation performance of L1 products is performed using standard S-1A and S-1B data, and are also stable and within specifications. In particular, the DLR calibration site composed of transponders and corner reflectors is used to assess the stability of the radiometry, and current analysis including data from 2017 until 2021 shows a mean value of -0.1 dB and standard deviations below 0.25 dB for both units. In addition to the point-target analysis, gamma measurements over uniformly distributed targets like rainforest are also used to assess a relative radiometric accuracy of S-1 products. Evaluating the flatness of such profiles, updates of the antenna patterns and processing gains are performed in order to ensure the radiometric accuracy. The geolocation accuracy is monitored through the use of dedicated acquisitions over additional calibration sites and includes the compensation of known instrument and environmental effects, e.g., propagation through troposphere and ionosphere [2]. Current analysis of the point targets shows an absolute mean value of less than 25 cm in azimuth and less than 15 cm in range for both units, and standard deviations of less than 50 cm in all cases.
The quality of the L2 products is also continuously monitored by the SAR-MPC (see dedicated presentation in [4]). During the summer of 2021, particular attention was given to this task due the successful introduction of the new SAR Wave Mode beam 2 (WV2) configuration, characterized by a higher incidence angle of beam 2 and new antenna gain settings. Following the update, the MPC performed analysis which confirmed increased SNR in the WV2 products and the corresponding improvement in the L2 products [4].
The IPF is also continuously evolved to improve the data quality and its usability. Main evolutions which have been included in the latest IPF version deployed in November 3rd, 2021 (IPF 3.4) are
• Inclusion of the RFI mitigation module, which contains time and frequency domain approaches to perform detection and removal of RFI signals [3]. Annotation from the RFI detection and correction steps has been included in dedicated fields in the metadata. At the moment, the correction step is deactivated, and we expect it will become active until the end of 2021. A dedicated presentation is planned on this topic in the Sentinel-1 session.
• Inclusion of the burst cycle ID in the product annotation, a number that uniquely identifies a burst cycle within each repeat cycle, providing correspondence between the burst cycle ID of a given sub swath and a region on Earth’s surface. Corresponding IW and EW burst ID maps will be made available to the public in the first quarter of 2022.

[1] Sentinel-1 Annual Performance Report 2020, on-line document, https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar/document-library
[2] R. Piantanida et al., "Accurate Geometric Calibration of Sentinel-1 Data," EUSAR 2018; 12th European Conference on Synthetic Aperture Radar, 2018
[3] Franceschi et al., “Operational RFI Mitigation Approach in Sentinel-1 IPF”, submitted to EUSAR 2022
[4] A. Benchaabane,” Sentinel 1 Level 2 Ocean Products Performance Monitoring: current status and evolutions”, submitted to the LPS2022


Acknowledgement:
The results presented here are outcome of the ESA contract Sentinel-1 / SAR Mission Performance Cluster Service 4000135998/21/I BG, funded by the EU and ESA.
The views expressed herein can in no way be taken to reflect the official opinion of the European Space Agency or the European Union.

Introduction

Copernicus Sentinel-1is a constellation of two C-band Synthetic Aperture Radars (SAR) operated by the European Space Agency, launched in March 2014 for S1A, flying in tandem with S1B since 2016. Their Level-1 products consist of high-resolution Radar images distributed either in GRD (Ground-Detected) or SLC (Single-Look Complex) processing. Over the ocean, these measurements depend on the surface roughness, the presence of waves and currents. The knowledge acquired since decades on the SAR data allows for the generation of Level-2 products of surface wind vectors, wave spectra and radial velocity, referred to as OCN products (ocean).

L2 OCN Data quality is constantly evolving thanks to radiometric and algorithmic improvements. This work aims at presenting the strategy, current performances and evolutions of the so-called Sentinel-1 L2 OCN products from the Mission Performance Cluster Service (S-1 MPC). This activity both depends on and benefits from evolutions of the Level-1 products quality (in a dedicated presentation)

Ocean Wind

The estimation of wind vectors is made possible by the knowledge of GMFs (Geophysical Model Function) relating the calibrated NRCS to the wind in a statistical way. These functions are precisely known and have been evolving for decades. Their knowledge allows us to estimate a SAR wind vector at a resolution of 1 km from a Bayesian inversion.

The validation strategy relies on massive comparisons with atmospheric model forecasts at various resolutions: ECMWF 10 km 3-hourly forecast, NCEP 10km 3-hourly forecast, AROME and ARPEGE. Numerous statistical diagnostics are developed that monitor both the product performances on wind speed and direction. Strategies are being developed for the incorporation of in-situ data in the Sentinel-1 CAL/VAL chain, especially, weather buoy data, other satellite missions as well as opportunity data sets (LIDARs for example).

On the algorithmic side, the planned improvements concern

strong winds and the flagging of non-wind related features treatment that can otherwise affect the wind estimates.

Increase of update frequency of the ECMWF wind forecast used as a first guess in the ocean wind measurement process

Activation of RFI mitigation applied on input Level 1 products (dedicated presentation on RFI mitigation is planned during the symposium).

Ocean Surface Waves

The Sentinel-1 derived wave measurements provide 2-D ocean swell spectra (2-D wave energy distribution, function of wavelength and direction) as well as classical integrated parameters such as significant wave height for the observed swell partition, dominant wavelength, and direction.

Several dedicated methodologies have been set up for validation. (i) For each Sentinel-1 Ocean swell spectrum measurement, a directional spectrum is systematically produced with co-located WAVEWATCH III numerical wave model (ii) As very few acquisitions are available in coastal areas where in-situ buoys are deployed, we perform a dynamical co-location. This method allows propagating wave measurements acquired by Sentinel-1A in open ocean up to the closest in-situ buoy for comparison. Such methods will be particularly interesting for cross-comparison and inter-calibration of Sentinel-1A and B swell measurements. (iii) More classical methods such as co-location against altimeters are also used and will be presented as well.

We will present here the calibration and validation methodology, the main improvements put in place in the last years and the planned improvements in the coming months.

During the summer of 2021, particular attention was given to this task due the successful introduction of the new SAR Wave Mode beam 2 (WV2) configuration, characterised by a higher incidence angle of beam 2 and new antenna gain settings. The S-1 MPC analysis which followed the update has confirmed:

Increased SNR of about 5 dB in WV2 imagettes,

Reduced bias for L2 Ocean Swell Spectra (OSW) wind speed and significant wave height estimates,



Radial Velocity

The so-called radial velocity (RVL) is related to the velocity of the scatters in the line-of-sight of the SAR antenna. Over ocean, a strong dependence to surface current and winds-waves is expected. Unfortunately, the Sentinel-1 Level 2 RVL measurements are currently coloured by the Doppler frequency (DC) derived from AOCS and the antenna DC bias (electronic mispointing). This prevents the current version of the Level 2 processor to provide calibrated RVL estimates. We will present here the status of performances achieved from Sentinel-1 measurements in the OCN products and the foreseen way forward.

The overall status is that the calibration of RVL measurement suffers for insufficient spacecraft attitude knowledge now of the generation of the OCN products. Analysis of ways to collect the attitude information with proper accuracy are ongoing.

Few improvements were applied in 2021 by the introduction of a new SAR Wave mode Beam 2 (WV2) configuration, enabling to achieve better correlation between Doppler Centroid estimates and ECMWF range wind speed.

Acknowledgement:  

The results presented here are outcome of the ESA contract Sentinel-1 / SAR Mission Performance Cluster Service 4000135998/21/I BG. Copernicus Sentinel-1 mission is funded by the EU and ESA. 

The views expressed herein can in no way be taken to reflect the official opinion of the European Space Agency or the European Union. 

The Radio Frequency Interferences (RFI) disturbance is affecting more and more spaceborne SAR missions due to the increasing number of ground (or even space) emitters transmitting in the frequency band allocated for the Earth Observation. Operative L-band SAR missions such ALOS and SAOCOM already foresee RFI mitigation strategies at processing level. Many cases of RFI contamination have been observed by Sentinel-1 users as well.

The paper focuses on two aspects:
• The global monitoring of RFI based on Sentinel-1 acquisitions
• The introduction, in the Senntinel-1 operational processor (IPF) of the RFI detection and mitigation functionality

The strategy for the global mapping of C-band RFIs is based on the use of the so-called rank echoes, the first signal free echoes of each TopSAR burst. A statistical analysis of the spectrum of the rank echoes allows to detect and characterize RFI disturbance [8]. The routine analysis of the S-1 noise products allows to create databases including all the RFI occurrence of a given S-1 cycle (12 days). The analysis of the databases allows to create global RFI contamination probability maps and to assess the temporal evolution of the contamination. The RFI databases and the probability maps are regularly published on a dedicated website https://s1rfimap.aresys.it/.

The monitoring of RFI contamination performed in the framework of the Sentinel-1/SAR Mission Performance Cluster (MPC) can trigger, in case of particular RFI contamination events, dedicated analyses to characterize the source responsible of the contamination, being it a ground-based device or another space emitter working in the same band of Sentinel-1.

The observed increasing level of contamination triggered an evolution of the S-1 IPF (the operational S-1 processor) aimed at introducing the capability of automatically detecting and mitigating RFI signals. The mitigation strategy implemented in the S-1 IPF is based on the time and frequency domain analysis of the raw data. Statistical outliers identified in one of the two domains are marked as RFI signals and removed from the raw data to reduce the quality degradation of the focused data. An example of the mitigation results is reported in the figure for a Seninel-1A image acquired over Dubai on 22nd September 2021. The image on the left shows the artifacts corresponding to RFI contamination. The same artifacts are not visible in the image on the right after reprocessing with the RFI mitigation feature.
The results of the mitigation step have been included in the new S-1 products format, providing information about the performed detection and mitigation.

The S-1 IPF version (v340) implementing the new RFI mitigation strategy was made operational on the 3rd November 2021. For the time being, only the detection feature is active in order to collect statistics on the number of products and bursts affected by RFI contamination.

Acknowledgement
The results presented here are outcome of the ESA contract Sentinel-1 / SAR Mission Performance Cluster Service 4000135998/21/I BG, funded by the EU and ESA.
The views expressed herein can in no way be taken to reflect the official opinion of the European Space Agency or the European Union.

SAR images benefit from excellent geometric accuracy due to accurate time measurements in range and precise orbit determination in azimuth. Moreover, the interferometric phase of each single pixel can be exploited to achieve differential range measurements for the reconstruction of topography and the observation of Earth surface deformation and surface motions. But these measurements are influenced by the spatial and temporal variability of the atmospheric conditions, by Earth dynamics, and by SAR processor approximations, which may lead to spurious displacements shifts of up to several meters. These effects become visible in various SAR applications including the retrieval of surface deformation applying offset tracking and various InSAR applications, which might require several post-processing steps and external information for correction.

To facilitate straightforward correction of the perturbing signals in the Sentinel-1 SAR data, the Extended Timing Annotation Dataset (ETAD) was developed in a joint effort by ESA and DLR based on research results and processor prototypes available at DLR. ETAD is a novel and flexible product for correcting the SAR range and azimuth time annotations in standard Sentinel-1 interferometric wide-swath and stripmap products. It accounts for the most relevant effects, including tropospheric delays based on 3D ECMWF operational analysis data, ionospheric delays based on total electron content (TEC) maps inferred from GNSS, solid Earth tides calculated following geodetic conventions, and corrections of SAR processor approximations. The effects are converted to range and azimuth time corrections with an accuracy at a global level of at least 0.2 m, and are provided as 200m resolution grids matching the swath and burst structure of Sentinel-1 SAR data. First experimental evaluations show that an even better accuracy of a few centimeters can be attained when applying the ETAD corrections.

The ETAD product is planned to become an operational Sentinel-1 product by the end of 2022. Currently, the processing software is undergoing pre-operation evaluation at ESA. In addition, it was integrated into the Geohazards Thematic Exploitation Platform (G-TEP) where users can participate in pilot studies and can generate ETAD products for dedicated applications. First successful usage of ETAD corrections could be demonstrated in ice velocity tracking and InSAR applications. ESA has now formed several study groups making use of the G-TEP to perform further evaluations. Our presentation will summarize the ETAD product and report on the status of operational integration. Moreover, results from the pilot studies with the G-TEP will be presented.

The current family of Copernicus Sentinel-1 (European Space Agency / European Commission) products primarily contains Level-1 Single Look Complex (SLC) and Ground Range Detected (GRD) products [1], which inherit their definition from the European heritage Synthetic Aperture Radar (SAR) satellite missions ERS-1, -2 and ENVISAT. These products have over the years proven to be reliable high-quality data sources since the start of Sentinel-1A in 2014 and users largely benefit from the open and free data policy of the Copernicus program. This has led to Sentinel-1 products being routinely used in several operational applications as well as enlarging the user base of SAR data in general.
However, the rapid increase of data volume is presenting a challenge to many users who still want to exploit this wealth of information but lack the resources for the processing needed to convert these Level-1 products to interoperable geoinformation. Cloud exploitation of data offers opportunities for accelerated data exploitation but requires new strategies of data management and provision.
In this context, the term Analysis Ready Data (ARD) has been coined and several activities have indicated the potential of enlarging the Copernicus product family by such ARD products. However, no agreement has yet been reached on the definition of ARD as different user communities perform very different analyses and thus have different understandings of analysis readiness.
With the aim to standardize different categories of ARD, the Committee on Earth Observation Satellites (CEOS) has set up the CEOS Analysis Ready Data for Land initiative (CARD4L) [2]. Different product specifications are currently being defined to provide guidelines on how to best process and organize data to serve as many use cases as possible with the respective products. One such product is Normalized Radar Backscatter (NRB) [3].
However, the CARD4L NRB specification can also be thought of as a guideline rather than a specification. It gives recommendations on what information should be included in a dataset but gives no concrete specifications on how this information should be created. For this reason, data providers are required to translate these guidelines into actual products with the software of choice to then reach out to CEOS for assessment of CARD4L compliance.

In this context an ARD product for Sentinel-1 was defined: the Sentinel-1 Normalized Radar Backscatter Product (S1-NRB). The aim is to be fully aligned with the CARD4L certification, offering a high-quality radiometrically terrain corrected SAR backscatter product with all relevant general metadata and per-pixel metadata (ancillary data layers). It is intended as a global and consistently processed product achieving the highest possible quality. It is designed to be complete to satisfy requirements of users with a prioritized focus on the Copernicus Services. Ultimately, it intends to significantly lower the effort for SAR processing for the SAR user community. Core characteristics are the structuring into the Military Grid Reference System (MGRS) tile grid for interoperability with Sentinel-2 ARD products, storage in Cloud Optimized GeoTIFFs and the provision of Spatiotemporal Asset Catalogue (STAC) metadata.
We present the characteristics of this S1-NRB product, summarize the results from several use cases conducted to ensure a high data quality, and give an outlook on the future availability.
This type of product is now baseline in the frame of the Copernicus Expansion and Sentinel Next Generation and is hence planned for further future missions.

References
[1] CLS, Sentinel-1 Product Specification, Version 3.9, 2021, https://sentinel.esa.int/documents/247904/1877131/Sentinel-1-Product-Specification-18052021.pdf/c2f9d58d-217f-e21d-548d-97a2cbd71e2b?t=1621347421421.
[2] A. Lewis, J. Lacey, S. Mecklenburg, J. Ross, A. Siqueira, B. Killough, Z. Szantoi, T. Tadono, A. Rosenqvist, P. Goryl, N. Miranda, and S. Hosford, "CEOS Analysis Ready Data for Land (CARD4L) Overview." pp. 7407-7410, IGARSS, Valencia, Spain, 2018, https://doi.org/10.1109/IGARSS.2018.8519255.
[3] CEOS, Analysis Ready Data For Land: Normalized Radar Backscatter, Version 5.5, 2021, https://ceos.org/ard/files/PFS/NRB/v5.5/CARD4L-PFS_NRB_v5.5.pdf.

The Sentinel 1 C & D Mission
The Sentinel 1 C & D (S1C and S1D) satellite program, whose Thales Alenia Space is the Prime Contractor, aims at supplying two identical observation satellites to ESA.
The Sentinel 1 C & D (S1C and S1D) satellites are part of the important Copernicus long term program founded by the European Union’s Earth Observation Programme, whose objective is the deployment in orbit a several satellites, equipped with high performance instruments and aiming at looking at the Earth and environment monitoring.
The Sentinel 1 mission is based upon a constellation of polar-orbiting radar satellites embarking a C-Band Synthetic Aperture Radars, imaging the same ground track night and day, regardless weather conditions in a combined six-days period.
The two satellites S1 C and S1 D are the “twin young brothers” of the of the already in orbit operating satellites Sentinel 1 A & B, which were launched on April 3rd 2014 and April 25th 2016, respectively. S1 C and D will perform the follow-on mission of S1 A and B.
The S1C and S1D satellites, even if they embark the same C-Band Synthetic Aperture Radars and the OCP payload, actually in use with S1 A and B, they are not fully identical to S1A and S1B. In fact S1 C and D are now equipped with the additional AIS (Automatic Identification System) payload, which will allow monitoring of the naval ships traffic.
Furthermore some performance upgrading’s have been implemented, in particular the on-board computer SMU (Satellite Management Unit) and the mass memory units DSHA (Data Storage and Handling Assembly) performance have been enhanced.
The S1 C/D project involves the contribution of several European industries, among which the main industrial partners are Airbus-De (supplier of SAR) and TESAT (supplier of OCP - Optical Communication Payload).
The first satellite S1D has recently achieved the Pre Storage Review (PSR) milestone and it is in storage in a dedicated clean room area at TAS-in-Italy Rome facility.
The second S1C satellite is presently undergoing the assembly and integration phase and its delivery is planned before the end of the 2022.
The two satellites are compatible with both Soyouz and VEGA-C launchers of Arianespace.
The paper will describe the Sentinel 1 C & D mission, as well as their design features and characteristics of both satellites.

Satellite magnetic data over the past two decades (from records of the Oersted, Champ, CryoSat-2 and Swarm missions) offer an unprecedent coverage of the dynamics within the Earth’s core [1]. Meanwhile, recent progress in simulations of the geodynamo shed new lights on the core physics, emphasizing the role played by hydro-magnetic waves [2]. The conjunction of those two (observational and numerical) constraints led to the discovery of Magneto-Coriolis modes of period about 7 years [3], which explain a significant part of the interannual magnetic field variations. As a result of the “Swarm 4D-Earth Core” project, we are now in a position to understand the physics responsible for the rapid changes observed in the rate of change of the magnetic field.

With this recent evolution of our understanding, we foresee the possibility to improve our knowledge of the base state within the Earth’s core, over which hydro-magnetic waves travel. This is the overarching goal of the “Swarm 4D-Earth Core” project continuation. We shall present the main steps envisioned to reach this target. These involve upgraded satellite datasets (including data error cross-covariances), large scale core surface flow models related to subgrid induction processes, as well as the recovery of the base state from statistical and dynamical constraints contained in the latest generation of dynamo simulations. Our project also aims at better characterizing the sensitivity of hydro-magnetic modes to the base state through eigen-mode studies [4] and direct numerical experiments. Particular care should be brought to the magnetic boundary conditions, in particular in the vicinity of the core equator, where most of the interannual magnetic signal is observed. The knowledge of the base state in the core is key for the implementation of data assimilation algorithms for geomagnetic purposes.

[1] Hammer, Finlay & Olsen. Applications for CryoSat-2 satellite magnetic data in studies of Earth's core field variations, Earth Planets Space 73, https://doi.org/10.1186/s40623-021-01365-9 (2021)
[2] Aubert & Gillet, The interplay of fast waves and slow convection in geodynamo simulations nearing Earth's core conditions, Geophys. J. Int. ggab054, https://doi.org/10.1093/gji/ggab054 (2021)
[3] Gillet, Gerick, Jault, Schwaiger, Aubert & Istas, Satellite magnetic data reveal interannual modes in Earth’s core, Proc. Nat. Acad. Sci. (in revision).
[4] Gerick, Jault and Noir. Fast Quasi-Geostrophic Magneto-Coriolis Modes in Earth's core, Geophys. Res. Lett. https://doi.org/10.1029/2020GL090803 (2021)

Low Earth orbit satellite data enable magnetic field gradient tensor elements to be estimated by taking along track and, in the case of the current Swarm constellation mission, across track differences. Here we produce time dependent core surface advective flow models by inverting geomagnetic virtual observatory (GVO) gradient tensor elements of the secular variation (SV) from Swarm satellite data every 4 months in the period 2014-2019. We apply a temporal constraint that minimises flow change between epochs, as well as spatial regularization. The data predictions show no obvious biases, and are very similar to those of the CHAOS-7 field model. Our normalised misfit to the data is 0.93, suggesting the data uncertainties have been slightly over-estimated. The greater sensitivity of the gradient tensor SV elements to higher harmonic degrees of the flow enable more coefficients to be resolved, around 160 compared to of order 100 for SV vector data. We use a variety of spatial regularizations that result in rather different flow geometries, though they all display the main features seen in previous studies, such as westward drift in an equatorial band around the equator in a region straddling the Greenwich meridian, eastward drift in an equatorial band around the equator beneath the Pacific, and the eccentric planetary gyre. Flow beneath the Pacific has non-equatorially symmetric and non-tangentially geostrophic ingredients, notably cross-equator flow in the region below Indonesia.

Rapid temporal changes in the Pacific region are seen in both SV vector field and SV gradient tensor GVO time series around 2017, most clearly in the radial SV component, and also in several of the SV gradient elements. We investigate the changes in the flow and flow accelerations associated with this geomagnetic jerk. We show that, despite the differences in the flows themselves as a result of the spatial regularization, the changes to the core surface flow beneath the Pacific associated with the jerk are consistent. In particular, the east-west component of acceleration, calculated by simple first differences of the flow with no smoothing, has the opposite sense either side of around 160 degrees W longitude, where the acceleration is very small, and changes sign at the jerk epoch. The image shows the E-W acceleration component at 170 degrees E, 160 degrees W and 130 degrees W from left to right, and for three spatial norms from top to bottom. The acceleration change is rapid (less than a year), and before and after the jerk the acceleration is essentially constant for the duration of our model. Flow accelerations in the jerk region are significantly higher than elsewhere on the core surface. There is a similarly well-defined change in the radial component of the core surface secular acceleration in the Pacific region at the jerk epoch.

Earth’s core-generated magnetic field undergoes rapid, and at present unpredictable, changes on time scales of less than a decade. These events prevent accurate forecasts of the magnetic field more than a few months ahead. Study of sub-decadal field changes has long been hampered by a lack of global coverage from ground observatories. But with more than eight years of vector magnetic field measurements now available from the Swarm mission, combined with data from the earlier CHAMP and Ørsted missions, as well as calibrated platform magnetometer data from Cryosat-2, a new, more detailed, picture of rapid core field changes is emerging. We describe observations of sub-decadal field changes captured by a regular grid of geomagnetic ‘virtual’ observatories at satellite altitude based on Swarm data. These highlight the dynamic nature of field changes in the Pacific region over the past decade, where oscillations are observed in the radial field component. Changes in the slope of the secular variation of the east-west field component have also been seen in recent years, particular in Europe, the Atlantic and Asian sectors. We describe the latest update of the CHAOS global geomagnetic field model and use it to map the origin of these sub-decadal changes at the core-mantle boundary. We reflect on the similarities between recent field changes and geomagnetic impulses observed earlier in the twentieth century and on possible explanations for the observed changes in terms of hydromagnetic waves in the core. The time series of global satellite magnetic observations is still short compared to the dynamical time scales of interest; an extended Swarm mission is therefore vital if further progress is to be made in understanding these events and using them to probe Earth’s properties and dynamics. Improvements in the temporal resolution of core signals are also needed; proposed future geomagnetic missions with improved local time coverage, in particular the NanoMagSat mission, would deliver exciting new insights on this frontier.

Geomagnetic jerks are abrupt changes in the acceleration of Earth’s magnetic field that punctuate geomagnetic records. A rich phenomenological description of these events has been constructed over the past fourty years by taking advantage of the complementary strengths of ground observatory and satellite measurements. This has raised the question whether a single physical cause can explain the important characteristics of all observed jerks. Their possible dynamical origin has been studied in recent numerical geodynamo simulations achieving a realistic interplay and time scale separation between slow convection and rapid hydromagnetic wave propagation in Earth's outer core, with these latter waves playing a key role in the generation of jerk signals. To assess the generality of this explanation, here we analyse a catalog of 14 events obtained during a 14000 year long temporal sequence from a model that is the closest to date to Earth's core conditions regarding time scale separation. The majority of jerk events are found to arise from intermittent local disruptions of the leading-order force balance between the pressure, Coriolis, buoyancy and Lorentz forces (the QG-MAC balance), that leads to an inertial compensation through the emission of rapid, non-axisymmetric, quasi-geostrophic Alfvén waves from the region where this force balance is disrupted. As in an earlier simulation, jerk events of moderate strength arise from the arrival at the core surface of hydromagnetic wave packets which originate at depth, and these account well for jerk features that have recently been documented by satellite and ground observations. The more realistic timescales in the simulation reported here allow further details to be distinguished, such as multiple temporal alternations of geomagnetic acceleration pulses at low latitudes, long-range synchronisation of pulse foci in space and rapid longitudinal westward drift of these foci at the core surface. The strongest events in the catalog arise from disruption of the leading-order force balance near or at the core surface, from the combined influence of the arrival of buoyancy plumes and magnetic field rearrangement at the core surface. The hydromagnetic waves that are sent laterally and downwards generate signals that clearly illustrate the presence of nearly synchronous 'V-shaped' magnetic variation patterns over a wide portion of Earth's surface and also at mid to high latitudes, despite the source being confined in equatorial regions of the core surface. Other well-known characteristics of strong geomagnetic jerks such as surges in the intensity of the secular variation and inflexions in the length-of-day variations are also reproduced in these events. Irrespectively of the event strength, the results therefore support the hypothesis of a single physical root cause - the emission of hydromagnetic magneto-inertial waves following a disruption of the QG-MAC balance - for jerks observed throughout the geomagnetic record.

In order to understand the processes involved in the deep interior of the Earth and explaining its evolution, in particular the dynamics of the Earth’s fluid iron-rich outer core, only indirect satellite and ground observations are available. They each provide invaluable information about the core flow but are incomplete on their own:
- The time dependent magnetic field, originating mainly within the core, can be used to infer the motions of the fluid at the top of the core on decadal and subdecadal time scales.
- The time dependent gravity field variations that reflect changes in the mass distribution within the Earth and at its surface occur on a broad range of time scales. Decadal and interannual variations include the signature of the flow inside the core, though they are largely dominated by surface contributions related to the global water cycle and climate-driven land ice losses.
- Earth rotation changes (or variations in the length of the day) also occur on these time scales, and are largely related to the core fluid motions through exchange of angular momentum between the core and the mantle at the core-mantle boundary.
The “core contributions” on these observations are all related to the core flow that can also be modelled. In terms of modelling, we consider that the core is fully coupled with the mantle and allow for viscosity, magnetic field and rotation to intervene.
Here, we present the main activities proposed in the frame of the GRACEFUL ERC project, which aims to combine information about the core deduced from the gravity field, from the magnetic field and from the Earth rotation in synergy, in order to examine in unprecedented depth the dynamical processes occurring inside the core and at the core-mantle boundary.

The lateral and vertical thermochemical heterogeneity in the mantle is a long-standing question in geodynamics. The forces that control mantle flow and therefore Plate Tectonics arise from the density and viscosity lateral and vertical variations. A common approach to estimate the density field for geodynamical purposes is to simply convert seismic tomography anomalies sometimes assuming constraints from mineral physics. Such converted density field does not match in general with the observed gravity field, typically predicting anomalies the amplitudes of which are too large. Knowledge on the lateral variations in lithospheric density is essential to understand the dynamic/residual isostatic components of the Earth’s topography linking deep and surface processes. The cooling of oceanic lithosphere, the bathymetry of mid oceanic ridges, the buoyancy and stability of continental cratons or the thermochemical structure of mantle plumes are all features central to Plate Tectonics that are dramatically related to mantle temperature and composition.We present a new global thermochemical model of the lithosphere and underlying upper mantle constrained by state-of-the-art seismic waveform inversion, satellite gravity (geoid and gravity anomalies and gradiometric measurements from ESA's GOCE mission), surface elevation and heat flow data: WINTERC-G. The model is based upon an integrated geophysical-petrological approach where mantle seismic velocities and density are computed within a thermodynamically self-consistent framework, allowing for a direct parameterization in terms of the temperature and composition variables. The complementary sensitivities of the data sets allow us to constrain the geometry of the lithosphere-asthenosphere boundary, to separate thermal and compositional anomalies in the mantle, and to distinguish dynamic vs isostatic surface-elevation contributions. At long spatial wavelengths, our model is generally consistent with previous seismic (or seismically derived) global models and earlier integrated studies incorporating surface-wave data at lower lateral resolution. At finer scales, the temperature, composition and density distributions in WINTERC-G offer a new state of the art image at a high resolution globally (225 km average inter-knot spacing).

The Goddard Laser for Absolute Measurement of Radiance (GLAMR) laboratory at Goddard Space Flight Center (GSFC) is a state-of-the-art spectral and radiometric sensor characterization facility. The work is based on the calibration methodology developed by National Institute of Standards and Technology (NIST) called Spectral Irradiance and Radiance Calibration using Uniform Sources (SIRCUS). The GLAMR facility consists of a set of tunable lasers that have combined continuous scan capability across the 340-2500 nm spectral range; 1-meter integrating sphere with selectable 203-mm, 254-mm, and 305-mm diameter exit ports; wavemeters; laser spectrum analyzers; monitor radiometers; transfer radiometers; various control electronics and computers. The whole GLAMR system can travel to the sensor vendor facility for measurements. GLAMR combines spectral and radiometric characterization into one measurement sweep by creating a monochromatic extended source by fiber-coupling the tunable lasers to the integrating sphere. Spectral position of the light is measured with tens-of-picometer accuracy, and the radiometric scale can be transferred with 0.24 – 1.4 % (k=2) uncertainty depending on spectral region. This talk will provide a description of the GLAMR system, operation, spectral and radiometric traceability, and examples from recent (Landsat 9 OLI-2 and JPSS-4 VIIRS) and upcoming (PACE Ocean Color Instrument and CLARREO Pathfinder) deployments.

The objective of the SPAR@MEP project is to derive a consistent long-term data set of Aerosol Optical Thickness (AOT) and surface Bidirectional Reflectance Factor (BRF) using the SPOT- VGT and PROBA-V MEP archive with the Combined Inversion of Surface and AeRosol
(CISAR) algorithm. This archive contains more than 20 years of observations starting in 1998 acquired in four spectral bands (blue, red, NIR and SWIR) by three satellites: SPOT-VGT 1 (04/1998 – 04/2012) and 2 (01/2003 – 06/2014) and PROBA-V (04/2013 – 10/2021). The first step of this activity consists in the verification of the temporal consistency of the MEP time series obtained from the three different sensors that are considered in some sense compatible because of their similar design. This continuous time series is harmonised with respect to a common calibration reference. For that purpose, time series of clear-sky Top-Of-Atmosphere BRF has been extracted over the pseudo invariant calibration site (PICS) Libya-4.

Our calibration reference consists in simulated TOA BRF and is referred to as Libya-4 Rayference Calibration Reference (LRCR). Four different radiative transfer models have been used for the generation of LRCR. The estimation of LRCR uncertainty relies on a two-fold approach. Firstly, LRCR has been compared with observations acquired over Libya-4 by well-calibrated radiometers such as MODIS or S2/MSI for close-to-nadir viewing conditions and Sun zenith angles not exceeding 30◦. Secondly, the LRCR angular consistency between nadir and large viewing conditions have been estimated. LRCR, the mean accuracy of which has been estimated to about 2%, is used as an absolute reference to correct differences between the tree satellites.

This correction accounts for the sensor spectral differences between the radiometers, i.e., the time series is harmonised but not homogenised. Each sensor is calibrated to the common reference in a way that maintains the characteristics of that individual sensor such that the calibration radiances represent the unique nature of each sensor. This means that two sensors which have been harmonised may see different signals when looking at the same location at the same time where the difference is related to known differences in the responses of each sensor such as differences in the sensors spectral response functions. In the present case, only minor corrections are applied to PROBA-V data. For SPOT-VGT1 and 2, there corrections are in the range of 1 to 4%. Additional work is still needed to characterize the radiometric uncertainty at the pixel level.

For more than 15 years, the Moon has been evaluated as a valuable radiant calibration source for Earth observation sensors. The absence of an atmosphere, its temporal stability and high and predictable availability makes it an ideal target for the vicarious calibration of optical sensors. The photometric stability of the surface of the Moon is estimated to be of the order of 10 9 per year.
In 2017, ESA launched an initiative to derive the new “Lunar Irradiance Model of ESA” (LIME). The model is based on automated measurements of the total lunar irradiance, taken at high altitude using a multispectral CIMEL CE318-TP9. The instrument was calibrated and characterized at the National Physical Laboratory, against SI-traceable standards. The University of Valladolid mounted the instrument on the Pico Teide in Tenerife, where it continues to perform nightly lunar irradiance measurements for lunar phases angles between -99° to +99°, at a frequency of about 100 quality controlled measurements per year.
A follow-on project is currently on-going to take new measurements and develop further improvements to the model with better the spectral resolution, improved spectral interpolation between model wavelengths and a more detailed uncertainty analysis. It is also planned to develop, within the project, a reference implementation to be released in the form of an open source repository. The current status and achievements of this project are demonstrated in this presentation.
Up to now, the model has already been applied to calibrate different spaceborne sensors, mostly on an ad-hoc basis (Nemenam et al.). The PROBA-V was routinely calibrated during its mission life-time using vicarious calibration methods. As part of the operational radiometric calibration, the PROBA-V instrument has been taking lunar acquisitions (Sterckx et al.). For eight years, the moon was imaged twice a month at a constant phase angle, 7 degrees before and after full moon. This dataset has been compared with the LIME model during the instrument operational phase.
Since mid-2020, PROBA-V operational phase was transferred to the ‘experimental’ modus. With the instrument recording only about 25% of the nominal earth-view acquisitions, the freed capacity has been used to record extra lunar data. Up to now, 7 full lunar cycles of -90 degrees to 90 degrees phase angle with a granularity of 10 degrees have been acquired. This data is being be used to support the development and further understanding of the LIME model.
With the nominal calibration acquisitions of the moon at constant phase, only a small part of the model is exploited. This presentation will show how LIME is applied to calibrate the PROBA-V sensor and how the results of the experimental acquisitions contribute to the model development.


Neneman, M.; Wagner, S.; Bourg, L.; Blanot, L.; Bouvet, M.; Adriaensen, S.; Nieke, J. Use of Moon Observations for Characterization of Sentinel-3B Ocean and Land Color Instrument. Remote Sens. 2020, 12, 2543. https://doi.org/10.3390/rs12162543

Sterckx, S.; Adriaensen, S.; Dierckx, W.; Bouvet, M. In-Orbit Radiometric Calibration and Stability Monitoring of the PROBA-V Instrument. Remote Sens. 2016, 8, 546. https://doi.org/10.3390/rs8070546

Planet currently operates a constellation of about 200 high resolution cubesats spanning three major design generations and 6+ years of collecting Earth imagery. In addition to significant improvements in telescope design, the Dove satellites have benefited from three major updates to their imaging sensors, introducing spectral band responses matching those of Sentinel-2 bands and increasing the number of bands from four to eight.
Satellites are launched into sun synchronous orbits (SSO) in groups (called "flocks") of varying numbers and time intervals, a process designed to refresh the fleet by replacing older satellites. The large number of operational satellites, heterogeneous nature of their design, and wide range of operational states present significant challenges to providing reliably consistent radiance products. These products form the basis for higher level Planet analytic product offerings, including surface reflectance, normalized Basemaps, and Planet Fusion data.
Interoperability and consistency between all of Planet’s satellites is essential. We have recently moved to using Sentinel-2 as the radiometric reference for both Doves and SkySats. Sentinel-2 has matching
bands for the more recent Dove satellites and both constellations occupy similar SSO orbits, this allows sufficient crossovers for deriving accurate calibrations. For the older Doves with sensor band responses significantly different from Sentinel, we collect crossovers over well characterized calibrations sites. For the newer Doves, crossovers with Sentinel-2 are collected world-wide, allowing for larger quantities of calibration data and conditions covering a wider dynamic range. These world wide crossovers are randomly split into two collections, one used for calibration and the other for validation.
Planet also conducts a program of monthly Moon image collections by every Dove satellite. Each satellite images the moon over the complete set of phases. Subsequent comparison to the ROLO model, which provides < 1% relative accuracy, allows for general monitoring of the health of the Doves as well as the ability to make small relative calibration adjustments.
Using crossovers collected over the same time period of each calibration update, comparisons with the RadCalNet network of sites provides another independent and traceable source of radiometric validation. This is monitored on a per-flock basis over time during the entire life of each satellite.
In this presentation we will provide an overview of the current process for updating the calibration of Dove satellites while in orbit, from initial commissioning to regular biannual incremental updates. Our program of lunar monitoring will be described, and specific examples shown for both health monitoring as well as relative, per-satellite calibration adjustments. We will then show results of this ongoing campaign along with the validation of the latest radiometry.

The Copernicus Sentinel-2 Global Reference Image (GRI) was originally designed with the main goal of being used by the ESA Sentinel-2 (S2) Ground Segment Level-1 processor to geometrically refine S2 data. The usage of the GRI has shown an actual improvement of the S2 data geometric performance. Indeed, both absolute geolocation and relative (multi-temporal) performances have significantly improved and meet the expectations.

However, for now, the GRI is an internal ESA product not easy to handle and used mostly by expert users.
Therefore, the usage of the GRI as Database of Ground Control Points (GCPs) has been identified as an important improvement to be easily used as the future common layer of reference to refine imagery of several missions (including S2).

The proposed GRI version as database of GCPs is a preselected set of points of interest extracted from the Level-1B images that compose the GRI. The parameters are carefully tuned to provide a sufficient and homogeneous density of points for most applications related to geometry. It will have the same coverage and geometric properties than the current GRI version, and it will be usable also for many imagery data with lower spatial resolution (up to 50m).
Finally, this new GRI version will have a lot of advantages: easier to manage than raw L1B images, lighter, only constituted of validated and qualified points ensuring the repetitiveness of the co-registration. It will also be freely distributed to users for their own geometric issues.

In this presentation, we first recall the concept of GCP and how the GCP database is generated using the current multi-layer GRI version and the new Copernicus DEM at 30m resolution. The benefit of using this new GRI version as database of GCPs instead of the current multi-layer GRI version for geometric refining of S2 data is also presented. The extension of the use of this new GRI version to other missions is also highlighted.

The evident effects of climate change require the systematic and timely monitoring of the arable lands and grasslands. The new era of the Common Agricultural Policy (CAP2020+) has introduced the concept of the exhaustive monitoring, as opposed to the sampled-based current operating model, and the optimization of its Integrated Administration and Control System (IACS). The new CAP will attempt to shift towards a more regional based ensemble of regulations, according to the requirements and the particularities of the local ecosystems. These are mentioned as Member State (MS) specific eco-schemes for Pillar I and agri-environmental measures for Pillar II. The Sentinel satellite missions have been acknowledged as a key component for the provision of reliable solutions for CAP monitoring, thanks to the high spatial and temporal resolution data that they freely offer.
There is a number of studies that have used Sentinel data, and particularly Sentinel-1 and Sentinel-2, in the context of CAP-specific grassland monitoring. Monitoring of grassland activity and the detection of abrupt changes, such as mowing and grazing events, is required for the compliance verification that is accordant to CAP’s direct payments rules (Pillar I), as well as for the conceptual design of targeted agro-ecological and climate-focused policies (Pillar II). In order to detect abrupt changes on grassland we need uninterrupted optical imagery time-series, i.e. from the Sentinel-2 mission. Nevertheless, the continuity of the Sentinel-2 time-series is often hindered by cloud coverage, which is even more of an issue for Northern European countries. In order to tackle this problem, many research studies suggest the complementary usage of Sentinel-1 Synthetic Aperture (SAR) data, which are mostly weather independent, when Sentinel-2 data are affected by clouds.
In this work, we propose deep learning architectures in order to i) generate dense cloud-free Normalized Difference Vegetation Index (NDVI) time-series at the pixel level and ii) precisely track the occurrences of abrupt changes that can be characterized as mowing events. Moreover, we propose a scheme for the quantification of the grassland use intensity that is based on the frequency of mowing events, predicted at the previous step. The area of interest for this study is the country of Lithuania for the year 2020. Specifically, in order to generate dense NDVI time-series, we implement a Recurrent Neural Network (RNN) architecture that takes as input Sentinel-1 time-series along with any available, namely cloud-free, Sentinel-2 images. Consequently, these artificially produced NDVI time-series are given as input to a similar RNN architecture, aiming to accurately detect mowing events.
Finally, the pixel-level decisions are aggregated indicating the agricultural activity at the parcel level. The overall evaluation of the proposed methodology was achieved using annotated event instances that were generated through a sophisticated photo-interpretation process, performed by three experts.
Acknowledgements: This work has been supported by the EU's Horizon 2020 innovation programme under grant agreement H2020-869366 ENVISION.

European rural landscapes are under pressure due to rural-urban migration, change of livelihood strategies, socio-economic and institutional changes regarding land use, climate change, and invasion of alien species (IAS). Agricultural land abandonment is probably, the most widespread land change process in Europe and affects both croplands and grasslands. Agricultural land abandonment may result in the formation of novel ecosystems with positive but also negative impacts on the environment. Evidence shows that IAS may spread over abandoned agricultural lands (Masemola et al., 2020), resulting in diminishing the ecosystem services and thus, have significant negative implications for human livelihoods and human well-being. Among IAS common in Europe is Giant Hogweed (Heracleum Sosnowskyi), further, H. Sosnowskyi. It was introduced early 20th century in some European countries as an ornamental plant originally from the Caucasus. During the 1950s, it has also been planted as a livestock fodder crop due to the substantive biomass potential of this plant. Following the demise of the Soviet Union, H. Sosnowskyi went out of control and is now rapidly spreading over in Eastern European countries. H. Sosnowskyi aggressively spreads, it is difficult to control H. Sosnowskyi. H. Sosnowskyi is also dangerous to humans and animals. Satellite remote sensing provides an unprecedented source to monitor the state of the land cover land use, particularly with the advent of ESA’s Copernicus Sentinel monitoring program- SAR Sentinel-1 and optical Sentinel-2 satellites, and therefore, extending the availability of optical Landsat observations dating back to 1980s. Our major goal was to document the agricultural land-cover change from 1990 to 2020 in temperate Eastern Europe (with a strong focus on European Russia and neighboring countries), namely transitioning of managed grasslands and croplands to abandoned lands at a different stage of natural succession, but also to reveal the spread of H. Sosnowskyi and urbanization. We tested the suitability of random forest classifier as well as U-Net architecture for convolutional neural networks. Training and validation data have been collected with the use of very-high-resolution satellite imagery available via Google Earth, Planetscope constellations, dense Landsat, Sentinel-2 composites. Work has also been complemented by validation data collection during the field campaigns, attracting trained volunteers and open-access crowdsource information. We employed available Landsat-based land-cover maps circa 1990 from the University of Maryland (Potapov et al., 2014) and circa 2015 from Greenpeace-Russia (Glushkov et al., 2021) to attribute the transitions from managed cropland and grassland to different stages of agricultural land abandonment for a Russia (European part) and neighboring countries. Further, we complemented the analysis with detailed elaboration on land use and land cover by 2020 with the analysis of Sentinel-1 and Sentinel-2 time series and zoomed-in Moscow province of Russia to understand the spread of H. Sosnowskyi. Our broad-scale mapping of land-cover change using Landsat-based from 1990 to 2015 showed a widespread abandonment of croplands and grasslands, particularly in European Russia, albeit with remaining cultivation spots, such as in Vladimir and Kaluga provinces, but also in the forest-steppe zone. Many abandoned lands became naturally afforested, yet the capacity still exists for further natural afforestation. The results of broad-scale analysis correlated well with change observed from the official agricultural statics at the province level. The detailed zoom-in to Moscow province showed that only a small portion of managed grasslands in 1990 remained mown by 2020. Formerly managed croplands and grasslands transitioned to unmanaged grasslands, areas with early shrub succession and young forest, but were also contracted due to urbanization and the spread of H. Sosnowskyi. Out of 1,790 thousand hectares of managed agricultural lands, approximately 8% were encroached by H. Sosnowskyi by 2020. From a remote sensing aspect, Sentinel-2 served as a source of data to develop monthly cloud-free composites. Preliminary analysis showed, while the add-on of Sentinel-1 derivatives did not result in higher classification accuracies, once it has been added to Sentinel-2 composites, nevertheless VV, and VH polarizations were useful to distinguish mowing activities. Feature analysis with random forest showed a contribution of Sentinel-2 SWIR bands and simple seasonal metrics, such as standard deviation calculated from May 1st to October 1st. We also tested and did not find a significant contribution of the Hogweed Index (which is based on the difference between green ad NIR bands) because it is probably represented with uncondensed information already available in the classified layer stack. Our further experiments with U-Net CNN showed the great potential of this classification approach. Yet, the requirement of having a large training set and complexity in the parameterization of -Net CNN leaves room for random forest classifier.

In sum, our study showed a massive agricultural land-use change transformation after 1990, resulting in cropland and grassland abandonment with subsequent shrubs and young trees encroachment. However, alternative trajectories of grassland contraction, as the case study in Moscow province showed, primarily urbanization and invasion of H. Sosnowskyi, additionally reduces available grasslands and meadows in temperate Europe. Our study showed a great advantage of using Sentinel-1 and Sentinel-2 time series, particularly the latter one, to map shrubs and young trees encroachment as well as the spread of H. Sosnowskyi. The invasion of H. Sosnowskyi is worrisome as it already affects many European countries-for instance in Denmark, Germany, France, the Baltic States, Russia and Ukraine. Therefore, our study shows how H. Sosnowskyi and the transition of grasslands can be monitored timely. Last but not least, we postulate, the withdrawal of lands from agricultural land use and restoration of vegetation should be steered rather left for “nature”, as it may result in unintended invasions of alien species, such as H. Sosnowskyi.

Cited literature
Glushkov, I., Zhuravleva, I., McCarty, J.L., Komarova, A., Drozdovsky, A., Drozdovskaya, M., Lupachik, V., Yaroshenko, A., Stehman, S.V., Prishchepov, A.V., 2021. Spring fires in Russia: results from participatory burned area mapping with Sentinel-2 imagery. Environ. Res. Lett. 16, 125005. https://doi.org/10.1088/1748-9326/ac3287
Masemola, C., Cho, M.A., Ramoelo, A., 2020. Sentinel-2 time series based optimal features and time window for mapping invasive Australian native Acacia species in KwaZulu Natal, South Africa. International Journal of Applied Earth Observation and Geoinformation 93, 102207. https://doi.org/10.1016/j.jag.2020.102207
Potapov, P.V., Turubanova, S.A., Tyukavina, A., Krylov, A.M., McCarty, J.L., Radeloff, V.C., Hansen, M.C., 2014. Eastern Europe’s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2014.11.027

Meteorological extreme events such as droughts have severe impacts on grassland ecosystems globally. Droughts can result in harvest loss, grassland degradation, and impacts on species composition. As a consequence of ongoing climate change, drought periods will become more frequent, more severe, and longer in the coming decades in many regions of the world. In Central Europe, grassland vegetation substantially deteriorated immediately in response to extreme droughts in recent years with major impacts on livestock farming. Time series of vegetation indices (e.g., Normalized Difference Vegetation Index) were most commonly used to analyze changes in grassland vegetation during drought periods from remote sensing data. Yet, a vegetation index value as such does not allow the differentiation of percent ground cover of different vegetation and non-vegetation land cover fractions. Fractional cover provides physically-based estimates of vegetation dynamics during droughts, thereby contributing to a better understanding of drought effects on grasslands and their spatial variability.
We developed a new approach to assess drought impacts in grasslands based on intra-annual time series of fractional vegetation cover. Specifically, our method produces intra-annual time series of the Normalized Difference Fraction Index (NDFI) from Sentinel-2. The NDFI is based on fractional cover estimates of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and soil. It allows for an integrated assessment of the three fractional cover components of grasslands by contrasting NPV and soil relative to PV. The NDFI is specifically sensitive to intra-annual drought effects, as it takes a pre-season NPV base value into account, thereby capturing the drought-relevant changes of NPV fractional cover within each growing season. In our previous study in a major Central European grassland region (Kowalski et al., 2022), we showed that (i) fractional cover time series of PV, NPV and soil can be reliably derived from spectral unmixing using a constant set of library spectra representative of the entire time series and study site and (ii) that the calculated NDFI time series tracked vegetation changes during drought periods well in line with several meteorological and hydrological drought indices. Beyond that, the high spatial resolution of the Sentinel-2-based NDFI revealed strong spatial variations of drought impacts in Central European lowlands, highlighting the value of Sentinel-2 for capturing detailed grassland dynamics during extreme meteorological events.
In this study, we extend the applicability of the NDFI for grassland drought monitoring across the larger environmental and land use gradients in Europe. We investigate twelve regions in typical land management settings thereby covering common climatic (e.g., Atlantic to continental climate) as well as topographic (e.g., lowland to alpine elevation) gradients. We base our analysis on the full Sentinel-2A/B archive from 2017 to 2021. To estimate fractional cover time series from Sentinel-2, we first build a global spectral library of pure PV, NPV, and soil spectra based on Land Use/Cover Area frame Survey (LUCAS) grassland samples. We verify the consistency of the library spectra by investigating NDVI vs. SWIR ratio across feature space distributions confirming temporally consistent spectral feature spaces for the test regions. Using the global spectral library in a regression-based unmixing approach, we derive fractional cover time series to calculate intra-annual NDFI time series from 2017 to 2021. First results for the grassland regions distributed along environmental as well as land use gradients in Europe underpin the potential for wall-to-wall monitoring of grasslands with the drought-sensitive NDFI on national to continental scales. Further research is required to assess the NDFI for global mapping of grassland drought, e.g., in semi-arid to arid grassland systems with different climate conditions and drought regimes.

References:
Kowalski, K., Okujeni, A., Brell, M., Hostert, P., 2022. Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series. Remote Sensing of Environment 268, 112781. https://doi.org/10.1016/j.rse.2021.112781

Livestock are an important source of proteins (meat and milk) for large groups of pastoralists in Africa. Quantitative data, with high spatiotemporal resolution, on biomass and biomass changes are vital to support management of grazing resources in Africa. The RAMONA (RAngeland MONitoring for Africa using Earth Observation - Continental Demonstrator) project will produce several spatial resource related products for rangelands in Africa based on Earth Observation data from Sentinel 1, 2 and 3. Biomass and biomass anomalies, two of the products, are briefly described here.

Herbaceous rangeland biomass (a RAMONA core product) [t DM ha-1] is the net accumulation of the photosynthetic gain of carbohydrates (gross primary productivity, GPP, [g C m-2 day-1]) and losses through autotrophic respiration (Ra). Whereas GPP can be estimated by remote sensing, Ra can’t. In limited areas it may be possible to estimate grassland biomass productivity by linear models directly from GPP, or by using simple scalars to estimate net primary productivity (NPP) from GPP as in the current Copernicus Global Land Service Dry Matter Productivity product where dry matter productivity is assumed static and equal 0.5 x gross dry matter production. However, in Africa, there is in general insufficient ground data for calibration and validation. An alternate solution is to combine remote sensing and ecosystem modelling. Using an ecosystem model to convert GPP to NPP and biomass provides better constraints to the relationships across an extensive geographical area. The seasonal and spatial dynamic in the model is provided by GPP and originating from Sentinel based Earth observation data. GPP is estimated via a state-of-the-art implementation of the light use efficiency (LUE) methodology based on time series of fAPAR from fused Sentinel 2 and 3 data. The general algorithm quantifies GPP [g C m-2] as a product of the absorbed photosynthetic radiation (APAR=fAPAR x PARin, [MJ]) and the efficiency by which APAR is converted into carbohydrates, εmax [g C MJ-1] and environmental scalars that downregulates εmax based on resource limitation or environmental constrains:

GPP = εmax • fAPAR • PARin • scalars (1)

GPP and NPP will be calculated every five days as accumulated GPP – autotrophic respiration (Ra). Biomass dry weight is assumed to equal NPP/0.45 assuming a carbon content of 45%. εmax denotes the maximum rate of conversion of light energy and varies with photosynthetic pathway (C3/C4), may vary spatially and temporally. Candidate values for εmax will be derived from; 1) current eddy covariance flux measurements from African rangelands sites, 2) historical flux measurements from the FLUXNET2015 data base and additional African sites, 3) extracted from literature and 4) extracted from existing LUE models such as MOD17 and similar models based on Earth observation.

fAPAR can be accurately estimated based on satellite data and is a key variable for the methodology. Candidate variables for deriving fAPAR include several vegetation indices (NDVI, EVI, kNDVI) and the S2ToolBox Level 2 fAPAR product. Candidate variables will be temporally gap-filled using fused S1/S2/S3 data with additional noise removal by time series processing using the TIMESAT package (https://web.nateko.lu.se/timesat/timesat.asp). TIMESAT-smoothed data from the S1/S2/S3 fusion provides regular and less noisy (due to clouds aerosols etc) input for downstream processing. NDVI has been frequently used in to estimate fAPAR, and this relationship has been shown to be linear in the Sahel. However, across large areas the influence of several factors affects the relationship, particularly variations in soil background colour. Several studies have found that the EVI has better relationship with fAPAR than other vegetation indices. These properties relate strongly to the broader dynamic range and lower sensitivity to soil background of EVI, but there is also evidence that the efficiency of EVI is related to its response to fAPARchlorophyll while NDVI is more related to fAPARcanopy. These reasons help explaining why EVI has been successfully used for GPP estimation across a range of biomes, including grasslands, and has been applied in African drylands.

PARin, the incoming photosynthetic radiation quantifies the energy available for conversion to biomass. PARin is normally derived as a fraction (0.48) of incoming shortwave radiation and are depending on position, time, cloudiness and aerosols. Candidate variables to estimate PARin include ERA5-Land reanalysis data set (Surface net solar radiation) and the Daily Downward Surface Shortwave Flux provided by EUMETSAT.

Scalars, quantifying environmental downregulation of the LUE include, temperature and moisture availability. The scalars vary over space and time and within rangeland types and plant functional types. For down-regulation due to temperature we will mimic the MOD17 algorithm with further calibration from flux measurements. Candidate variables to estimate temperature include ERA-5 Land, 2m temperature.

Water availability can be expressed in several ways. Vapour pressure deficit (VPD) is commonly used to assess moisture stress and has been applied successfully in Africa even if the advantage of using soil moisture have been stressed. Candidate variables to estimate water availability include: VPD will be derived from temperature and relative humidity, available from ERA5-Land. Soil moisture available to plants can be obtained at 20 m spatial resolution through the modelling of soil evaporation and plant transpiration (evapotranspiration – ET) using optical and thermal data as successfully exemplified in ESA Sen-ET (https://www.esa-sen4et.org/) and ET4FAO (https://et4fao.dhigroup.com/#/) projects and as have been applied for quantifying soil moisture stress on crops. Alternatively, soil moisture estimates are available from ERA-5-Land. Evaporative fraction (LE/(LE+H), where LE = latent heat and H = sensible heat, has been reported strongly correlated to GPP and, similarly to soil moisture, could be obtained through own modelling or derived from ERA5-Land.

NPP will be derived from GPP based on the carbon used efficiency (CUE, NPP/GPP) derived from one or several state-of-the-art dynamic vegetation models, as CUE has been shown to vary substantially over time and space. This approach will provide a temporally and spatially distributed CUE per plant functional type in African rangelands.

Biomass [DM] is assumed to equal NPP/carbon content on monthly basis, where carbon content of vegetation is on average 0.45. Additional correction will be applied for the shoot:root ratio and biomass turnover.

Calibration of the LUE model to estimate herbaceous rangeland biomass will be performed using data collected at test sites distributed in African rangelands and additional available existing data. Candidate variables and their combinations will be evaluated at the test sites in order to find the most suitable and robust combination for continental upscaling of herbaceous rangeland biomass.

Herbaceous rangeland biomass is one of three core products (the other are Rangeland Extent and Rangeland Type) of the RAMONA projects. The core products will be produced at 10 m resolution for all rangelands in Africa. RAMONA also include three experimental products (Biomass Anomalies, Phenology and Carrying Capacity and three intermediate products (Fractional Woody Cover, SAR Backscatter and Surface Reflectance).

Herbaceous Rangeland Biomass Anomalies will be generated from standardized z-scores of fAPAR derived from medium-term Copernicus Sentinel-3/Proba-V at 300 m resolution as outlined by earlier studies. These data will supply a minimum of a 7-year baseline which is expected to be appropriate in the highly variable and rapidly changing drylands of Africa, at least for on-the-ground users more interested in comparing present conditions to the recent past than to conditions over past decades. If 7-year baselines turn out unsatisfactory it is possible to fall back on MODIS NPP data (MOD17A3hgfv061, 500 m resolution, going back 20 years). Harmonization of data to ensure consistency with the biomass originating from the fused S1/S2/S3 data will be performed in the pilot areas. The data will be seasonally and annually integrated and z-scores for each season and year computed per rangeland pixel. Harmonization between the Proba-V and Sentinel-3 time-series may be required to ensure consistency. The Herbaceous Biomass Anomalies is a experimental product.

Fire behaviour is well described by a fire’s direction, rate of spread, and its energy release rate. Fire intensity as defined by Byram (1959) is the most commonly used term describing fire behaviour in the wildfire community.
In frequently burning savannas and rangelands, fire intensity exerts a major influence on tree cover, tree growth, biodiversity, greenhouse gas emissions and the carbon cycle in general. Recent research indicates that fire intensity may play a major role in schemes to reduce emissions from savanna burning. However, fire intensity is difficult to observe from space or over large areas.
Here, we determine Byram's fire intensity for the first time from space by assessing fire rate of spread and fire radiative power using infrared sensors with different spatial, spectral and temporal resolutions. The sensors used offer either high spatial resolution (Sentinel-2) for fire detection, but a low temporal resolution, moderate spatial resolution and daily observations (Sentinel-3 and VIIRS), and high temporal resolution with low spatial resolution and fire radiative power retrievals (Meteosat SEVIRI). We extracted fire fronts from Sentinel-2 (using the short-wave infrared bands) and use the available fire products for Sentinel-3, S-NPP VIIRS and Meteosat SEVIRI. Rate of spread was analysed by measuring the displacement of fire fronts between the mid-morning Sentinel-2 overpasses and the early afternoon VIIRS overpasses. We retrieved FRP from 15-min Meteosat SEVIRI as well as Sentinel-3 and VIIRS observations and estimated total fire radiative energy release over the observed fire fronts. This was then converted to total fuel consumption, and, by making use of Sentinel- 2-derived burned area, to fuel consumption per unit area. Using rate of spread and fuel consumption per unit area, Byram’s fireline intensity could be derived. We tested this approach on fires in a frequently burning West African savanna landscape. Field experiments to determine fuel consumption, rate of spread and fire intensity were also carried out in the area and compared to models. Comparison to our own experiments and other studies in the region show similar numbers between field observations and remote-sensing-derived estimates. We also demonstrate differences between early and late season fires and discuss the implications that production of a larger satellite-derived dataset on fire intensity could have for mitigation projects striving to reduce emissions from savanna fires through changing current fire regimes, e.g. towards late season fires. Shortcomings of the presented approach and foundations of an error budget, and potential further development, also considering upcoming sensor systems, are discussed.

Livestock herders in Mali and Burkina Faso live under the twin threat of drought and armed conflict. Moving their herds to find pasture and water depends critically on access to reliable information on rangelands. This talk discusses a call center that uses Copernicus Earth Observation imagery and field data to provide herders with information on pasture, water and markets but with a focus on rangeland conditions. The talk will go over the architecture of the data treatment, demo the interface, talk about successes and failures and show how you can play with the data yourself.

Transhumance, or the seasonal movements of livestock herds to find pasture and water, is a centuries-old tradition in Mali and Burkina Faso. The process of selecting routes for movement hinges on a complex network of factors including customary access rights, pasture growth, rainfall, surface water, among others. However, years of climate change and armed conflict have made herding more precarious and prone to rapid changes. As a result, access to data on environmental and market conditions is critical for pastoralists. While satellite imagery has made much of this information readily accessible to the spatial community, few channels exist to transmit this information to herding communities. As a result, rangeland monitoring has become more powerful than ever before, yet mostly inaccessible to the pastoralists who depend on this information for their livelihoods.

In 2015, the GARBAL call center was built to provide this data to herders in Mali and Burkina Faso. The call center is powered by an open platform GIS built from Copernicus remote sensing data on vegetation and water and field data on market prices and animal conditions. Herders calling the center are connected to an agent who uses a dashboard to respond to their questions: Is pasture available near me? Is it crowded by other herds? Can I sell my goats for a good price? The call center’s goal is to provide herders with decision-making support in planning their routes.

The interface is built on mapserver and uses automated scripts to download and treat imagery from Sentinel 2 and Meteosat which then display information on pasture conditions and water availability. Field data is routed through a network of local data collectors who provide weekly updates on livestock conditions and market prices. In addition to an interactive map, the interface provides user-friendly textual outputs that summarize all the layers for any area of interest on the map, which allows call center agents to quickly provide data to callers.

This talk will share a number of the lessons learned from the STAMP project and provide a demonstration of the platform (which is openly accessible). Specific topics of discussion will include:
The architecture of the system- what worked and what didn’t
Maintaining regular field data collection in areas of ongoing active conflict
Building and translating GIS data for communities with low literacy
Examining the call records to see what data matters the most to users

You can use the platform at www.stamp-map.org. For more information, contact Alex Orenstein (alex@orensteingis.com / @oren_sa)

A link between (remotely sensed) spectral variation and plant biodiversity (mostly expressed as species counts) has been suggested in the form of the spectral variability hypothesis (SVH). According to the hypothesis, higher spectral variation in canopy reflectance is related to (1) variation in habitats or linked vegetation types or plant communities which each having their own specific optical community traits or (2) variation in the species themselves according to their specific optical traits. Because optical community-traits are a summarized signal of the optical species traits of plant individuals, it is mostly a matter of scale which of both drives variation across pixels. The spectral variability hypothesis was examined in several empirical remote sensing case studies over the last years. These studies often report some correlation between biodiversity (species counts) and spectral variation; however, the strength of the observed correlations varies strongly between studies. In contrast, studies attempting to understand the causal relationship between (plant) species counts and spectral variation are still scarce. In this study, we address this research gap by critically discussing the link between spectral variation and biodiversity metrics. We support our perspectives through simulations and experimental data. Our results show that in many situations the spectral variation caused by species or functional traits is subtle when compared to other factors including for example physiological status and seasonality. Moreover, the methodical approach to calculate spectral variation was found to have a notable effect on the discussed link. We find that the degree of contrast in reflectance (used in many earlier studies examining the spectral variation hypothesis) has little to do with the number but rather with the identity of the species or communities involved. Hence, we recommend that spectral variability should not be quantified based on contrast rather than on manifoldness. The latter can for example by obtained using unsupervised clustering. While we describe cases where a certain link between spectral variation and plant species diversity can be expected, we believe that as a scientific hypothesis (which suggests a general validity of this assumed relationship) the SVH is flawed and requires refinement. Hence, we call for more research identifying the drivers of spectral variation in vegetation canopies and their link to plant species diversity and biodiversity in general. Such research will enable understanding under which conditions spectral variation is a meaningful indicator for biodiversity and how it could be implemented as part of a corresponding monitoring network.

Climate change and human activities have triggered an accelerated loss of biodiversity on a global scale. The Convention on Biological Diversity (CBD) commits nations to stem this loss by the end of this decade. In this context, Remote Sensing (RS) emerges as a potential tool for providing continuous, multi-scale, and systematic information that helps to adequately implement the post-2020 Global Biodiversity Framework, and other conservation actions.
The capability of remote sensing to inform about biodiversity on terrestrial surfaces relies on the Spectral Variation Hypothesis (SVH), which states that the variability of the spectroradiometric signals captured by remote imagers (spectral diversity) correlates with the variation in species on the ground. In vegetated environments, this variability could be influenced by taxonomic diversity (e.g., species richness) or the variability of vegetation functional properties (functional diversity). Researchers have explored for which metrics, methods, and sensors the SVH works and thus can be used to estimate plant biodiversity reliably from spectral diversity. However, these studies are limited to a few metrics, methods, and sensor features, usually evaluated in a single or a few vegetation communities. Therefore, most of their results are not easily generalizable and lack the information necessary to understand the differences in the performance found within and between studies.
To overcome these issues, we developed a modeling framework that allowed us to evaluate the capability of different functional diversity metrics (FDMs) to link spectral and vegetation functional diversities and assess the role of various sources of uncertainty inherent to remote sensing. This work focuses on plant “functional diversity,” which represents the variability of plant functional traits. Using radiative transfer models, we simulated 81.000 vegetation communities separated into 1.000 regions or images. Then we evaluated the information content of several FDMs derived from optical reflectance factors (R) or estimates of vegetation parameters (via model inversion). Some of these parameters are Essential Biodiversity Variables (EBVs) designed to monitor biodiversity change. Then we evaluated the effect of the spatial resolution, the spectral characteristics of the sensor, and signal (random) noise on the relationships between vegetation and remote sensing-based FDMs. Simulations allowed us to control and know all the factors influencing these relationships; however, they cannot represent all the complexity of the real world. For this reason, we tested the plausibility of the simulations using observational field data acquired in the forest biodiversity plots of the FunDivEUROPE network and Sentinel-2 (multispectral, 10 m pixel size) and DESIS (hyperspectral, 30 m pixel size) imagery.
The simulations demonstrated that variability of both reflectance factors and vegetation parameter estimates were correlated with plant functional diversity under ideal conditions (noiseless, high spatial, and spectral resolutions). Nonetheless, not all the functional diversity metrics were suited for studying plant biodiversity from space, and not all the aspects of functional diversity can be remotely captured. Only Rao’s quadratic entropy (Q), functional dispersion, and richness provided consistent relationships between remote sensing and plant trait-based diversities within and between communities (r² ∈ [0.61, 0.92]). Functional evenness and divergence were only weakly correlated with plant functional diversity (r² ∈ [0.03, 0.24]). We also found that the characteristics of the remote sensing data affected relationships of metrics based on reflectance factors or vegetation parameter estimates differently; and that the degradation of the spatial resolution had substantial effects on both methods (r² < 0.40). However, the degradation of spectral information (from hyperspectral to multispectral) mainly affected the parameter retrieval approach. The dimensionality reduction applied for the computation of functional diversity metrics minimized the effects of the noise in plant and spectral variables. Still, the noise increased the uncertainty for retrieving vegetation parameters when the spatial resolution was high. The analysis of remote sensing imagery was in agreement with the simulations. Sentinel-2 imagery provided better estimates of plant diversity from the ground plots than DESIS, since its higher spatial resolution could capture the internal variability of these plots. DESIS spatial resolution equals ground plot size, and therefore, it could only characterize the spectral diversity of the plot surroundings. Still, DESIS provided significant relationships with taxonomic diversity metrics. However, when Sentinel-2 spatial resolution was resampled to DESIS’s one, no correlations were found with field data. This result suggests that DESIS’s higher spectral information (despite not covering the entire optical domain) still constitutes an advantage when spatial resolution and signal noise are suboptimal.
The SVH has been tested systematically for the first time by consistently evaluating several metrics based on different types of information over a wide range of synthetic vegetation communities and under 27 different configurations and noise levels of the remote sensor. These simulations are coherent with the results found with remote sensing imagery and field observations. Our work clarifies the strengths and limitations of optical RS to monitor plant functional diversity exploiting the SVH. Furthermore, it draws valuable guidelines for the design of remote sensing products of plant functional diversity and missions dedicated to monitoring biodiversity change.

The increasing need for continuous global information on the biodiversity of Earth's forests calls for new approaches using satellite data. Advancing our scientific understanding of biodiversity states and changes on regional to global scales will contribute to assessing the impact of environmental change on ecosystems and facilitate the prediction of future ecosystem functioning. As an important dimension of biodiversity, functional diversity of plant traits links ecosystem functioning and biodiversity. Approaches to mapping functional diversity from space using satellite data build the base for large-scale analyses, opportunities to study vegetation throughout the phenological cycle, include time-series of multiple years, and access remote areas on large extents.

We investigate how functional diversity maps, derived from satellite imagery, link to in-situ measurements of biodiversity and ecosystem functioning, such as resilience and stability. We present an approach to upscale, map, and quantify functional diversity from physiological forest traits derived from Sentinel-2 data in a temperate forest ecosystem. We used two complementary data sources, APEX airborne imaging spectroscopy data, and Sentinel-2 satellite data. The high-resolution 2 m APEX data were spatially resampled to ensure our methods support spatially consistent mapping of functional diversity from space.

We derive functional diversity metrics from physiological forest traits, chlorophyll content, carotenoid/chlorophyll ratio, and water content in the forests of Canton Aargau, Switzerland, encompassing around 500 km2 of forests, using Sentinel-2 multispectral data. Based on the physiological forest traits related to forest health, stress, and potential productivity, we derive a cantonal map of forest functional diversity working towards comparing forests across landscapes and providing the base for analysis of changes over time. Comparing biodiversity monitoring data from in-situ measurements to functional diversity maps from Sentinel-2 data will provide answers to the question of how in-situ biodiversity measurements link to functional diversity captured from space. We use data from the Swiss biodiversity monitoring program, a long-term governmental program initiated in 2001 that monitors species richness of multiple taxa and at different spatial scales. These data allow studying relationships between plant functional and taxonomic diversity and how plant functional diversity links to the biodiversity of other taxa.

Diversity is coupled to ecosystem functioning. Linking Sentinel-2 derived functional diversity to forest resilience and stability provides information on the relationship between functional diversity and ecosystem functions. Understanding the relationships of functional diversity and ecosystem functions at the landscape scale is critical to monitoring and understanding forest ecosystems. We used drought resilience data derived from relative temporal changes in canopy water content to gain new information on the links of functional diversity from satellite data and ecosystem stability.

Our results will provide important information about the potential to monitor biodiversity and ecosystem function from space and open up new possibilities for large-scale interpretation. This paves the way towards a continental or global assessment of the functional diversity of forests using satellite data.

Given the accelerating global change, the protection of plant biodiversity is of major importance, which inevitably requires understanding its global distribution its relation to the earth system as a whole. Such plant biodiversity-environment relationships can be understood in terms of functional rather than taxonomic diversity. Still, knowledge on the global distribution of functional diversity and how it is shaped by individual functional traits remains sparse. In a joint effort, the scientific community is constantly gathering plant trait observations and making it available through the TRY database, which provides more than 11.8 million records of 2,091 traits across 280,000 plant taxa (TRY v. 5). Several studies attempted to spatially extrapolate these sparse trait observations using globally available predictors, including data on climate, soil properties, or landcover. However, so far there are only global maps for a few traits with hardly measurable and probably high uncertainties. Also, Earth observation satellite data is becoming a key technology for generating global products on plant functional traits. However, the bird-eye view from Earth observation primarily informs on upper canopies and, as a result, may only represent functional traits of the most competitive plants and not the actual plant community.

Here, we take a completely different perspective: an ever-growing plethora of geocoded plant photographs with species information crowdsourced by already more than a million citizens around the globe, i.e., the citizen science project iNaturalist. With the human eye, we immediately see that such plant photos, even if they are very heterogeneous, can provide information about morphological plant characteristics - and thus also about functional traits. To effectively harness this data treasure, we make use of deep learning and Convolutional Neural networks (CNN). Training CNN to accurately and robustly capture plant functional traits from plant photographs requires an excessive amount of training data for model training, consisting of pairs of plant photographs and plant traits. Such datasets are not readily available; however, while, iNaturalist data informs on plant species in the photographs, the above-mentioned TRY database contains enormous amounts of species-specific plant trait records. Therefore, we investigated how joining these two databases (iNaturalist, TRY) by the species affiliation enables a weakly supervised learning of CNN models for predicting functional plant traits from simple photographs. Our results show that morphological image features indeed suffice to predict several traits representing the main axes of plant functioning, including growth height, leaf area, leaf mass. Lower but still promising accuracies were obtained for traits that relate not directly to visible morphological features (nitrogen concentration and stem specific density). The accuracy was enhanced when using CNN ensembles (combinations of different CNN architectures), incorporating prior knowledge on trait plasticity and contextual information on climate (WorldClim). Our results suggest that these models generalised across growth forms, taxa, and biomes. We did not find any phylogenetic signal in the residuals (e.g., bias towards taxonomic groups), indicating that the models indeed learned to see plant traits in the photographs. The trait predictions were robust over heterogeneous crowdsourced photographs (image quality, and other image acquisition settings). Spatially aggregating such CNN-based trait predictions from 185,000 independent iNaturalist photographs via their geolocation enabled the generation global trait distribution maps, which reflect known macroecological patterns. A quantitative comparison with previous global trait distribution maps revealed significant correlations with Pearson’s r > .5 concerning growth height, seed mass, specific leaf area and stem specific density. Still, global trait distribution maps also commonly show large discrepancies, which we can also partly confirm with our products. Note, that most of the global trait distribution maps typically feature substantial uncertainty as trait expressions are commonly spatially extrapolated across large extents from sparse TRY records. In contrast, the extremely high and steadily increasing sampling density in the iNaturalist database enables the generation of global products without spatial extrapolation. All trait products derived this way are freely available, including the first-ever published map on the global distribution of leaf area.

The presented approach presents an alternative way to understand macroecological patterns. There exists a great potential of fusing crowd-sourced data with spaceborne remote sensing data or to use the respective products for comparison. Yet, biases in citizen science-based data still remains largely unknown and until now there does not exist a comprehensive reference dataset to evaluate global trait maps. Our results show that integrating crowd-sourced data may enable to further close the gap between actual and intended spatial coverage, which ecology has been falling short of for decades. Moreover, these findings demonstrate the potential of exploiting big data, with its volumes, sparsity, and variety, derived from professional and citizen science in concert with deep learning for assessing Earth’s plant functional diversity.

Grasslands are highly important ecosystems for biodiversity, carbon sequestration and soil protection. They cover significant parts of the European Union and are one of the largest biomes on Earth. Unfortunately, monitoring the changing status of grasslands, even in protected sites, is challenging due to limited conventional data collection resulting in inefficient management. The European Commission has developed a pilot operational monitoring service, known as EU Grassland Watch, for the grassland dominated Natura 2000 sites which exploits Earth observation data.
The key enabler for this service is the Copernicus programme and the Sentinel satellites which represent a step change in observing capability and capacity that can be directed to environmental monitoring. The European land area can now be monitored at a scale and frequency where habitat patch extents and changes can be identified and phenological and episodic events related to landscape processes can be followed. The Copernicus Land Monitoring Service (CLMS) also provides a range of support products including some directly related to Natura 2000 sites and grasslands.
The first step is to map the annual status and changes of land cover / land use (LC/LU) in and around (the site areas plus a 2 km buffer) each selected Natura 2000 site. Optical images from Copernicus Sentinel-2 plus the US Landsat 8 are used to both allow gap filling caused by clouds and snow and to extend the time series back to 1994. Copernicus Sentinel-1 synthetic aperture RADAR (SAR) data are also used to support the mapping and monitoring processes from 2014 onwards.
The EO data for the classification process are combined over short time periods as separate optical and SAR monthly (3-monthly and optical only prior to 2014) composites. The optical and SAR composites are classified separately using a Support Vector Machine (SVM) approach based on training data selected from the existing CLMS Natura 2000 and CORINE land cover products to give 17 classes over a four-level hierarchy. The two separate optical and SAR results are then merged by a simple rule based on the highest SVM probability to give a single LC/LU map for each year. The raster-based LC/LU maps are then aggregated to the level of the reference objects from the CLMS Natura 2000 product, with the output vector layer including the fractions of each class per object plus biophysical properties calculated from the optical data. In addition annual summaries of phenology, productivity, mowing and ploughing are provided along with indicators of the performance to the management within the sites (e.g. trends in total grassland area).
The EO-based products add up to a considerable volume of data which needs to be easily accessible to a broad range of users and stakeholders. A geospatial database has therefore been developed to store all the above products and indicators and provide easy access to the information via an interactive public webtool, the user-facing part of the EU Grassland Watch service. The webtool displays the results at a range of reporting levels to support stakeholders at local to European scales and will be updated at regular intervals once fully operational and visualises the results in ways most appropriate to the application, e.g., heat maps, trend indicators, temporal plots or thematic maps.
The EU Grassland Watch service will support EU Member States to take appropriate steps to proactively manage this important network of protected sites and prevent the deterioration of species and habitats. This service is a clear demonstration of how frequent spatially detailed EO data can be combined with exiting geospatial information to provide an effective tool for environmental management at local to continental scales. It is also a further example of a downstream service being built on the free and open data policy of Copernicus.

Grasslands cover about one third of the global land surface and are the most cultivated biome on Earth. They provide numerous ecosystem services, such as carbon sequestration and food production, and have a high importance for conservation as they are often species rich. Biodiversity, ecosystem services and functions in grasslands are strongly affected by the management regime, i.e. grazing, fertilisation, timing and frequency of mowing events, and its quantitative variation. The optimisation of farming efficiency towards yield maximisation usually leads to an increase in these treatments and thus land-use intensity (LUI), which may have numerous negative implications for the environment, such as biodiversity loss, water pollution, land degradation and increased carbon emissions. Information on grassland LUI is thus crucial for understanding trends and dynamics related to biodiversity, ecosystem functioning, earth system science and environmental monitoring.

However, large extent, high resolution information on grassland LUI is rare. New satellite generations, such as Copernicus Sentinel-2, enable a spatially comprehensive detection of the mainly subtle changes induced by differences in land-use intensity based on their comparably fine spatial and temporal resolution. We developed a methodology calculating a continuous LUI index by quantifying key parameters of grassland LUI such as grazing intensity, mowing frequency and fertiliser application across Germany using Convolutional Neural Networks (CNN) on Sentinel-2 satellite data with 20 m x 20 m spatial resolution. An unprecedented LUI validation and its components using large scale in situ grassland management data of the DFG (Deutsche Forschungsgemeinschaft - German Research Foundation) Biodiversity Exploratories program was performed. This dataset included spatially explicit information about livestock, fertilisation and mowing events in three regions across Germany from 2006 to 2018 which differ in soil as well as plant species composition and also land-use intensity.

In contrast to many past studies we employed 2 methodological aspects which allow an advanced result interpretation. First, a feature contribution analysis using Shapley values, summarising explanations of individual predictions to gain information about the global model structure and to robustly extract variable importances across different underlying models. Thus, the contributions of the predictor variables (spectral S2 bands) over time (one year) could robustly be quantified. Second, we assessed the model's spatial transferability by delineating the area of applicability (AOA) based on the feature space given by the training data and the model's variable importances. This provides valuable information especially for large scale predictions. Further, We evaluated the methodology's robustness with a spatial 3-fold cross-validation by training and predicting on geographically distinctly separated regions.

Shapley values substantiated our method's plausibility revealing a high relevance of springtime satellite observations and spectral bands related to vegetation health and structure. This was in line with the timing of management activities and overall vegetation dynamics as well as published management effects on grassland condition. Regarding LUI components, we achieved an overall classification accuracy (R2) of up to 66% for grazing intensity, 68% for mowing, and 85% for fertilisation. The subsequent overall LUI derivation was achieved with an accuracy of R2=0.82. The loss in predictive power when training and predicting on geographically distinctly separated regions varied between 3-20 %. AOA analysis showed differences between investigated vegetation periods potentially related to the increasing satellite data availability. The pixel number outside AOA differs substantially between regions revealing spatial patterns most likely reflecting fields or parcels. This points towards regional differences in land management practices, abiotic conditions or vegetation compositions not reflected by our training data.

The presented methodology enables a high resolution, large extent mapping of land-use intensity of grasslands.

The Fiducial Reference Measurements (FRM) programme, implemented through ESA, is designed to facilitate robust in situ reference observations for the calibration and validation of satellite products, following the principles defined in the Quality Assurance Framework for Earth Observation (QA4EO). The programme emphasises metrological traceability and uncertainty analysis.

FRM4VEG is an ESA-founded project that applies these metrological principles to three variables of interest for vegetation: surface reflectance (SR), the fraction of absorbed photosynthetically active radiation (fAPAR) and canopy chlorophyll content (CCC), and identifies three missions of interest for validation: Sentinel-2, Sentinel-3, and Proba-V. Phase 1 of the project successfully developed and applied FRM methods to the application of field spectrometers for SR and indirect optical techniques including digital hemispherical photography (DHP) and chlorophyll meters for fAPAR and CCC.

Phase 2 of the programme (FRM4VEG2) advances the developed concepts by including new instruments, measurement platforms, and more detailed uncertainty characterisation. A key difference between the two phases is the collection of in situ SR data from a hyperspectral imager mounted on an unoccupied aerial vehicle (UAV), for which validation protocols and procedures are being developed. This is being achieved through two activities. One took place this summer, involving collection of validation data over Wytham Woods (UK), something which was not possible in phase 1.

The other, which will take place in 2022, is a Committee for Earth Observation Satellites (CEOS) endorsed intercomparison exercise of UAV-mounted hyperspectral imagers over an agricultural site in Barrax (Spain). Known as the Surface Reflectance Intercomparison Exercise for Vegetation (SRIX4VEG), the exercise will bring together teams from around the world with similar instruments to assess the need for a SR validation protocol from UAV-mounted instruments. SRIX4VEG will consider two scenarios: 1) where a pre-existing protocol is used as a basis for measurements, and 2) where each contributor is asked to collect the validation data according to their own judgement.

The Fiducial Reference Measurements (FRM) concept for validation of satellite-based products is a keystone of the ESA calibration/validation strategy. With a strong focus on metrology, it includes the need for traceability and fully characterized uncertainty budgets according to community-agreed-upon measurement protocols and uncertainty evaluation procedures. Within this strategy, the ESA funded Fiducial Reference Measurements for Vegetation (FRM4VEG) project is aiming to address the challenge of applying metrological principles to vegetation and surface reflectance product validation.

For vegetation product validation, an end-to-end uncertainty evaluation framework has been developed for two main variables: the fraction of absorbed photosynthetically active radiation (fAPAR) and canopy chlorophyll content (CCC), which makes use of leaf area index (LAI) in its calculation. The process involves quantifying the uncertainties associated with individual in situ reference measurements from different instruments (Digital Hemipherical Photographs, LAI-2200, AccuPAR, SPAD chlorophyll meter), and incorporating these uncertainties (and those of the high-resolution satellite image) within the upscaling procedure, which facilitates the validation (uncertainty estimation) and conformity testing of moderate resolution satellite products. fAPAR and CCC FRM covering several irrigated and non-irrigated crops (Barrax, Spain) and deciduous broadleaf forest (Wytham Woods, UK) were collected in 2018 and 2021 field campaigns.

This talk will present to the validation community, firstly, the latest achievements of the FRM4VEG project for vegetation variables, from ground measurements to up-scaled maps with per-pixel uncertainties estimates, with particular attention to the uncertainties contributed by different instruments and data processing methods. Secondly, we will use the FRM data to validate the Sentinel-3 OTCI product and two Sentinel-3 OLCI FAPAR products, the OGVI (or Instantaneous green FAPAR) provided by ESA, in addition to the Sentinel-3 FAPAR provided by Copernicus Global Land Service. Finally, conformity testing against user/mission requirements (using uncertainties in both satellite and in situ data) will be presented, and future validation needs addressed.

The HYPERNETS project is preparing the next generation of hyperspectral radiometer with associated pointing and acquisition system and network processing for validation of water and land surface reflectance derived from satellite missions.

Spaceborne optical imaging missions such as Sentinel-2 and Sentinel-3 are used routinely to provide data for environmental monitoring of water and land surfaces via products such as chlorophyll and suspended particulate matter concentrations in water and Fraction of photosynthetically active radiation absorbed by vegetation (FAPAR) and Land cover over land. Validation of the water and land surface reflectance products is needed to ensure reliability of all such satellite data products.

For water reflectance, the AERONET-OC network is currently the main source of radiometric validation data. This network is mature and operational but only multispectral. A new radiometer, the HYPSTAR®, has been designed and is being tested to provide finer spectral resolution (3nm FWHM for the range 380-1020nm) at lower cost. The new radiometer is mounted on a commercially-available pan-tilt unit and is controlled by a rugged PC with purpose-built electronics and software. Data is transmitted in near-real time for standardised processing and distribution.

For land surface reflectance measurements, there are few automated systems. The new instrumentation that has been designed and is being tested within the H2020/HYPERNETS project will provide land surface reflectance measurements at high spectral resolution including spectral coverage extended to 1700nm (with 10nm FWHM resolution) and with angular/spatial variability.

The progress of the H2020/HYPERNETS project will be summarised briefly here. In situ data from the validation sites (7 in Nov 2021, 14 expected by May 2022) will be presented. The in situ data will be compared with water and land surface reflectance data from various satellites, including Copernicus missions Sentinel-2 and Sentinel-3 but also hyperspectral satellite data (e.g. PRISMA) and metre-scale satellite data (Planetscope Doves).

A growing number of High Resolution (HR) and Very High Resolution (VHR) optical satellites have been launched in the last years, and more will be launched in the next years. Together with the benefits that this increased availability of Earth Observation (EO) data can bring, there is a number of other aspects to be considered, like the increased need of harmonized calibration and validation (cal/val) methodologies and of common cal/val sites also dedicated to VHR missions. Several cal/val networks are in place nowadays (e.g. RadCalNet, AeroNet, Hypernets, cal/val sites set-up by national space agencies or institutions, etc.), but each site and network is usually dedicated to a specific task and need, with little flexibility.
Therefore, the European Space Agency (ESA) is investigating the idea of setting-up a common playground, i.e. the so-called Cal/Val Park, where to explore current and new cal/val methodologies for HR and VHR optical satellite sensors, for both testing and operational purposes. The Cal/Val Park is meant to be flexible enough to be used for both multi-spectral and hyperspectral missions, scalable enough to accommodate new needs and new EO missions that may be launched in the next years, and open to be used by both the “institutional space” and the “commercial and New Space” players. The Cal/Val Park concept is currently in the definition phase and is meant to be a multi-Agency effort.

Copernicus is the European Union Earth Observation program, dedicated to monitor our planet and its environment, giving free access to remote sensing data and derived Earth Observation products. For proper use in environmental monitoring and scientific applications, it is fundamental to guarantee high quality and consistency of these satellite derived products. One of the possibilities to ensure product quality is to perform quantitative comparisons of satellite derived products with matching in situ observation. Two options can then be considered for ground data sources: through intensive field campaigns or making use of permanent ground stations deployed and maintained on the long term. In the first case, a large variety of variable can be assessed, but logistical challenges and financial resources limit in time and space the products validation. More over meteorological constrains often limit the number of data that can actually be used for Earth Observation products. The second option is from far the most cost effective although it is not yet possible to cover all ground variables with permanent field deployment.
To achieve these objectives of systematic and long-term data validation, the Ground-Based Observations for Validation (GBOV) service has been implemented, facilitating the use of observations from operational ground-based monitoring networks and their comparison to EO products. The service is guaranteed through 3 different components:
• Collection of multi-year ground-based observations (Reference Measurements - RMs) of high relevance for the understanding of land surface processes from more than 80 sites. These RMs are then upscaled to generate Land Products (LPs), in order to validate the Copernicus products. In particular, the LPs distributed through the GBOV portal are: Top of Canopy Reflectance (ToC-R), surface albedo, Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Available Radiation (FAPAR), Fraction of Covered ground (FCover), Surface Soil Moisture (SSM) and Land Surface Temperature (LST).
• Upgrade of existing sites with new instrumentation or establishing entirely new monitoring sites to close thematic or geographical gaps.
• Implementation and maintenance of a database for the distribution of the Reference Measurements and the corresponding Land Products, available through the website https://land.copernicus.eu/global/gbov. GBOV data access is completely free, after registration and acceptation of the terms of use and the data policy.
Initially designed to serve the Global Land Service, GBOV products are used by a worldwide community The purpose of this presentation is to provide a status of four year service achievement and the perspectives.

In recent decades, numerous scientific field-instrument networks have been developed in different contexts and for several purposes. SpecNet, Aeronet, FluxNet, or similar are just a few examples. One promising area of research involves the use of automatic field spectrometers, operating in the visible and near-infrared spectral domain, for the purpose of long-term and unattended monitoring of their targets. The applications of field spectroscopy are manifold: from vegetation studies (plant phenology, plant physiology, and phenotyping) to the study of water quality, the atmosphere, and the optical properties of snow/glaciers.
In recent years, JB Hyperspectral Devices, with the support of the scientific community, has developed autonomous instruments, capable of operating continuously in the field in a wide range of harsh climatic conditions (from the equator to the poles) along with dedicated open-source software to process and manage the collected data.
These instruments acquire punctual, high-temporal-resolution hyperspectral data. Thanks to a robust and standardized data processing chain (based on R: A language and environment for statistical computing), they return time series of radiometric parameters as well as optical properties related to the monitored targets. JB devices are made to synchronously acquire upwelling and downwelling radiance, optimizing the acquisition speed according to light conditions and acquiring the dark current signal at each measurement cycle. For example, the FloX (Fluorescence BoX), features a high-performing spectrometer (FWHM: 0.3 nm, SSI 0.15, SNR 1000) and allows continuous measurement of solar-induced chlorophyll fluorescence (SIF) emission. Furthermore, continuous measurements of spectral down-welling and up-welling radiance, using an additional spectrometer in the VIS-NIR range, allows for the computation of reflectance and different spectral indices. Another example of JB’s instruments is the RoX (Reflectance BoX), which is a simplified version of the FloX. It contains only the VIS-NIR spectrometer and therefore it is designed to measure reflectance and spectral indices.
To further utilize the data collected with these devices, JB is currently in the process of making these instruments compatible with the latest flux data collection and processing systems operated in typical flux stations (e.g. SmartFlux from LI-COR). This approach is aimed to help with accurate synchronization and improved compatibility of various types of data collected by different devices at flux stations. Moreover, a collaboration with existing consolidated flux networks (e.g. FluxNet) is ongoing to explore better coupling between flux, optical, and remote sensing measurements. The possibility to integrate JB devices into flux networks, via a standardized procedure of configuration, set up, data processing, and different data product levels are further evaluated.
In the view of satellite cal/val activities, the need of multiple instruments distributed around the globe is furthermore introduced. A specific study, conducted on a subset of nine RoX and FloX field spectrometer systems, deployed across the northern hemisphere in Europe, US, Africa and China was conducted, with the purpose being to validate ESA’s Seninel-2 time-series. The instruments were installed above agricultural fields, broadleaf forest, savannah and alpine pasture for timeframes ranging from several months to up to three years, continuously measuring hyperspectral reflectance data in the visual/NIR range between 350nm and 900nm. Spatially temporal clustered time-series were validated using the ground-truth for clear-sky conditions. Our results suggest a good agreement with R² larger than 0.5 between field spectrometers and satellite bands. Vegetation indices agree very well with R² larger than 0.7, e.g. NDVI with R² =0.89 between Sentinel and field spectrometer subsets across all sites. Our results suggest that RoX and FloX monitoring field spectrometers are a valid ground truth for known vegetation targets. In terms of reflectance bands, they are in good agreement with the Sentinel-2 satellite time-series and in terms of Vegetation Indices, they are in very good agreement.
In this contribution, we present an overview of the state-of-the-art JB hyperspectral devices with applications ranging from SIF, water, atmosphere and snow studies. Finally, point field spectrometers are put forth to promote the potential for a future ground-based network of devices with multiple purposes: from investigating ecosystem properties to the validation of satellite products. In this way, field spectrometers can contribute to guiding the application of ground-based remote sensing practices towards an improved insight into vegetation, water and snow across scales, as well as bridging information between ground and satellite.

The European Space Technology Master Plan
The European Space Technology Master Plan (ESTMP) is a unique one-stop-shop of information on space technology in Europe, covering ESA, national and other European institutionally funded programmes and activities. For over two decades, ESA has been conducting a Harmonisation of European space technology and preparing the annually published ESTMP together with key stakeholders, including ESA Member States, ESA Associate Members and European Cooperating States. Since 2017, the ESTMP has been jointly published with the European Commission.
Based on the content of the ESTMP, the presentation will provide a brief overview of the European technology landscape, including ESA and national programmes and activities. Some excerpts of relevance to Earth Observation developments in Europe will also be presented.
The presentation will complement other presentations within the session by providing:
- the overall context for European space technology
- the European landscape of institutional programmes funding space technology
- A high-level overview of ESA technology programmes
- A summary of national programmes included in the ESTMP
- The role of the Technology Harmonisation process
- Some excerpts of particular relevance for Earth Observation

Societal challenges like climate change and adaptation, digitalisation, mobility, renewable energy, sustainability and civil security are the main topics for the German Earth Observation Programme. As the largest contributor to the European Earth Observation Programmes at ESA, Eumetsat and EC, Germany is shaping together with our international partners a user-driven, well-balanced, technological outstanding and long-term EO landscape for Earth science, operational services and downstream business. The German National EO Programme complements the European activities in terms of data exploitation, service development, technology preparation and mission development and operations. In order to prepare new missions, this programme is continuously funding the development of crucial EO instrument technologies. Examples from predevelopments for German radar, optical and Lidar missions will be presented.

CNES, Centre National d'Etudes Spatiales, is the French space agency, responsible for shaping and implementing France’s space Policy at National, European and International levels.
Among its five key domains (launchers, science, Earth observation, telecommunications and defence), Earth Observation programme is a fundamental pillar which covers a full scope of activities: from science and technology needs and priorities to the development of applications and services, with a strong activities in infrastructure and data management
Earth observation is becoming increasingly precise and involves major challenges for the future: advancing our understanding of the functioning of the Earth system, in particular the water, energy and carbon cycles, understanding and assessing climate change and its effects, and the impact of humans on the Environment. This research and the development of new space missions contribute to satisfying major societal needs for up-to-date and qualified environmental information. CNES is working to develop and to renew the space infrastructure needed for continuous innovation to address those needs and on the other hand support new actors to develop the market with Connect By CNES initiative.
In this session, we will present the Earth Observation program in its various aspects, "science and climate" drivers, current satellite missions and associated activities, international partnerships and initiatives, downstream applications development especially within the New Space context, as well as perspectives for the future.
We will put a strong emphasis on innovative technological developments and achievements in key areas, some already operational in current missions, others being planned and/or developed in the frame of future innovative EO missions.

The Italian Space Agency is preparing the future of Earth Observation through a range of activities and programs, which work together for efficient results covering short- to medium-terms activities as well as others which will not be completed until farther into the future. These programs are all defined in strong connection with industry, research partners and user gathering inputs and needs through national workshops, regular bilateral meetings and open call for ideas.
The main drivers are enable new measurements and on board functionalities, but also facilitating on ground information retrieval and sharing,
In this paper we will focus mainly on earth observation instruments to provide some examples of new developments under the “new earth observation payloads and missions program”. The program, running since 2015, covers a wide range of instruments (optical, microwave, quantum) addressing both large complex instruments and small for constellations of small satellites.
Regarding microwaves, one line of activity concerns the development of a low frequency wideband radiometer in the range 0,4-2,0 Ghz for remote sensing of the cryosphere. The innovation is related to extend the frequency spectrum for radiometry below L-band and fully relies to the capability to detect and filter RFI on board, which is also a new approach. An instrument prototype for airborne campaign is under development. Still considering the microwave part of the spectrum, ASI is working on deploying the first X-band SAR in a GEO orbit that would make a highly innovative contribution to EO capabilities in meteorology, geophysical processes, infrastructure monitoring thanks to the temporal frequency of the measurements. Another observation concept is based on a distributed low frequency (40-50 MHz) radar sounder flying in formation with small recurrent platform providing subsurface measurements of ice masses and arid areas. Low frequency sounding radars have flown in space around planetary bodies but never around Earth. The challenges are mainly related to autonomous control of large constellation flying in close formation and to a large syntethic antenna in a distributed architecture.
Activities are also ongoing on multi-frequency transmitters and receivers operating in the Ka band for the observation of tropospheric water vapour, based on an active differential measurement technique (NDSA: Normalized Differential Spectral Attenuation) and on a train of co-rotating LEO satellites. An experimental campaign is on going on ground.
Coming to optical range of the spectrum, preliminary studies are on going on a payload operating in the medium infrared, based on super-resolution and compressive sensing technique with the aim to improve ground spatial resolution and mitigate saturation/blooming effects. The core of the payload is a Spatial Light Modulator (SLM): a bidimensional array of micro-mirrors electronically actuated. Thanks to compressive sensing approach, the proposed payload eliminates the compression board, saving mass, memory and energy consumption. Compressive sensing approach is applied also in the ASI project dedicated to evaluate the spatial distribution and the effect of impurities of the snow apparent thermal inertia (API) as indicator of snowmelt processes on fragmented landscapes. To this end the project foreseen the study of a VIS-TIR payload based on multiwave and qDIP detector.
LIDAR are also on the payload roadmap of the Italian Space Agency, on the basis of the heritage acquired with Aeolus and Earthcare mission. Technological developments are on going on high-power lasers including diodes, efficiency and optical coating.
To look forward in the future, a new type of sensor based on quantum technology, is expected to improve significantly the gravity measurements, leading to better Earth monitoring and enhanced or new EO services. The quantum payalod currently under study at ASI is based on the integration of two different technologies: an "enhanced" CAI instrument consisting of a gravitational interferometry gradiometer with ultra-cold atoms, on which an optical frequency measurement using an ultra-stable laser is implemented, which also provides time measurements. This would lead to an improvement of the gravity model even at low harmonic degrees and its temporal variations, with advantages in the modeling of mass transport and its global variations.

The first ever United Kingdom (UK) National Space Strategy was published in 2021 and sets out the government’s ambitions for the UK in space, bringing together civil and defence space policy for the first time. Earth Observation (EO) applications and services are identified as a high growth area, highlighting EO data as a priority to tackle climate change challenges.

UK Space Agency has a strong heritage of collaboratively supporting programmes across science, technology and industry. The UK Government supports a number of technology programmes in EO development, including but not limited to the UK Space Agency funded Centre for Earth Observation Instrumentation (CEOI), a consortium led by Airbus Defence and Space in partnership with QinetiQ, University of Leicester and the Rutherford Appleton Laboratory (part of the Science and Technology Facilities Council).

Since 2007, the CEOI has sought to maintain and grow UK capability in EO instrumentation. The CEOI oversees a grant programme which, with parallel investment from industry, funds novel EO instrumentation projects to raise Technology Readiness Levels (TRL) to a level that can contribute to major EO missions. Since 2007, CEOI funding has assisted UK EO technologies to be part of at least 17 mission opportunities across the European Space Agency (ESA), Copernicus, the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), the National Aeronautics and Space Administration (NASA), and commercial programmes.

Some highlights include:
• Two funded technologies are the basis of ESA Scout missions CubeMap and HydroGNSS
• An on-board calibration system (the Cryogenic Solar Absolute Radiometer - CSAR) that will fly on the TRUTHS high precision climate mission, now in advanced study in ESA
• The mission concepts SEASTAR (oceanography) and WIVERN (wind and rain) are two of the four Earth Explorer 11 candidates selected for Phase 0 study
• Three commercial instruments for maritime surveillance, high-resolution imaging and video, and thermal imaging, are either flying or are at an advanced build stage

In addition to funding instrumentation development, the CEOI runs a range of community building activities to promote close working relationships and knowledge transfer between academia and industry. This includes holding events such as an annual national EO conference; providing advice and guidance to projects teams on technology development and commercialisation; and supporting UK companies to make the best case possible when pursuing a role in EO missions.

The UK Space Agency has also recently established the National Space Innovation Programme (NSIP) to support the development of innovative space projects. It supports projects that may be high risk but have the potential for high returns and a clear target market. Launched in 2020, the programme has so far run two funding opportunities for project teams to advance products, services and technologies that have application in the space sector. In its first year, 27 projects received grants, which were awarded to both industry and academia led project teams across the UK. Several of these projects are focused on developing EO technology, including the development of a novel High Resolution Infrared Sensor payload for heat detection (Global Satellite Vu).

NSIP EO projects include:
• TreeView: Precision Forestry to Tackle Climate Change (The Open University)
• High resolution thermal infrared space telescopes for globally monitoring the energy efficiency of buildings (University of Cambridge)
• Global Lidar Altimetry MISsion: GLAMIS (University of Edinburgh)
• Hyperspectral Microwave Sounder Constellation of Nanosatellites for Climate change And Mitigation (STFC RAL Space)
• GHGWatch (Geospatial Insight)

Rutherford Appleton Laboratory (RAL) Space scientists contribute to and underpin UK EO and international programmes in environmental science through the provision of research expertise, services and facilities in support of the UK science community. RAL Space develop enabling technologies of the future and are delivering missions such as ESA Earth Explorer candidate mission LOCUS Supra-THz mission of Upper Atmosphere, BAROmetric Differential Absorption Radar (BARODAR) mission for surface Air-Pressure measurement from space, and Hyperspectral Microwave Sounders (HYMS).

RAL Enabling Technologies include:
• Schottky diode mixer development a critical technology for heterodyne microwave radiometers, from microwaves up to supra-THz frequencies
• Development of a new generation of closed cycle, miniature space coolers for detectors and sources. These coolers can lower the operating temperatures of heterodyne mixers in EO instruments, which reduces the receiver noise contribution and, in doing so, increases the instrument sensitivity
• Novel techniques to integrate microwave radiometers for EO application. This new technology opens the way for microwave remote sensing from New Space platforms (CubeSats and SmallSats)
• 200 GHz cloud radar (GRaCE) is a ground-based millimetre wave radar built as a technology demonstrator and to establish the scientific benefits of a future space radar
• Mid InfraRed hollow waveguide integration technological developments aim to enable new science in the field of atmospheric studies, EO and astronomy, through new instrumental capabilities, both ground-based and space-based (e.g., in the ESA Scout mission CubeMAP)
• Cold Atoms and Quantum Sensors research and development of the next generation sensors based on the quantum properties of ultra-cold atomic gases

This highlights some of the UK’s EO technology development programmes ensuring the long-term, resilient supply of data and skills needed for economic growth, climate change and science discovery.

Within the US National Aeronautics and Space Administration (NASA), the Earth Science Technology Office (ESTO) is leading the effort in developing new, CubeSat- and SmallSat-class instruments, instrument technologies and information systems technologies for Earth observations. ESTO has been on forefront of the technology development and validation, through Advanced Component Technology (ACT), Instrument Incubator Program (IIP), Advanced Information Systems Technology (AIST), In-Space Validation of Earth Science Technology program (InVEST), and the Sustainable Land Imaging Technology program (SLI-T). With these programs ESTO has been able to cover the full technology development cycle, from concept to airborne and space flight demonstration and ultimately infusions into missions. Over time, ESTO investment in emerging technologies have enabled new mission architectures, rapid development, and lower costs leading to unique observation capabilities for new science investigations. For example two space demonstrations of a radar and a radiometer observing the structure of hurricanes near simultaneously enables future missions with simultaneous active, passive observations such as with the EVM-3 INCUS mission.

This talk will cover some of these efforts through the full cycle from technology development to missions.

The Black Sea and Danube region is constituted of three parts – the sea basin, coastal areas and catchment areas. They must be understood as a whole for an accurate understanding of the region's dynamics in a systematic and holistic manner. The Danube catchment, by far the biggest tributary of Black Sea, constitutes the second largest river basin in continental Europe with its area of more than 800.000 km2. Shared by 19 countries (nine of which are EU Member States), more than 81 million people of different cultures and languages live here, making it also the world’s most international river basin. Although the countries are very different in terms of economic strength, the region is strongly interlinked, with potential for further integration and growth. Thus, the region is connected through both opportunity and challenge. The policies of the countries are interdependent, however, they all benefit greatly from improved cooperation in, for example, completing missing transport links, reducing pollution and danger from floods, sustainable management of the environment, lowering dependency on energy providers from outside the region, and addressing demographic change. This presentation is about two parallel activities in ESA's Black Sea and Danube Initiative - Applications Line:
1) The Danube Environmental Risk Assessment Platform (DEAP) provides a suite of applications based on Earth Observation data to support environmental risk management within the Danube catchment. The purpose of the platform is to provide regional stakeholders, who currently do not regularly use EO data, with access to dynamic environmental assessments using such datasets. The service comprise a suite of cloud-based applications (deployed on CREODIAS DIAS) which are able to detect, monitor, analyse and characterise the sources of environmental problems using available EO data in conjunction with in-situ inputs, numerical modeling outputs and other reference data to deliver meaningful (actionable) maps, statistics and other data across 19 countries. At an operational level, the service identified industrial waste discharge, transport waste discharge, agricultural run-off, and ecosystem degradation in near real time, and represents a unique tool for regional agencies. Stakeholders include environmental protection agencies, port authorities, fisheries management agencies and development agencies, etc.
2) Earth Observation services for Black Sea Protection (EO4BSP) overlap the entire area of the Black Sea and propose a holistic approach that covers different elements with potential environmental impact. The project is implementing six services that are being delivered to a number of 13 stakeholders from the Black Sea riparian countries and one International organization - The Black Sea Commission. The services comprise a suite of cloud-based applications which will detect, monitor, analyze and characterize the sources of environmental problems using available EO imagery in conjunction with in-situ inputs and other reference data. Service applications are developed for deployment in the cloud and shall employ advanced dispersion modeling techniques in conjunction with EO Data to deliver meaningful (actionable) maps, statistics, and other data. The services are: S1 - Land Use Land Cover coastal changes, where analysis and modelling of land-use change trends and urbanization allow us to evaluate the spatial development patterns providing a key for effective planning practices in the context of Marine Strategy Framework Directive (MSFD) and Maritime Spatial Planning (MSP) implementation; S2 - Eutrophication, which represents one of the most severe and widespread environmental problems for coastal zone managers; S3 - Marine front identification and mesoscale circulation, providing data fusion, satellite observations, numerical modelling and data assimilation, as well as skill assessment and metrics with a focus on sea state, temperature, turbidity, and SPM, identification of ocean fronts; S4 - Oil tankers path identification, making use of historical AIS data; S5 - Oil spills identification and monitoring, where Sentinel-1 Ground Range Detected Level 1 data is used to detect and characterize possible oil spills; S6 - High-resolution water quality monitoring in anchorage areas, focussing on chlorophyll a (chl_a), turbidity, and total suspended matter (TSM).

The Black Sea and Danube region integrate three basic components - sea basin, coastal areas and catchment areas. Such a collection and integration is essential for an accurate understanding of the region's dynamics in a systematic and holistic manner. With the increasing demands on natural resources arising from extensive human activities, the region faces several environmental problems including serious water, land, air pollution from agriculture, industry and cities.
Agriculture is an important part of the economic development in the Black Sea and Danube region and the basin is home to one of the main global grain production areas as well as high-value crops such as vineyards, orchards and vegetable farming as well as livestock production. In parallel, intensive agriculture and deforestation often contribute to overexploitation of the soil, increase of soil erosion, subsequent sediment input and also have an influence on bed-load transport not only in the immediate area but also far downstream until the river reaches the sea, the final sediment trap. As a consequence, soil degradation is accelerating, exacerbated by human activities such as the inappropriate management of arable land, grassland and forest land. Besides the agriculture priorities, ensuring sustainable use of forest resources is equally important for improving economic, social and environmental conditions in the region. Many countries are currently undergoing inventories of their forests and developing forest investment plans taking into account various public interests and support of forest reconstruction, afforestation, shelterbelt planting, as well as combating threats due to the increasing drought occurrence, forest fires and illegal deforestation is needed.
Therefore, the importance of harmonized monitoring and strategy for land management contributes to solutions for pressing environmental issues and works towards a sustainable future. Region-wide continuous monitoring fosters further innovation in agriculture, forestry and rural areas enhancing the viability and competitiveness of all types of agriculture, and promoting innovative farm technologies and sustainable forest management and among others help to mitigate the impacts of natural disasters and climate change consequences (especially droughts and crop failure).
Most of the Danube or Black Sea regional initiatives target ‘development of critical support systems’ or ‘smart, integrated observing systems’, so current Earth Observation (EO) capacity and derived spatial datasets might represent a major contribution here. A large number of satellite sensors, acquired in different spatial, spectral and temporal resolutions, are currently available for operational support, however, the use of satellite EO within these regional level activities remains still relatively low due to several factors, including requirements for tailored processing of the EO data in each region, requirements for the fusion of a range of diverse and heterogeneous datasets and more structured and integrated results.
The general approach of the project is based on the implementation of a regional web-based application platform providing demonstration and access to value-added services, processing tools and EO & non-EO data sources relevant for agricultural and forestry monitoring. The platform is designed to cover various aspects and with the aim to respond to a wide range of identified user requirements:

- Support the uptake of EO data and information use by the stakeholders involved in agriculture/forestry in the Danube Catchment and greater Black Sea basin
- Endeavour to tie on many other activities aiming at fully exploiting the potential of big data services for EO as well as at reaching towards other domains by promoting the cross-exploitation with non-EO datasets in an efficient computing environment
- To meet both technical and information content requirements and to ensure that BSADRI is correctly aligned and interoperable with them
- Respond to performance levels requirements (as long-term monitoring, country-wide extent - large area coverage, but still maintaining scalable solution addressing spatial and thematic variability)
- Stimulate both data-driven services and use of value-added - synthetic thematic information
- Provide an intuitive environment and descriptive demonstration of EO based services
- To fit needs and requirements of various types of users as international entities, regional organizations, national authorities but also the private sector, NGO organizations or even single farms

For this purpose, the regional platform is composed of interconnected elements covering various functions to target the needs of the above-mentioned bases such as EO data processing tools, value-added services, other services as Copernicus or cloud-based tools & applications or additional information services (EO, Spatial, Natural Resources, Agriculture and Forestry)
For better illustration selected use cases are presented covering various agricultural and forestry domains and involve implementation of value-added services addressed by the list of applications in the domain of Natural Resource Management in Agriculture and Forestry. The services will consume EO products generated by the pre-processing segment and apply tailored processing and analysis through dedicated processing chains. A number of processing routines including machine learning classification, temporal profile analysis, data mining, spatial-temporal feature analysis, data analytics and fusion will be integrated on the platform as scalable capabilities for Big Data processing. The use cases thematically focus on topics related to land balance (Georgia), precision farming, operational monitoring for CAP support and forest management to facilitate forest inventory.

Oil spills on the sea cause dark patches in C- and X-band Synthetic Aperture Radar (SAR) imagery, because they damping small-scale surface waves that are responsible for the radar backscattering. The wave damping, hence the radar contrast, is less pronounced at L-band, except for thick parts of the oil spills [1]. On global scales, the main source of marine oil pollution is operational ship traffic, though static sources such as oil seeps or oilrigs may also contribute. There was, however, no static oil-pollution source reported for the Bulgarian Black Sea coast.

On 1 July 1921, the US cargo steamship SS Mopang sank off Sozopol on the Bulgarian Black Sea coast, close to the city of Burgas, after hitting a sea mine that was left from World War I. After the ship wreck had been lying at a depth of about 30 m for almost one century, in August 2018, local newspapers reported an oil leakage that was observed after a period of strong winds and currents in that area. Heavy fuel, of which the SS Mopang had originally loaded 650 tons, was leaking out of the wreck’s tanks.

We used 16 years of spaceborne SAR imagery, acquired between 2006 and 2021 by Sentinel-1A/B SAR-C, ALOS-1/2 PALSAR-1/2, and Envisat ASAR, to investigate for how long, and under which conditions, heavy fuel was leaking out of the wreck. The oil spill of the SS Mopang was visible on more than 100 SAR images; however, we could not find it on every SAR image acquired in the area, which implied that either the leakage of heavy fuel occurred only sporadically, or a continuous leakage of oil could not be seen on every SAR image.

Further analyses revealed that oil spills, which could be attributed to the Mopang wreck, were detected only in the warmer season, from May to November, when the temperature of the bottom water layer exceeded 10 °C. Apparently, under these conditions the heavy fuel’s viscosity was low enough to allow for its leakage and subsequent rise to the sea surface. In contrast, during colder months, when the bottom temperature was below 10 °C, the viscosity of the heavy fuel was too high and hence, we did not find any manifestation of the Mopang spill on SAR imagery acquired from December to April.

Similar patterns of the environmental conditions were found for all cases, in which we observed a Mopang spill on SAR imagery: periods of high winds, resulting in high sea state, were always preceding each oil spill detection, which implies that some mechanical action on the wreck’s hull was needed to initiate further leakage. In addition, we always found a decrease in significant wave height, wind and current conditions shortly before the SAR image acquisition. We therefore hypothesize that a period of increased mechanical stress on the wreck’s hull was needed to force the leakage of heavy fuel, but that calmer conditions at the time of the SAR image acquisition were needed, likely to prevent the leaked heavy fuel from being mixed with sea water without forming a coherent spill at the sea surface that manifests on the SAR imagery.

In cases when the Mopang spill also showed up on L-band SAR imagery, the leakage of heavy fuel must have been strong enough to form a thick oil spill that caused a detectable contrast on L-band SAR imagery. Estimates of the spill’s thickness, however, were not possible.

The understanding of the marine and coastal bio-optical processes has important benefits for society especially in the areas with high human marine activities, as well as for environmental protection. The Black Sea receives drainage from almost one-third of continental Europe (five times its own surface) that includes significant portions of 17 countries, 13 capital cities, and some 160 million people. However, biogeochemical variables are generally derived from data acquired in the visible range of the spectrum, and thus they are hampered by the presence of clouds, a significant issue in a basin that has a 40-50% of average cloud coverage.

At some especially strong river discharges (such as the Danube plume), there is a strong correlation between Sea Surface Salinity (SSS) and the Colored Detrital Organic Matter (CDM) to the point that one can be considered as a proxy for the other, at least over the area most influenced by the river discharge. This connection is caused by the capability of SSS to track the proportion of freshwater contributed by the river. As far as the proportion of sedimentary material contributed by the river is approximately constant, some biogeochemical variables associated with primary productivity will strongly correlate with the amount of river water being mixed in the sea basin and thus with SSS.
In the framework of the ESA EO4SIBS (Earth Observation data for Science and Innovation in the Black Sea) project, we used this connection to derive a new CDM product in the Black Sea generated from SMOS SSS measurements. Despite its limited resolution (we use L4 SMOS SSS with spatial resolution grid of 0.05x0.05º), this new SMOS-derived product provides CDM information over all-weather conditions. Here we present the methods and the assessment of the performance of this new experimental product in comparison with the available CDM products in the Black Sea.

The monitoring of the Sea Surface Salinity (SSS) in semi-enclosed seas has a significant impact in the study of climate change. In those basins the oceanographic processes occur at shorter temporal scales than in the open ocean, and therefore, trends and anomalies can be detected before. In the Black Sea, river run-off and precipitation exceed evaporation making the typical salinity values much lower than in the global ocean (17-18 psu versus 32-38 psu). Moreover, unlike other large estuarine basins, the Black Sea is a deep basin (maximum depth of ~2200 m) with a large North-Western shelf. A distinct vertical layering is created between the surface waters in the upper 100m and the deep graphic conditions maintained by strong stratification resulting from river runoffs flowing at the surface and the entrance of saline Bosporus waters at depths.

The geophysical characteristics of the basin hinder the satellite SSS acquisition: 1) the dielectric models have some limitations in this range of low salinity values; 2) the strong stratification limits the use of in situ data to calibrate or validate the satellite acquisitions; 3) the sensitivity to potential trends requires a robust stable time series of measurements. Besides there are some already known acquisition issues in the region: 1) Strong land sea contamination; 2) Strong contamination from very close Radio Frequency Interference sources. In the framework of the ESA regional initiative project An Earth Observation Data for Science and Innovation in the Black Sea (EO4SIBS), we have developed new algorithms to deal with these issues. Here we present the enhanced methods used in the generation of the first regional SSS products in the Black Sea and the quality assessment of their performance. The products consists of: a) Daily level 2 maps at 0.25ºx0.25º with an accuracy of 1.5 psu; b) 9-day level 3 maps at 0.25ºx0.25º with an accuracy of 0.5 psu, and; c) daily level 4 at 0.05ºx0.0505º with an accuracy of 0.4 psu.

Many studies have proven the value of machine learning and remote sensing for crop yield forecasting. However, in years of severe droughts, such forecasts get unreliable. The goal of this study was to improve the reliability of the crop yield forecasts, particularly in drought years. For this purpose, the Pannonian Basin in southeastern Europe was chosen as the study area, as droughts have heavily affected agricultural production there in the last decades.

Wheat and maize yields were forecasted using a random forest model on various explanatory datasets based on Earth Observation, in situ measurements, and meteorological reanalysis and forecast data. Two drought indices, Evaporative Stress Index and Standardized Precipitation-Evapotranspiration Index, were used to include information about the occurrence of droughts. The predictions were established monthly for four lead times to harvest. The first prediction was made three months before harvest and the last one at the moment of the harvest. The results were cross-validated using three-year intervals as testing sets. Years of severe droughts, 2003, 2007, 2012, and 2015, were additionally analysed.

The validation showed that good predictions could be made from around two months before harvest. This was reflected in correlations of predicted and measured crop yields around 0.6. Forecasts with longer lead times than that led to significantly worse predictions. In years of severe droughts, the results were ambiguous. The wheat yield forecast model underestimates the crop losses, which led to a bad performance even shortly before harvest (correlations lower than 0.4). The maize predictions, on the other hand, showed good performances in drought years. The model underestimated crop yield losses only slightly, and the validation showed a good performance with correlations around 0.5 in the month before the harvest. Overall, a slight underestimation of drought impacts on crop losses remained for both crops at all lead times. Despite that, the results, especially for maize yields, have a considerable potential to predict crop yields reliably before the harvest and thus contribute to reducing socio-economic impacts of crop yield losses in the Pannonian Basin.

Energy SCOUT is a commercial business intelligence service developed by Terrabotics in collaboration with the European Space Agency (Business Applications Centre), following a successful kick-start project in 2018/2019. Energy SCOUT stands for Energy Supply Chain Observation & Understanding Tool and uses cutting-edge satellite remote sensing & advanced image data analytics to shine a new light on oil & gas and energy supply chain activities around the world.

Energy SCOUT information products are built around user needs and requirements captured thorough rigorous engagement with a representative selection of end users. End users were chosen from identified customer segments and actively engaged through a pilot program. Energy SCOUT products are intended for commercial application by end users such as oil & gas operators, private equity firms, ESG investment groups, and commodity traders. The ultimate goal of the Energy SCOUT products are to enable better decision making based timely and unbiased information, underpinned by Earth Observation data.

Energy SCOUT products have been built by leveraging open-source satellite data. The products provide reports on a weekly basis over areas on the order of tens or even hundreds of thousands of square kilometres. By combining raw satellite data of various spatial, temporal, and spectral resolution together with technologies like Artificial Intelligence, Computer Vision and Machine Learning, activities and changes can be detected fast and information can be provided routinely. The radar, optical, and thermal satellite sensor that were used to develop the modules are the Copernicus Sentinel 1, Sentinel 2 and NOAA VIIRS respectively.

Energy SCOUT is a modular set of data services, employing feeds of open-source satellite and reported in-situ data and transforming them into value-added information. The information services target different activities along the oil & gas supply chain. The Energy SCOUT modules have been released in stages since 2017.

Currently available modules include ShaleAnalystTM and FlareAnalystTM, which launched as a commercial pilot in July 2021. The ShaleAnalystTM module is a shale well-level activity and event tracking tool, that provides information about well pads, water ponds and lease roads, as well as monitoring oil & gas well pad lifecycle on a weekly basis using optical and radar satellite data. The FlareAnalystTM module is an Oil & Gas flaring activity monitoring tool, that delivers reports about flare activity detected by satellites grouped by operators on a monthly basis.

Energy SCOUT roadmap modules include various tools, for example TBX Basin Production Index as an oil & gas production proxy index, EmissionsAnalyst for field/facility emission monitoring, RefineryAnalyst for oil refinery activity monitoring, TBX Pipes for pipeline activity monitoring or ESG Observer & LandMetric for rapid site & biodiversity evaluation.

With funding from the European Space Agency’s Business Applications programme, Earth-i is exploring the integration of multi-sensor and multi-operator satellite data and the application of advanced AI technologies to benchmark, monitor and forecast supply chain activity. A range of satellite and in-situ data are being routinely collected and processed to understand production levels and environmental impact of global commodity supply chains including copper, nickel and steel production. Early results have been extremely promising, and Earth-i is now actively working to develop the operational proposition and to commercialise the capability.

The products are being developed initially for the financial services markets, and for this Earth-i is working with Marex, a world leading UK-based commodity broker who provides clients with an unrivalled trading platform and breadth of coverage in metals, energy and agricultural markets. Initial customers of the service range from physical traders to hedge funds, quantitative funds and commodity research organisations. For non-earth observation companies like these, the challenges of identifying, procuring, processing, analysing and integrating satellite data are too significant, and so the benefits of satellite derived datasets are failing to be taken up, despite their significant value in answering complex supply chain questions.

The supply chain data does not need to be 100% accurate, as they are not the sole information source used for decision-making: sometimes only a notification of change of direction or speed is enough to trigger further investigation. Conversely, significant events of this kind can only be detected by monitoring hundreds of locations at a high temporal frequency to identify significant changes or abnormal behaviours, be it at global, regional, countrywide or individual site level. They key is to be able to detect changes over time at key nodes of each supply chain and in the environment surrounding those nodes, and to create absolute and relative measurements of those changes.

With a multi-year historic time-series of high-revisit images, such as those now available from satellites including the Copernicus Sentinels, it is possible to gain a deeper understanding of supply chain activity, productivity and environmental impact over multi-annual periods. A multi-sensor approach also minimises the limitations of relying upon a single data type, by using optical satellites for object identification and change detection, SWIR and IR for thermal feature and change detection, complemented by SAR and InSAR to ensure coverage during cloudy periods or for night-time observation. The combination of sensor types gives good primary and secondary indicators of activity to a high level of confidence, while the multi-operator strategy limits the reliance upon any single source of data whilst increasing the data capture opportunities.

With the application of machine learning techniques, it is possible to set up automated processing chains to detect change over time to a high level of accuracy, whilst keeping the manual quality control burden down to minutes on a daily basis. Such automated detection helps to provide a consistent level of accuracy that allows hundreds of sites to be analysed and published daily within a couple of hours of data receipt. Machine learning also unlocks the value of data not visible to human eye within high resolution and medium resolution data sets. This has a significant commercial benefit in keeping the service affordable, unlike services based upon VHR data only.

The products generated are of significant interest to the financial services sector not only because of their ability to help inform of likely movements in commodities and equities market prices, as well as acting as leading indicators for macro-economic performance, but also because they act as an independent evidential benchmark of the commodity asset owners on their net zero and environmental sustainability targets and provide a means of measuring the impact of their ESG investments over time.

In this presentation Earth-i will demonstrate the latest developments from the project, including several examples of the high-revisit, multi-sensor approach, the machine learning analytics applied, and the commercial products generated for financial and regulatory institutions to exploit, and how the work is set to drive transformational change in the industries being monitored.

The goal of the AquaLedger project is to demonstrate the integration and connection between Distributed Ledger Technology (DLT)/blockchain and earth observation (EO) technologies in the sector of supply chain management. More specifically, automated earth observation based analytical processes will be employed to provide valuable information about the tracking and tracing of fishery and aquaculture commodities and/or the verification and appending of transactions recorded in the blockchain. Thus, the activity aims to advance current knowledge and expertise in DLT and EO convergence resulting in an integrated system that will be demonstrated in real life conditions while also addressing the needs of the relevant actors in the supply chain management of the aquaculture sector. The implemented system is validated in the context of near-shore aquaculture activities.

A sustainable supply chain can be defined as a process of providing services to the aquaculture users during a way that fosters continuous and reliable water quality information and indicators. Water quality remote sensing constitutes a fast-growing field spurred by major investment into satellite observation capabilities. In aquaculture, water quality monitoring is required not only for sea farms but also for hatcheries that pump water from the ocean, especially because of certain species (i.e. fish larvae) that are very sensitive to water quality issues. The quality of the water where the aquaculture activities take place, affects the final products, jeopardizing the security of the food that reaches the consumer. However, despite its undeniable value, this type of information is not usually tracked and monitored during the supply chain of the food.

Thus, the main use cases of the implemented system involve i) employing EO based services towards the provision of accurate information about the water quality of aquaculture assets as well as the farming productivity ii) showcasing how EO data can connect the physical environment to the distributed ledgers and be utilised for validating the conditions of transactions between actors in the supply chain and iii) monitoring the compliance of the food movement in the supply chain

Among the abovementioned water quality parameters, sea surface temperature is a key factor because it influences the variation of many physical and biochemical seawater properties as well as the entire life cycle of the fish. Sea surface temperature is monitored through the use of the Sea and Land Surface Temperature Radiometer (SLSTR) of Sentinel 3 with a spatial resolution of 1 km. Additionally, turbidity, chlorophyll-a and nutrient concentration are very important parameters for health and growth whilst the monitoring of pollution factors is essential for preventing their catastrophic impact (i.e. high mortality rate) on fish farms. Various algorithms (empirical, semi-empirical and analytical) were tested to generate the essential water quality information from atmospherically corrected Sentinel 2 MSI and Sentinel 3 OLCI data which have been widely used in the context of water quality monitoring studies. The most appropriate algorithm was selected through the use of field observations collected at the fish farming site. In the context of this application case, the Case 2 regional Coast Colour (C2RCC) processor and empirical algorithms were utilized for the acquisition of water quality parameters from Sentinel 2 MSI. The sea skin temperatures values extracted from Sentinel 3 SLSTR were also considered accurate if prior outlier detection and removal is performed. It must be noticed that the Sentinel 2 data were able to extract valuable water quality information both with analytical and empirical algorithms. Sentinel 3 OLCI data did not achieve competitive results given that it has a limited performance when it comes to coastal areas or similar ecosystems laying within a short distance from the land.

Last but not least, in order to serve the AquaLedger application cases for seafood supply chain, different DLT/Blockchain platforms have been analysed towards the realisation of the most effective solution. External events being provided by EO component and other external data sources (i.e. Enterprise Resource Planning software) are incorporated in the DLT system through the realisation of web-services.

Fisheries are the major activity exploiting marine resources and have a significant impact on marine life as well as on the economic development and food security of nations. Nowadays, more and more consumers are demanding sustainable and responsible fish products and the comprehensive monitoring of fishing activity provides new insights into improving the transparency of the fish supply chain. While policies and regulations attempt to control and manage the origins of fish, however, the traceability of the fish supply chain presents many obstacles concerning the data availability.

Deimos and IPMA (Instituto Português do Mar e da Atmosfera), with the support of the Portuguese Directorate-General for Natural Resources, Safety and Maritime Services (DGRM), are developing the pilot application “Monitoring Fishing Activity” for the e-shape project (Horizon 2020, Grant agreement ID: 820852). This is a unique initiative that brings together decades of public investment in Earth Observation (EO) and cloud computing resources to develop services for decision makers, citizens, industry and researchers, always in close collaboration with key users.

The main goal of this pilot is to strengthen the knowledge on the fish supply chain by developing an operational EO service that monitors the dynamics of vessels operating in the Northeast Atlantic waters, focusing on two fleets involved in pelagic fisheries of highly migratory oceanic species: the pole and line and the drifting longline fisheries targeting tuna and swordfish respectively. Using data from the 2012-2018 period recorded within the Portuguese ZEE, the results aim to characterize and quantify the fishing pressure of these fleets on marine ecosystems, and to relate fisheries with environmental parameters. A further objective is to raise awareness among key users of human footprint in marine ecosystems and motivate them towards more efficient, environmentally friendly and sustainable fishing strategies and practices, complying with international, European and national regulations.

Key organizations involved in the fisheries monitoring at different levels have been providing different sets of requirements based on their needs. These organizations range from public institutions as DGRM, the central service of the Portuguese State for the administration and implementation of policies on fisheries and the Regional Directorate for Fisheries for the Azores region (DRPA), academic centres as the Centre of Marine Sciences (CCMAR, one of the foremost marine science research centres in Portugal, and non-governmental organizations as Sciaena, whose mission is to promote the implementation of the sustainability of fisheries.

Fisheries management is continuously frustrated by the lack or poor quality of critical data on fish catches/ sizes, fishing locations, and relevant environmental conditions. While quantitative methods for managing fisheries have developed considerable complexity, the quality and availability of data remains an obstacle to meaningful advances in fisheries management. The integration of information technology will play a major role in fisheries management and end-to-end traceability of the fishing activities. To that end, the application currently being developed integrate the following key datasets:
- Automatic Identification System (AIS) data for fishing vessels. AIS is a surveillance solution that uses transceivers on ships which can be tracked by terrestrial stations across the coastline or with satellite receivers. Information provided by AIS, such as unique vessel identification, position, course, and speed, can be displayed on an electronic chart and/or ingested into an information system.
- eLogBooks.Electronic records of catch and effort registered at the time of the catch operation that need to be reported to National Fisheries Control Authorities. Data transmission to authorities can be at the time of landings or immediately after the fishing operation has been concluded and catches recorded. Logbooks are widely used as a method of collecting statistical information on commercial activities.
- Sales. Often referred to as landing declarations, reporting the weight and value of the vessel sales by year and fishing port.

Based on those key datasets, three different products are provided to users within the Monitoring Fishing Activity pilot:
- Fishing Trips. Trajectories of fishing vessels from the Portuguese pelagic longlining and pole and line fishing fleets based on satellite AIS points datasets. From the analysis of the speed profiles of vessels, additional information is provided for the possible activity status in each AIS emission data point - fishing and non-fishing.
- Fishing Footprint. Geographical distribution of the fishing activity in a period of time. The results are based on the aggregation of the AIS data points that were identified as fishing over a certain grid.
- Fishing sales. Yearly sales in quantity (ton) and percentage rate of target species per port. Sales reports of the anonymized vessels and the weight amounts are aggregated according to the fishing fleet, year, fishing port, and target species.

During the first half of 2022, Deimos and IPMA will add environmental characterization of fishing areas to the service portfolio. The proposed service takes advantage of state of the artf EO-based datasets that provide key parameters such as Chlorophyll concentration, Sea Surface Temperature and others, correlating those with tuna and swordfish catches reported in elogbooks. This shall allow a better understanding of the behaviour of those fishing fleets and how they align with the environmental conditions of fishing grounds.

Moreover, a major purpose for this project is to disseminate the outcomes of these services through a web-based tool following rigorous access criteria for the different users, to be established by IPMA and DGRM. In that regard, Deimos is currently working on integrating the services presented here into the NextGEOSS platform, an integrated ecosystem for users to access EO data and deploy EO-based applications.

The United Nations Economic Commission for Europe (UNECE) has led a process to develop a free opensource standard and system to track and trace products through the entire garment and footwear supply chain – from field to factory to shop floor, enabling companies to make verifiable sustainability claims that consumers, governments and regulators can trust. This short presentation will focus on the UNECE Toolbox, including Policy, Standard and Guidelines as well as it’s work on Blockchain Pilots for enhance transparency and traceability in the garment and footwear sector.

There is an increasing need to track food from the source to the end stages of consumer outlets, primarily in order to ensure the safety and quality of the produce. Implementation of the European Green Deal strategy will require improved due diligence of deforestation-free commodity production, sustainable fisheries, fair trade, organic foods, carbon emission reduction from supply chain activities, etc.

To date, knowing whether or not a company’s supply chain is sustainable, and is not related to e.g. deforestation, remains a challenging task, because of either the lack of supply chain data on environmental factors, or because of limited visibility into supply chain asset data relations. Lastly, an equally important factor are the linkages between the environmental data and the supply chain data.
In terms of traceability, there are significant new technological opportunities in the context of “tracking and tracing” of agriculture commodities, in particular by the convergence of traditional EO information services with emerging distributed data, platforms and IoT sensor networks.

The lack of data on environmental factors has been largely solved due to advances in cloud computing, allowing the low cost processing of satellite imagery at scale. Satellites acquire petabytes of earth system imagery every day. This data contains valuable information on supply chain sustainability and transparency waiting to be uncovered.

In this session we highlight the possibilities of using new traceability technology together with satellite data for end-to-end supply chain due diligence and supply chain management. We will provide case study results with a special focus on deforestation due to the pending EU Regulation to mimimise EU-driven deforestation and forest degradation. Specifically, we focus on the latest practical challenges and opportunities of Distributed Ledger Technologies as a typical hyped "newtech", OCG standard setting and encrypted analysis on sensitive commodity sourcing geodata, in relation to its use in agricultural commodity supply chains in combination with satellite derived business insights.

The ALTIUS mission: operational and scientific objectives
Didier Fussen, Noel Baker, Antonin Berthelot, Emmanuel Dekemper, Philippe Demoulin, Quentin Errera, Ghislain Franssens, Nina Mateshvili, Nuno Pereira, Didier Pieroux and Sotiris Sotiriadis
BISA
Adam Bourassa, Doug Degenstein, Nicholas Lloyd and Daniel Zawada
U.SASKATCHEWAN
Navarro Reyes, Daniel; François, Michael; Montrone, Luciana; Santandrea, Stefano; Frommknecht, Björn; Zehner, Claus; Wehr, Tobias
ESA-EO

ALTIUS is an original, groundbreaking mission which answers open questions and needs within the atmospheric remote sounding community. The project was proposed by the Belgian Institute for Space Aeronomy (BISA) and it is presently implemented as an element of the ESA Earth Watch program. The data processing algorithms will be developed in a collaboration between BISA, the University of Saskatchewan and the industry. Ozone and aerosol stratospheric profiles are the main operational objectives of the mission.
The truly innovative technology being developed through ALTIUS resides in the use of a high-performance microsatellite of the PROBA class that can operate in a multi-mode approach. Starting from a well-stabilized and controlled platform attitude, a single sensor can be optimized for a combination of limb scattering measurements and solar, stellar, and planetary occultations. The combination of an imager with an accurate attitude control allows for determining the tangent altitude by relating the instrument field of view to the satellite attitude. Moreover, occultation observations can also be carried out in inertial pointing without the need for an onboard tracking mechanism. Twilight observations will also be performed, to inter-compare occultation and limb retrievals for large solar zenith angles.
The ALTIUS instrument bridges urgent and significant gaps that exist in the availability of long-term satellite measurements which are essential to the climate research community.

ALTIUS is a limb sounder mission for the monitoring of the distribution and evolution of stratospheric ozone at high vertical resolution in support of operational services and long term trend monitoring. The ALTIUS mission will provide detailed stratospheric ozone profiles information at high vertical resolution, which adds valuable information to total column ozone used for data assimilation systems by operational centres based on nadir sounders.
The ALTIUS data will also be of high importance for the atmospheric modelling community, for use as input to climate models and their validation. It will reduce a gap in high-resolution limb observation data. It has the potential to contribute to the GCOS (Global Climate Observing System) ozone profile ECV (Essential Climate Variable). Off-line ALTIUS products are relevant for the validation of the Copernicus Atmosphere Monitoring Service (CAMS) model. Other secondary products, such as Aerosols extinction, will be provided as well for scientific interests.
The geophysical validation of the different ALTIUS products will provide also valuable scientific opportunities.
The ALTIUS mission is under implementation by ESA within the Earth Watch Program, with participation of Belgium, Canada, Luxembourg and Romania. Based on a PROBA-NEXT Platform, the satellite embarks an optical instrument made of three independent channels (VIS, UV and NIR) to observe the Earth limb through a field of view of 100 km x 100 km, measuring scattered sun light (bright limb) and atmospheric attenuation (solar and stellar occultation mode) in order to retrieve ozone and other species.
Project is going through phase C with an expected launch mid-2025.
The current Project status and plans covering flight model manufacturing, launch, commissioning, validation and routine operations will be presented.

The ALTIUS instrument is a three channel, imaging instrument that plans to provide global measurements of ozone limb profiles for the atmospheric community. The instrument utilizes acousto-optic tune-able filters (AOTF) in the visible and near infra-red channels and a 4-element stacked Fabry-Perot imaging complex in the UV channel. All three channels observe solar occultation and bright limb measurements while the UV channel additionally observes stellar occultation. The large dynamic range of signals coupled with novel imaging technologies presents interesting challenges for instrument calibration which are discussed.

Stratospheric aerosol plays an important role in the Earth’s radiative balance, stratospheric composition, and atmospheric dynamics. The background stratospheric aerosol layer is commonly perturbed by large volcanic eruptions, and more recently by smoke from large wildfire events in Australia and western North America. The limb scatter measurement technique used by ALTIUS is highly sensitive to stratospheric aerosol extinction from the cloud-top to altitudes approaching 40 km. Stratospheric aerosol is also an interfering species for the ozone retrieval; accurate knowledge of the stratospheric aerosol extinction is important to retrieve accurate ozone. Here we present the algorithms developed to retrieve stratospheric aerosol extinction from ALTIUS measurements developed at the University of Saskatchewan. The algorithm uses measurements in the visible and near-infrared spectra and builds upon heritage from the Optical Spectrograph and InfraRed Imager System (OSIRIS) and Ozone Mapping and Profiler Suite-Limb Profiler (OMPS-LP) missions. Recent scientific results using limb scatter stratospheric aerosol observations will also be presented, with an emphasis on how future measurements from the ALTIUS mission will add scientific value.

Ozone retrieval algorithm for ALTIUS observations
Didier Fussen, Noel Baker, Antonin Berthelot, Emmanuel Dekemper, Philippe Demoulin, Quentin Errera, Ghislain Franssens, Nina Mateshvili, Nuno Pereira, Didier Pieroux and Sotiris Sotiriadis

BISA

The retrieval of ozone number density profiles with a good vertical resolution is the major objective of the ALTIUS mission.
The preliminary retrieval algorithms will be presented for various observation geometries: solar limb scattering, solar occultation and star occultations.
For limb scattering, several innovative approaches will be introduced, including the use of principal components, the computation of the radiative transfer field by Montecarlo methods, the selected step-by-step spectral processing and an iterated chi-squared method.
For solar occultations, the processing of the full solar disk will be addressed in the case of a full spectral imager. For stellar occultations, the influence and the mitigation of star scintillation will be discussed.
The expected performances of ozone retrieval will be compared and discussed with respect to the mission requirements, in terms of SNR and averaging kernels.

Programmed as an element of ESA’s Earth Watch programme, Atmospheric Limb Tracker for Investigation of the Upcoming Stratosphere (ALTIUS) has been developed as a gap filler mission responding to the urgent need to continue the global, long-term of monitoring of stratospheric ozone, related species and aerosols at the vertical resolution of the order of a few km. Planned for operation in the 2025-2029 period, ALTIUS will continue the essential Climate Data Record (CDR) on stratospheric ozone started in the 1980s with the SAGE series and extended successively with other limb/occultation sounders on the UARS, Odin, Envisat, ACE, Aura, and OMPS-LP missions. Recognized by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV), the vertically resolved ozone CDR is essential for large-scale research activities endorsed by the World Climate Research Programme (WCRP) under its core project Stratosphere-troposphere Processes And their Role in Climate (SPARC), and for stratospheric ozone assessments published every four years by the World Meteorological Organization (WMO) and United Nations Environment Program (UNEP) in the framework of the Vienna Convention for the Protection of the Ozone Layer. The Earth Watch programme aims at fostering the delivery of Earth observation data to operational services. As a contributor to this programme, ALTIUS will provide daily global measurements of the ozone profile in near real time to operational services like the Copernicus Atmosphere Monitoring Service (CAMS) and the Belgian Assimilation System of Chemical ObsErvations (BASCOE). In delayed mode ALTIUS will also provide consolidated ozone data records to the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S).

The present contribution introduces to the approach being developed for the necessary geophysical validation of ALTIUS ozone profile data within the operational constraints of the Earth Watch programme. After a review of mission and user requirements against which ALTIUS data will have to be validated, the different objectives of ALTIUS validation during the commissioning phase of the mission and beyond will be discussed. The validation approach includes classical comparisons to independent measurements collected from ground-based monitoring networks contributing to WMO’s Global Atmosphere Watch (GAW), namely, balloon-borne ozonesonde data for the lower part of the ALTIUS vertical range up to ~30 km, differential absorption lidar data (DIAL) for the stratospheric range up to ~45 km, and millimetre wave radiometer data for the stratospheric and mesospheric ranges. Comparisons with respect to observations from other satellites will extend the ground-based validation to the global domain and to quality features not detectable with point-like ground-based measurements. Quality assessments will be complemented by modelling support. A routine validation service for ALTIUS will include baseline monitoring of the geophysical data quality relying on an operational validation system, and in-depth validation by a dedicated team who will produce consolidated validation results and give analysis support to the evolution of the retrieval algorithms. An Announcement of Opportunity for the calibration and validation (Cal/Val) of ALTIUS will be published approximately two years before launch, with the aim to open the ALTIUS Cal/Val to the international community and to a wider range of external data and activities, to foster exchanges within the validation community and with the instrument and algorithm experts, and to promote the use of ALTIUS data.

Pan-Arctic sea ice thickness has been monitored by the European Space Agency SAR altimeter CryoSat-2 since 2010. However, sea ice thickness observations are so far restricted to Arctic winter months (Oct-Apr) because conventional processing techniques fail to work when meltwater ponds accumulate at the ice surface during summer months. Conventional radar waveform classifiers cannot differentiate melt pond-covered ice floes from leads and the ponds introduce a bias to the radar range measurement. Our current understanding of basin-scale sea ice melting patterns during summer are limited to weakly constrained ice-ocean model simulations, at a time when ice thickness observations would be most valuable for sea ice forecasting.

Here we will present a new method for generating valid sea ice thickness observations in Arctic summer months (May-Sept) over the ten-year period between 2011 and 2020. We use 100s of optical and SAR images coinciding with CryoSat-2 orbits to train a 1D convolutional neural network for separating altimeter returns from melting ice floes and leads. Pan-Arctic sea ice radar freeboards are mapped from the difference between floe and ocean surface elevations. A set of radar echo simulations are then performed to quantify the range bias caused by melt ponds dominating the reflected altimeter signal. Corrected sea ice freeboards are converted to thickness by accounting for the ice density and the residual snow load accumulated on the ice. We find that CryoSat-2 sea ice thickness observations capture the spatial patterns of sea ice thickness in July-September measured by airborne EM-bird sensors on Alfred Wegener Institute campaigns. The seasonal cycle of CryoSat-2 observations also matches well to ice draft observations from the Beaufort Gyre Exploration Program mooring sonars between May and Sept in 2011-2018 and to the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) reanalysis. Finally, we will demonstrate how summer sea ice thickness observations have a longer ‘memory’ than ice concentration observations, controlling the persistence of ice area anomalies over many months. CryoSat-2 summer sea ice thickness observations can therefore benefit seasonal forecasts of sea ice extent, skilfully predicting ice extent at lead times up to 10 months, both on a pan-Arctic and regional basis.

Sea-ice thickness is a key factor and indicator in understanding the impact of the global climate change. Deriving basin-wide sea-ice thickness estimates from satellite laser and radar altimetry relies on freeboard measurements. The freeboard-to-thickness conversion in turn requires information of snow mass and the density of the sea-ice layer that have unknown spatio-temporal variabilities and trends directly translating into the uncertainty of decadal sea-ice thickness data records. In addition, inter-mission biases arise from, e.g., different sensor types and frequencies as well as varying footprint sizes affected by surface roughness across regions and seasons. Therefore, carrying out validation and inter-calibration studies is crucial for reliable and continuous observation of the Earth’s cryosphere. To achieve this, it is beneficial to have simultaneous measurements of freeboard, snow depth, and sea-ice thickness, which provide reference data for both direct satellite observations and geophysical target parameters.

Here, we present Alfred Wegener Institute’s (AWI) IceBird program, which is a series of fixed-wing aircraft campaigns to measure Arctic sea ice and to monitor its change. During two late-winter campaigns in the western Arctic Ocean in 2017 and 2019, we have carried out surveys with the unique scientific instrument configuration including an airborne laser scanner (ALS) for surface topography and freeboard measurements, a tethered electromagnetic induction sounding instrument (EM-Bird) for total (snow+ice) thickness measurements, and an ultrawideband frequency-modulated continuous-wave microwave radar to measure snow thickness. Therefore, we are able to observe all three bounding interfaces in the sea-ice–snow system in high resolution along survey tracks on regional scales.

During the ship-based drift expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) between October 2019 and September 2020, helicopter surveys were carried out in high spatio-temporal resolution throughout the year, including the polar night, to measure freeboard and roughness with the ALS both in local grid pattern and in larger scale. Coincident EM-Bird ice thickness data and information from snow measurements on the ground will help linking these parameters and monitor them and their effect on satellite retrievals for a full seasonal cycle.

The individual parameters are important for describing and monitoring the state of the Arctic sea ice and validating retrievals from satellite data, but combined they offer further possibilities to characterise sea ice. By assuming isostatic equilibrium, we are able to estimate up-to-date bulk density values for different sea-ice types from the IceBird data and to derive a parametrisation of sea-ice bulk density based on sea-ice freeboard. These data allow us to explore spatio-temporal variations in sea-ice parameters observable from space and to evaluate the validity of the freeboard-to-thickness conversion in satellite altimetry through comparison against dedicated satellite overpasses and orbit collections.

About a third of Greenland’s total ice losses come from the Northwest sector, a sector that includes a large number of marine-terminating outlet glaciers, which have all experienced widespread retreat triggered by ocean-induced melting. Accurately estimating the mass balance of this region using satellite altimetry has therefore important implications and requires robust estimates of surface elevation change. Airborne campaigns across the Greenland Ice Sheet provide an extensive dataset to validate satellite radar altimetry estimates of surface elevation and elevation change.
Here, we measure changes in surface elevation in the Northwest sector of the Greenland Ice Sheet from CryoSat-2 between July 2010 and July 2021 and find that the surface has lowered at a rate of 21.9 ± 1.1 cm/yr on average over this period, with rapid thinning at the ice sheet margins at a rate of 46.9 ± 5.9 cm/yr. To validate this dataset, we compare our satellite radar rates of elevation change to airborne laser altimetry data from NASA’s Operation IceBridge. Overall, we find a good agreement between the two datasets with a mean difference of 6.7 cm/yr and standard deviation of 73 cm/yr. Differences between satellite radar and airborne laser rates of elevation change are larger in the coastal SARIn region of the sector with a mean difference of 9.5 cm/yr, while difference are almost negligible in the interior LRM region with differences of -0.7 cm/yr. In addition, we examine ASIRAS Ku-band radar and ALS laser data collected during the ESA CryoVEx campaigns of 2012, 2014 and 2016 in the Northwest sector to further investigate differences between satellite and airborne measurements of surface elevation.
We further compute mass change from our validated dataset of CryoSat-2 surface elevation change, and we show that the Northwest sector lost 456 ± 5.7 Gt of ice between July 2010 and July 2021. By partitioning our mass balance estimates into sub-regions of the Northwest sector, we can compare our mass balance estimate to independent estimates from gravimetry and the mass budget method across different spatial scales. We show that our altimetry estimate is the least negative across all regions, the gravimetry and mass budget estimates alternating in recording the largest ice losses. Our comparisons reveal that the spatial pattern of differences between mass balance estimates is complex, suggesting that discrepancies between techniques do not solely originate from one single region or technique. However, airborne campaigns such as ESA CryoVEx and NASA Operation IceBridge campaigns play an important role in investigating the existence of potential biases in satellite radar altimetry estimates of surface elevation change and their subsequent impact on mass balance.

Snow on the sea ice is an element that is still very poorly known and poorly observed. However, it plays an important role in climate change due to its albedo, and in the dynamics of the sea ice due to its insulating properties. This lack of knowledge is also one of the main factors limiting the measurement of sea ice thickness by altimetry.
A method to measure this snow depth was first proposed in [Guerreiro et al. 2016]. It is based on the comparison of two altimetry frequencies, the Ku frequency on board CryoSat-2, and the Ka frequency from Saral. Indeed, these frequencies have different snow penetration properties. This observation was one of the reasons for adding a second frequency to the Copernicus CRISTAL polar altimeter project.
Following this study, an Altimetric Snow Depth (ASD) product was developed in the Arctic as part of the ESA CryoSeaNice project. In the framework of the ESA CS+AO project, an equivalent snow depth product has also been computed in Antarctica. These products are presented in [Garnier et al. 2021].
The first objective of this presentation is to show the relevance of this approach and the contribution of these data to the measurement of sea ice thickness.
To do so, the ASD data are compared with different datasets from different sources: space data obtained with the AMSR-2 passive radiometer, model data and in-situ data.
The AMSR-2 data are currently the only other snow depth observations that provide monthly estimates of snow depth on the polar ice pack. The first version (Meier et al, 2018) available on the NSIDC website (https://nsidc.org/data/AU_SI12/versions/1) has the disadvantage that it only covers first-year ice. The Bremen AMSR-2 v1.0 product (Rostosky et al, 2018) is calculated on multi-year ice but only for the months of March and April (during the Operation Ice Bridge campaigns). Only the NSIDC product covers the Southern Hemisphere, but this is complete because in this region the pack ice is mostly first-year ice.
Furthermore, in the Arctic, we will compare the ASD data with the Warren99 modified climatology (Arctic only), the PIOMAS model, the NESOSIM model (Arctic only) and the CMEMS LIM-3 sea ice model. The performance of these snow depth solutions will be evaluated by comparison with: 1) several Operation Ice Bridge (OIB) airborne campaigns, 2) the 2017 ESA-CRYOsat Validation Experiment (CryoVex) campaign that includes the KAREN Ka-band airborne altimeter, and 3) Beaufort Gyre Exploration Project (BGEP) data. We will also present the impact of different snow depth solutions on sea ice thickness estimates.
Finally, the important potential of these results for the monitoring of sea ice evolution by the future Copernicus CRISTAL mission will be discussed.
Acknowledgements: This work was supported by the ESA CryoSeaNICE project, the ESA CryoSat+ Antarctica project and the CNES TOSCA CASSIS project.

Sea ice and its snow cover play key roles in Earth’s climate and satellite altimetry can provide basin-wide estimations of sea ice and snow thickness. These approaches to retrieval from satellite data are based on assumptions about the dominant scattering surfaces of the radar waves in order to estimate the elevations of air/snow and snow/ice interfaces. Often it is assumed that the Ka-band mainly scatters from the air/snow interface and that Ku- and lower frequencies scatter from the snow/ice interface.

In this presentation we summarise data gathered using nadir-looking surface-based radars and coincident field data from multiple campaigns including MOSAiC, CryoVEx and SIPEX 1, conducted in the Arctic and Antarctic between 2007 - 2020. We compare the snow and ice conditions and radar waveforms, across the campaigns. Our results demonstrate the complexity and variability of scattering observed in radar data gathered over 2-18 GHz, Ku-band (13 GHZ) and Ka-band (35 GHz) frequencies. We investigate the effects of the snow and sea ice properties and meteorological conditions as well as radar frequency and polarisation on the waveforms.

We find, for instance, that the snow/ice interface is not usually the dominant scattering surface in the Ku-band, but that it was for a set of very low-density snow pits over Antarctic sea ice. We also show that meteorological conditions in the past, as well as when the snow and ice were sampled, play an important role in the scattering characteristics of the snow and ice as observed by the radars. Lastly we outline the implications of relating data from ground- and satellite-borne instruments, and how these insights might be applied. We discuss barriers to direct application, such as scales and geometries, and how the information gained through these studies and how these relate to analysis of data from current and future missions such as CryoSat-2 and CRISTAL.

Reference and repeat-observations of land-ice topography is critical to identify causal links between climate and ice trends, generate an accurate record of ice mass balance, and quantify land ice contribution to sea level change. Over the last 30 years, radar altimetry has been instrumental in monitoring ice sheets and their contribution to sea level change. Launched in 2010, the European Space Agency Altimetry mission CryoSat-2 was the first radar altimetry mission with a SAR/Interferometry radar altimeter payload. The aim of this new technology was to gain a better insight into the evolution of the cryosphere, in particular over the steep slopes typically found along ice sheet margins and glaciers, where the majority of the mass loss is taking place. CryoSat’s revolutionary design features a Synthetic Interferometric Radar Altimeter (SIRAL), with two antennas for interferometry, the corresponding SAR Interferometer (SARIn) mode of operation increases spatial resolution while also increasing the accuracy of the geo-location by resolving the angular origin of off-nadir echoes occurring over sloping terrain.

While the elevation of the Point Of Closest Approach (POCA), or level-2, is the standard product of the CryoSat-2 mission, the Interferometric mode of CryoSat-2 provides the ability to resolve substantially more than just the elevation at the POCA and thus led to a paradigm shift in radar altimetry observation over land ice. The so-called “swath processing” exploits the fact that over sloping terrain, CryoSat-2 altimeter operates in a manner such that the interferometric phase of the altimeter echoes may be unwrapped to produce a wide swath of elevation measurements across the satellite ground track, well beyond the POCA only. This technique provides the opportunity to increase spatial resolution and to recover elevation over regions where conventional radar altimetry fails; providing an opportunity to monitor land ice trends globally from radar altimetry.

Here I will discuss work done over the last decade to develop and apply swath processing over all components of land ice including the Greenland and Antarctic Ice Sheets, ice shelves, and glaciers worldwide. Swath-based studies have quantified global ice mass balance, sub-glacial lakes activity, ocean melting of ice shelves, contributing to a better understanding of processes linking ice trends to climate forcing. I will also highlight swath elevation as a CryoSat dataset over land ice globally as part of the EOLIS implementation of the CryoTEMPO programme. Finally, I will discuss opportunities to expand the application of swath processing further, in particular in the context of CRISTAL whose primary mode over land ice will be SARIn and with swath processing as a core technology to derive time-dependant ice topography.

Amongst the manifold human pressures impacting marine ecosystems, eutrophication and extreme weather events frequently result in microalgae coastal proliferation. Accurate observation of massive phytoplankton blooms in the coastal ocean is however challenging because phytoplankton composition and concentration can change over short time scales and because their spatial distribution typically displays small scale variability. While remote sensing can provide spatially and temporally resolved observations, the existing satellite missions present limitations in temporal, spatial, and/or spectral resolution when it comes to monitoring phytoplankton blooms in estuaries, bays, fjords, or coastal lagoons. Compared to previous missions, Sentinel-2 (S2) has been demonstrating improved capabilities for the detection of nearshore harmful algal blooms (HABs) due to its ability to synoptically observe inland and coastal waters every 5 days at a spatial resolution higher than 20 m in 10 spectral bands from the visible to short-wave infrared spectral domains. In addition, S2 provides accurate measurements of the water reflectance due to the development and implementation of atmospheric correction and deglinting algorithms specifically designed for optically complex coastal zones. Due to its enhanced observation capability, S2 therefore made it possible to detect and document several highly-concentrated phytoplankton blooms over a variety of coastal and inland waters during the last six years.
Despite such recent improvements, the majority of HAB remote sensing investigations generally focused on specific regional case studies. Therefore, only a modicum of bloom forming microalgae species has been separately documented so far. The objective of the present study is to propose a broader perspective for the remote sensing assessment of phytoplankton blooms, in order to better resolve the optical diversity of green, golden, red and brown seawater discolorations associated with the taxonomic and pigmentary diversity of phytoplankton coastal proliferation and/or accumulation. For that purpose, a database of high-biomass blooms and phytoplankton seawater discoloration events, both documented in situ and synchronous to S2 image acquisition, has been compiled from the IOC-ICES-PICES Harmful Algae Event Database (HAEDAT), from the French Phytoplankton and Phycotoxin Monitoring Network (REPHY), and from several phytoplankton events reported in the literature. Altogether, about 100 bloom records were compiled in more than 20 countries worldwide. For the selected blooms, available in situ information reported that the phytoplankton community was generally dominated by one or two microalgae species. The compiled S2 HABs database covered 25 different bloom forming phytoplankton species belonging mainly to the dinoflagellates, ciliates and cyanobacteria groups. The spectral characteristics of the remote-sensing reflectance (Rrs) were then extracted for each dominant species, and analyzed in order to evaluate S2 ability to distinguish phytoplankton optical types in the case of highly concentrated and quasi-monospecific coastal blooms.
Whatever the species, all Rrs spectra displayed a typical red-edge pattern with a valley at 665 nm and a peak at 705 nm, resulting from the interplay between the optical properties of pure seawater and chlorophyll-a at high concentration. Optical discrimination of the dominant bloom forming species could therefore not be based on the sole red-edge pattern. The variety of bloom optical types was better resolved when investigating Rrs spectral shape variability over all S2 spectral bands from 443 to 900 nm. Preliminary results from commonly used normalization and classification methods suggest that at least six bloom types could be distinctly identified using S2, including blooms dominated by Trichodesmium (a phycoerythryn-rich cyanobacteria), Noctiluca scintillans (a dinoflagellate causing orange seawater discoloration), and Mesodinium rubrum (a ciliate responsible of burgundy red tides). These species present typical pigment profiles and/or physiological characteristics that give them distinctive Rrs features. For the cyanobacteria other than Trichodesmium, blooms dominated by Nodularia spumigena and/or Aphanizomenon flosaquae displayed a Rrs spectrum whose shape significantly differs than those of blooms dominated by the Microcystis and/or Anabaenopsis genera. For the dinoflagellates other than Noctiluca scintillans, the Rrs variability was high at both intra- and interspecific levels, therefore generating a higher risk of confusion. However, blooms dominated by species such as Lingulodinium polyedrum or Prorocentrum sp. displayed a different Rrs shape than blooms dominated by Karenia sp. Blooms dominated by Lepidodinium chlorophorum or Margalefidinium polykrikoides could be confused with non-dinoflagellates blooms dominated by the haptophyte Phaeocystis globosa.
While the present compilation is inherently limited to the available concomitant in situ and satellite HAB datasets, it aims at shedding light on the visible tip of coastal eutrophication amenable to multispectral satellite detection. Improving the capability of remote sensing to detect massive red tides and phytoplankton seawater discoloration is indeed crucial because coastal algal proliferation is likely to increase in both frequency and amplitude in the next decades due to escalating nutrient runoffs from agriculture and nearshore urban development.
To our knowledge, this is the first time that a Rrs library of massive coastal blooms has been compiled from the S2 archive. The main reflectance types could guide a first guess identification of the dominant bloom-forming species in the absence of in situ information. Such spectral library also interestingly complements studies based on laboratory phytoplankton cultures, where accurate measurements of the inherent optical properties are performed at species level. It is however challenging to measure Rrs in the lab, as well as to extrapolate laboratory results to the scale of real phytoplankton events. By providing the bulk signature of typical bloom types that also contains the second order variability associated with non-algal colored constituents, the compiled S2 Rrs library could benefit to the development of optical models for coastal waters. From a satellite mission perspective, the current limitations of our S2-based reflectance classification are also useful to highlight which bloom forming species present a risk of confusion, and for which further work is required using either an enhanced spectral resolution and/or advanced clustering algorithms.

Since 2011, Caribbean small island developing states (SIDS) have been impacted with inundations of a large seaweed, Sargassum, creating a multitude of economic and environmental issues. Sargassum inundations impacts tourism, fisheries, endangered nesting turtles and seabirds, and coastal ecosystems including coral reefs. The source of the Sargassum is in the North Equatorial Recirculation Region (NERR), in the North Atlantic, southeast of the Caribbean Sea. A large bloom of Sargassum forms annually in the summer months and is referred to as the great Atlantic Sargassum belt (GASB). In its peak, the GASB can extend to the Gulf of Guinea, West Africa to the Caribbean Sea with a wet weight of 29 million metric tons but varies annually. Factors that might influence the growth of the GASB include increased nutrients, from either river discharge or wind-driven upwelling, and a change in sea surface temperature. Movement of Sargassum between the GASB and the Caribbean Sea, however, is more influenced by wind and waves as well as the large-scale ocean currents.

Sargassum can be observed in optical imagery using both the Alternative Floating Algae Index (AFAI) and the Maximum Chlorophyl Index (MCI). MCI data is available from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) products at 300m resolution. AFAI data is available using Moderate Imaging Spectroradiometer (MODIS) products at a 1km resolution. Due to the temporal resolution needed, AFAI images of Sargassum are used to create a time-series of the bloom distribution but smaller key areas may be examined using MCI data at a higher resolution. A combination of Earth observation data with particle tracking tools (Ocean Parcels) using a Copernicus Marine Environment Monitoring Service global ocean reanalysis model will be used evaluate the role of possibly physical and biogeochemical drivers of the GASB’s growth and variability.

This research project aims to answer three key questions to further understanding of the GASB phenomenon:
1. Why did the GASB form in 2011?
2. What is the likely cause of known intra and inter annual variability in the GASB?
3. Does the GASB form in the west of the Equatorial Atlantic, and is it greatly influenced by the Amazon River plume size and direction?

It is possible that the effects of Climate Change caused a ‘tipping point’ that shifted Sargassum from the Sargasso Sea in the North Atlantic to the NERR. As the GASB is vulnerable to a range of physical and biogeochemical processes, further understanding of the GASB involves looking at the ecosystem of the Equatorial Atlantic which is made accessible due to Earth observation data.

Macroalgal beds provide important ecosystem functions and services, such as contributing to carbon sequestration and biodiversity. Macroalgal abundance along polar coastlines, including those in Greenland, the world’s largest island, may be changing due to ocean warming. The remote and harsh environment makes in situ monitoring difficult and costly. Therefore, a lack of baseline data in Greenland complicates efforts to assess changes in macroalgal abundance. Satellite remote sensing has proven to be an effective large-scale monitoring tool for submerged vegetation in shallow and relatively clear coastal waters. Here, we provide the first fjord-scale distribution estimates for intertidal and shallow, submerged macrophytes (primarily macroalgae but also some seagrass) in the Nuup Kangerlua, Kobbefjord, and Ameralik fjord systems using 10-m resolution Sentinel-2 MSI images. Macroalgal abundance is estimated by applying commonly used vegetation indices (FAI, NDVI, EVI) and supervised classification algorithms. The Sentinel-2 images are also used to quantify the spatiotemporal variability of ice and turbidity to examine possible relationships between the impacts of light availability and the distribution of macroalgae. Classification of coastal macroalgae in this region is complicated by large seasonal changes in solar zenith angle, sea ice cover, icebergs, and turbidity. Other challenges include frequent cloud/fog cover, large tidal range, proximity of exposed intertidal macroalgae to terrestrial vegetation, and topographic shading. Furthermore, submerged macroalgae in these fjord systems do not form canopies that float on the surface. Our classification targeted submerged, sub-tidal macroalgae and intertidal macroalgae whose degree of exposure varies with the tidal stage. We minimized the seasonal influences of solar zenith angle and sea ice by limiting the analysis to the months of May-September. Centimeter-scale drone orthomosaics and drop-camera images were used to validate the satellite-derived macroalgae distributions. As topographic shading and turbidity limit detection in some areas, and macroalgae growing at water depths deeper than 10 m are not detected, the Sentinel-2-derived macroalgae distribution presented here should be considered a conservative estimate. Our approach to classify coastal marine macrophytes in complex fjord environments in Greenland form the basis for expanding the mapping along in coastal Greenland and elsewhere in the Arctic.

Tropical regions harbour some of Earth’s most productive and diverse marine ecosystems, which provide important services for human populations. Under future scenarios of global climate change, potential alterations to phytoplankton ecological indicators are predicted to occur in the tropical oceans. Therefore, there is an increasing requirement for the synoptic monitoring of such indicators in marine ecosystems. Phytoplankton phenology and size structure are key ecological indicators that influence the survival and recruitment of higher trophic levels, marine food web structure, and biogeochemical cycling. For example, the presence of larger phytoplankton cells within marine ecosystems supports food chains that ultimately contribute to fisheries resources. In addition, the timing of food availability in tropical marine ecosystems has far-reaching impacts on higher trophic levels, reef-dwelling organisms, and coastal fisheries that are an invaluable economic resource in tropical regions. Monitoring these ecological indicators can thus provide important information to help understand the response of marine ecosystems to environmental change. Here, we use an interdisciplinary approach to investigate the interannual variability of phytoplankton ecological indicators in response to climate warming within tropical sub-regions of the global oceans. Remotely-sensed datasets are used to derive and investigate the mechanistic linkages between phytoplankton indicators and the physical environment, and where possible, we extend our analysis using available in situ datasets (e.g., BGC-Argo) that describe concurrent biophysical changes within the deeper layers of the water column. Furthermore, we attempt to bridge the gap between long-term trends in phytoplankton dynamics, regional warming, and stocks of commonly-fished, planktivorous pelagic species (e.g., anchovies and sardines) over the oceanic regions of interest. We note that the results of this research are preliminary and are currently ongoing as part of a recently-funded European Space Agency Living Planet Fellowship. This project aims towards understanding the trophic linkages between phytoplankton indicators and fisheries under a changing climate.

Overexploitation of marine fishing grounds is a worldwide problem that is increasing through the last decades analogous to the introduction of new fishing technologies and the reduction of fish stocks. As the fish population in an area decreases, professional fishermen must put more effort to sustain their production numbers and in turn increase the pressures on the ecosystem creating a perpetual loop that inevitably leads to economic and environmental collapse. Therefore, sustainable development in the fisheries industry requires a balance between viable production rates and fish stock monitoring by the competent authorities to intervene if and where it is necessary.

The present study aims to establish a processing chain for monitoring the large-scale distribution of small pelagic fish using remote sensing data. Copernicus Sentinel-3 data are used to calculate the oceanic parameters such as chlorophyll-a, which drives the primary production, and Sea Surface Temperature (SST) from which many species depend. Daily Sentinel-3 data are used for the calculation of chlorophyll-a concentration and SST, from the sensors OLCI and SLSTR. To compensate for missing pixels due to cloud cover and invalid measurements, a spatiotemporal interpolation method has been developed to produce Level-3 information. The identification and localization of mesoscale oceanic fronts are performed by a gradient-based technique. For this process, the original, interpolated data are used for each parameter to calculate the ocean color and thermal fronts for the chlorophyll-a and SST parameters respectively.

Another variable that is related to the spatial distribution of small pelagic fish is the oceanic circulation and specifically the formations of permanent and seasonal mesoscale oceanic fronts. For this purpose, data for the daily circulation characteristics are computed from an in-house developed gradient-based methodology. These variables, with the addition of bathymetry data, are used in a Random Forest regression model to identify the daily Potential Fishing Zones (PFZs). The model is calibrated and validated with the use of field acoustic measurements of fish biomass.

Final PFZ daily data are produced with the co-assessment of all the variables with a dedicated trained Random Forest regression model. For the training of the model real, four years of acoustic data of sardines (Sardina pilchardus) and anchovies (Engraulis encrasicolus) are used as collected in the framework of the Mediterranean Acoustic Survey (MEDIAS) carried out in the North Aegean Sea (Eastern Mediterranean). In correlation to the field measurements, the model results present an accuracy of 81% on identifying areas with high biomass, showcasing the great potential for detecting near real-time PFZs. In terms of variable importance, the higher score has Chlorophyll-a concentration and after it, in descending order, the bathymetry, the SST followed by the thermal and ocean color fronts. Even though oceanic fronts hold a vital role in small pelagic fish distribution the correlation depends on various factors, such as seasonality, the width and scale of the fronts, and the unique biological and oceanographic characteristics of each surrounding area. As such, further study improvement must examine the field data's spatial relation to the observed permanent or seasonal fronts and classify them in order of importance for the local fish populations.

The proposed methodology can equip the fishery management authorities with the necessary tools for accurate and continuous monitoring of the fishing fields. This information can lead to sustainable development in the fishing sector with a better understanding of the spatial and temporal system dynamics. Furthermore, the results of the model consist of ready-to-use daily data layers that can easily be incorporated into other scientific fields, such as biodiversity studies, without the need for GIS and spatial analysis expertise.

With the aim of safeguarding species diversity of our oceans, it is fundamental to investigate and track the pressure of fishing fleets. Poaching is a global problem that can damage the biodiversity of both terrestrial and marine protected areas. The unsustainable exploitation of fish stocks by overfishing in protected areas during a time when the fishing is not allowed is a contributing cause for fish population decline and grave environmental damages. Indeed, pirate fishers often use prohibited fishing gears and unregulated practices potentially fatal for entire marine habitats and ecosystems.

Transceivers equipped onboard ships, including Vessel Monitoring Systems (VMS) and Automatic Identification System (AIS), can be used for identifying ships and tracing their routes. However, the IMO (International Maritime Organisation) regularizes the usage of such monitoring systems only for specific classes of ships (> 300 tons). Besides, this collaborative system can be intentionally turned off during unlawful activities, which according to Frontex occur most of the time night-time.

On the other hand, ship detection methodologies based on spaceborne Synthetic Aperture Radar (SAR) images promote extensive applications to achieve effective maritime security, thanks to the big amount of available data and cloud penetrating capabilities. In addition, the synergetic exploitation of multifrequency/multimission data can provide better treats at sea by using multiple images gathered with short time intervals (below 15 minutes) and with different radar backscattering properties. In this context, the COastal Area monitoring with SAR data and multimission/multifrequency Techniques (COAST) project aims at


1) identification of non-cooperating and visible ships in SAR images by means of pre-selection and discrimination approaches applied to multi-frequency data acquired from different missions, selected on the basis of revisit time analysis;
2) identification of non-cooperating and non-visible ships in SAR images, by means of identification and study of wakes in multi-frequency SAR images;
3) classification and 3D reconstruction of ships by means of SAR tomographic techniques;
4) joint use of SAR and AIS data for estimation of motion components (direction, heading, speed) and characterization of ships.

To achieve these purposes, a ship detection pipeline is constructed for multimission data analysis including Sentinel-1, Cosmo-Skymed, and SAOCOM data. The study is carried out by implementing a cascade approach that employs a sub-look analysis (SLA) for the removal of false alarms after the pre-screening. Tomographic SAR processing of fully polarimetric data sets acquired in multi-baseline (MB) interferometric SAR configuration, instead, is used to analyze and characterize the scattering mechanisms of complex targets, as ships. A polarimetric approach to SAR tomography, indeed, is a useful tool to characterize different components of complex volumetric media, in terms of height positions but also by their physical properties.
All these techniques are used for maritime applications including illegal fishing, maritime trafficking, and management of protected coastal areas.

Agricultural field boundaries information is an enabler for numerous agricultural applications. For example, it produces relevant input for crop monitoring and yield forecasting applications; it provides a foundation in countries without cadastral data; it is applicable in precision agriculture. Additionally, an up-to-date Integrated Administration and Control System (IACS) including parcel geometric delineations with an accuracy better than 1:5000 is a prerequisite to monitor the EU’s Common Agricultural Policy (CAP) subsidy expenditures. Therefore, improving IACS by exploiting the potential of digital technologies, eventually reducing administrative burden for farmers, paying agencies and other stakeholders is needed. An automatic system for parcel boundary delineation based on satellite or aerial imagery can aid the farmers to speed up the declaration process and the paying agencies to better monitor changes in agricultural use. Automating the process of mapping these boundaries using remote sensing imagery would permit a more agile and rapid update, especially when large swaths of land are in scope. However, this is a complex task due to the limited number of available data sets, the lack of detection methodology benchmark, and the high imbalance between classes. Besides, the field boundaries are frequently irregular with lower spectral contrast among the edges, and the field area and rapid seasonal changes may further complicate the detection process.

This contribution aims to advance and facilitate development and comparison for field boundary detection. Specifically, we want to benchmark several deep learning techniques such as the UNet, DeepLabv3 and the Vision Transformer. Furthermore, as the data set is extremely unbalanced (0.027% boundary vs 99.973% interior pixels), we will also explore the combination of models, optimisers, loss functions, and accuracy metrics that yield the best results. Our initial results have shown that segmentation boundary loss and triangular cycling learning rate outperform baseline losses such as binary cross-entropy (BCE), intersection over union (IoU) or Dice because they do not penalize boundary misalignment adequately.

To create a strong benchmark, we will use an open AI-ready data set, which will be presented in another session, that consists of a single-date data set (labels + images) based on a 1-m aerial orthophoto for regional-scale analyses.

To facilitate benchmarking with other methods as well as uptake from end-users, we will publish the full comparison results, model weights and source code. Such results and open source code would highly be beneficial for national administrations to update their parcel mapping.

Agriculture and rural areas are central to the European Green Deal and the new Common Agricultural Policy (CAP) will be instrumental in delivering on this high European Green ambition by managing the transition towards a sustainable food system.

For the period 2023-27, the new CAP will be built around nine key objectives, among which the climate change action, the environmental care, the landscape and biodiversity preservation and the food and health quality protection. Each European country will have to design a national CAP strategic plan to contribute to these nine objectives, combining funding for income support, rural development and market measures. Eco-schemes are a new instrument in the CAP to be included in the national strategic plans. They need to receive at least 25% of the budget for direct payments, providing stronger incentives for climate- and environment-friendly farming practices and approaches (such as organic farming, agro-ecology, carbon farming, etc.) as well as animal welfare improvements.

This new CAP legislation requires new tools for deciding the payments allocation and assessing the policy impact. This is what is called the “Monitoring and Evaluation Approach”, which moves from the process of controlling the agricultural practices compliance with the regulations over 5% of the arable land in each country to the process of monitoring the agricultural practices over the whole countries and during the whole year. The monitoring approach has the twofold objective to accompany the farmers towards more sustainable practices and to simplify the Integrated Administration and Control System (IACS - which is the European system to administer CAP payments) and make it more efficient. Within this reform, new technologies and satellite Earth Observation (EO) is seen to take an increasing role.

In 2017, ESA launched the Sentinels for Common Agricultural Practices (Sen4CAP) project which was aimed at demonstrating how Sentinel derived information can support this new “CAP monitoring approach” through use case studies. The Sen4CAP project was conducted with the guidance of a Steering Committee made of DG-Agri, DG-JRC and DG-Grow. Czech Republic, France, Italy, Lithuania, Netherlands, Spain and Romania act as pilot countries.

The project delivered an open-source Sen4CAP system which automatically ingests and processes Sentinel-1 and Sentinel-2 (and possibly Landsat 8) time series in a seamless way to compute more than 120 markers every week for each parcel and store them in a massive database which is growing over time. The usefulness and relevance of these markers were demonstrated in the framework of three case studies selected from an in-depth consolidation of Users Requirements: the crop diversification, the monitoring of permanent grassland and of Ecological Focus Area (EFA). These three schemes are the actions that farmers have to put in place to receive the Greening Payment.

Based on the Sen4CAP markers database, four EO products were generated in 2019 and 2020: (i) crop type maps, (ii) grassland mowing detection, (iii) harvest detection and (iv) monitoring of catch crops, nitrogen-fixing crops and land-lying fallows. The products accuracy was assessed based on the subsidy applications layer provided by the Paying Agencies, farmer’s interviews and Planet data. This demonstration was run on the cloud at national scale in the seven pilot countries (more than 15 million of parcels monitored over 635.000 km²). It was conducted hand in hand with the Paying Agencies, ensuring to evaluate the markers usefulness to support the payment decision and enabling a continuous adjustment of the developed methodologies to their real needs.

The overall accuracy of the national crop type maps (focusing on more than 100 crop types by country) ranged between 74% and 97%. The recall and precision values of the grassland mowing detection ranged from 72% to 88% and from 51% to 77%, respectively. In the EFA context, the harvest of the main crop was first detected: most of the harvest events were detected within a week (values ranging from 53% to 75%) or within two weeks (values ranging from 69% to 89%). The markers used to assess the compliancy of the parcels regarding the practices’ regulations were shown to correspond very well, with a few exceptions, to the validation data.

The first version of the open-source Sen4CAP system was released in November 2019, with a dedicated hands-on training organized in January 2020 with 44 participants coming from 20 different countries. Regular webinars have been organized, with more than hundred participants each time, and a very active forum is now in place. In total, more than 40 Paying Agencies have been engaged with the project, having either used or installed the system or participated to trainings. So far, the Sen4CAP system has more than 370 downloads and the version 3 is now available, showing the very active users’ community behind. Indeed, the markers database which was demonstrated in the limited context of the selected Greening use cases, can be accessed through an API interface and therefore used in a very flexible way according to each Paying Agency and new application.

From 2021, the Sen4CAP system in general - and more specifically, its makers database - is evolving to support new use cases more related to the sustainable agriculture practices and the policy performance measurements. So far, this evolution relied on users’ feedback and now, a formal selection of new use cases coming from Paying Agencies is taking place. They might be for instance the length of the period where bare soil is present, the detection of land cover change, the presence of irrigation, the computation of an agri-environmental indicator for “High Nature Value” farmland. These new use cases will be developed and assessed with Paying Agencies during the first half of 2022, and then implemented into new open-source modules in the Sen4CAP system.

Leading the way to digital CAP monitoring; a fully operational national-scale monitoring system in Denmark

Satellite remote sensing provides extraordinary potential for timely and effective CAP monitoring. Denmark has been one of the leading countries in Europe to embrace Earth Observation (EO) data for CAP monitoring as a means to substitute costly on-the-spot checks. In this presentation we will share our experiences from the last years cooperation between DHI and the Danish Agricultural Agency that has now led to a fully operational national-scale system used by the authorities in Denmark. In 2021 we just finalized the second year of a fully operational season in Denmark monitoring of approximately 600.000 agricultural parcels for various requirements.

The system includes three core components to provide a streamlined end-to-end monitoring system (VeriCAP) 1) a powerful cloud-based back-end used for image processing and database creation, 2) an analysis toolbox used for deriving key information and statistics for individual parcels, and 3) an intuitive front-end web viewer to visualize and interpret results. Once results are received by the Danish Agricultural Agency, they are shared with the farmers in an online traffic light GIS system ensuring transparence and allowing the farmers to follow the status of the remote sensing monitoring results during the season. Via an App the farmers can send in geo-tagged photos as proof of activity at the end of the season if their parcels were flagged as possibly non-compliant during the automatic monitoring.

Our work so far has focused on use Sentinel-1, Sentinel-2, and Landsat imagery for providing near-real time information at the national scale. We present insights into using this rich time series of imagery for parcel boundary validation, crop classification, monitoring grassland activity, harvest detection, as well as catch crop monitoring. Furthermore, methods have been developed and implemented to deliver results not only on the parcels level but on the sub-parcel level to allowing the paying agency to follow activities on the sub-parcel level.

We also discuss some key technical challenges related to agricultural monitoring including training data collection and dealing with imbalanced classes. Overall, we show a practical ‘real world’ implementation of a CAP monitoring system that has been well received by public authorities and the farming community. In 2021 the solution has received two Danish awards: the Danish public sector innovation prize in September and in November at the annual Danish geospatial professionals gathering the solution won the jury prize.

Grassland mowing event detection from multi-sensor time series to evaluate agri-environmental measures – a model comparison exercise


Grassland areas are an essential part of the agricultural landscapes across Europe and provide key ecological functions and services in a multifunctional agriculture. The conservation and sustainable use of grassland areas directly contributes to policies, strategies and measures at European and national level that aim at a transition towards more sustainable agriculture within the scope of the European Green Deal.

Grassland areas are managed intensively or extensively for the provision of food, fodder or raw biomass for energy production. Due to their high relevance for the preservation of biological diversity, they are subject to agricultural policy measures as well as nature protection. Besides overall conversion embargos, those measures are also targeted towards a more extensive use of grassland, which is known to have positive impact on biodiversity. On the other hand, the management intensity in grasslands can also be an indicator for the evaluation of climate protection measures (e.g., in peatlands). For meadows, the management intensity can be described by proxies such as the mowing frequency where a higher number of cuts indicates higher intensities. Besides mowing frequency, the date of mowing (e.g., date of first cut) is also a relevant parameter for the evaluation of agri-environmental measures under the Common Agricultural Policy (CAP), e.g., for fallow land.

However, as information on grassland management is usually not reported there is a lack of knowledge on the spatial distribution of grassland use intensity. Lately, it has been shown that the availability of dense time series of remote sensing data enables to fill this gap. Various approaches have been published that make use of radar data, optical data or a combination of data from both domains. Two recently published studies of the authors underlined the overall potential of dense time series from combined Sentinel- and Landsat-data (Lobert et al. 2021; Schwieder, et al. 2021). In another recent publication, de Vroey et al. (2021) evaluated the rule-based approach of the Sen4Cap processor for the detection of mowing events for Belgium, which was developed in the Sentinels for Common Agricultural Policy framework, and makes use of Sentinel-1 and Sentinel-2 time series. All studies highlight the potential as well as limitations of the proposed data and methods for selected regions and provide accuracy measures for the evaluation of their results. However, the accuracies among the different studies are hardly comparable due to different settings and measures of the validation framework.

Against this background we conducted a model comparison study to predict mowing date and mowing frequency in grassland and fallow land from dense time series of Sentinel-data and third-party missions. We here present the concept and result of the comparison study where we estimated mowing events and dates for the federal state of Brandenburg using i) a convolutional neural network approach with time series of Sentinel-1, Sentinel-2 and Landsat 8 data (Lobert et al. 2021), ii) a rule-based algorithm using time series of Sentinel-2 and Landsat 8 data (Schwieder et al. 2021), iii) a rule-based algorithm based on Sentinel-1, 2 and Landsat 8 data (Schwieder et al. 2019) and iv) the Sen4Cap processor (de Vroey et al. 2021). The validation concept builds on parcel boundaries as an input, for which we used the Land Parcel Identification System (LPIS), which enabled to differentiate fallow land, temporally used and permanent grassland. However, as these data are not always widely available and do usually not allow to differentiate management variations within grassland parcels (e.g., partly mown and/or pasture use), we additionally derived parcel boundaries for Brandenburg based on a processor for the fully automatic parcel delineation from S1/S2 data as proposed by Tetteh et al. 2021, and compared the output from both data sets, including an independent validation from field management information and PlanetScope data.

We will present the results of the comparison exercise as well as a feasibility use case, in which we use the estimated grassland management information for the assessment of measures in nature protection (e.g., FFH areas) and agricultural policy (e.g., ecoschemes for the next CAP period). The output of this feasibility study will be summarized as an outlook for the future application of Sentinel-based indicators of grassland management within the monitoring and evaluation framework of the CAP.


References:

De Vroey, M.D., Radoux, J., Zavagli, M., Vendictis, L.D., Heymans, D., Bontemps, S., & Defourny, P. (2021). Performance Assessment of the Sen4CAP Mowing Detection Algorithm on a Large Reference Data Set of Managed Grasslands. In, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 743-746).

Lobert, F., Holtgrave, A.-K., Schwieder, M., Pause, M., Vogt, J., Gocht, A., & Erasmi, S. (2021). Mowing event detection in permanent grasslands: Systematic evaluation of input features from Sentinel-1, Sentinel-2, and Landsat 8 time series. Remote Sensing of Environment, 267, 112751.

Schwieder, M., Frantz, D., Loibl, D., Griffiths, P., Pfoch, K., Lilienthal, H., Hostert, P. (2019). Grassland Use Intensity Metrics from Sentinel-1 and Sentinel-2 Data. In, ESA Living Planet Symposium Milan, Italy.

Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2021). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 112795. https://doi.org/10.1016/J.RSE.2021.112795

Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719.

Sentinel-based agricultural subsidy monitoring is applied by an increasing number of national paying agencies. The aim of our study is to test the application of Sentinel data analysis for preliminary checking of claims and monitoring SAPS (Single Area Payment Scheme) and greening subsidies at national scale in Hungary. Preliminary checks involve identifying false claims by crop type and parcel boundary before the final deadline of claim correction (early June). SAPS eligibility is checked based on the presence of a cultivated crop, the absence of weeds, mowing and avoidance of overgrazing. The greening criteria checked include crop diversification at farm level, maintaining sensitive grasslands, fallow land, nitrogen-fixing crops and catch crops. The pilot project was checked against the requirement of reducing the necessary on-the-spot checks from the currently required 5% of the parcels.
Sentinel-2 images were pre-processed in the open-source FORCE framework to the level of bottom of atmosphere (Level-2A) images. The first step was to develop a random forest based crop classification scheme at the level of individual parcels. For this, we used Red Edge 1, Red Edge 2, Shortwave Infrared 1 and Shortwave Infrared 2 bands, calculated NDVI, Bare Soil Index, Enhanced Vegetation Index, Structure Insensitive Pigment Index and developed our own Yellow Crop Index. Parcel boundaries were extracted from all claims and the mean values of these indices extracted within each parcel and for each image date. These time series of the individual parcels were categorized with a random forest classifier, and probabilities of individual classes were output for all parcels. This allowed calculation of categorization certainty using the probability surplus index, enabling the paying agency to inform those farmers whose claims were non-compliant with the highest certainty. For the preliminary checks, a system of 9 crop classes was developed and gradually upgraded to a scheme of 22 classes for the monitoring pilot. Parcel boundaries were identified by raster analysis using directional kernel edge detection. Cultivation events such as mowing, overgrazing, sowing and harvesting were identified from NDVI timelines using expert-generated rulesets.
For the initial check, Sentinel-2 based crop classification of the time series between January and the end of May provided an overall accuracy of 76%. For the time series of the whole year, the 22 classes reached an accuracy of 88%, with the most widespread crops (winter cereals, maize, sunflower, rapeseed , grapes) reaching accuracies above 90% (89% for grasslands). For both cases, ranking the parcels based on probability surplus enabled selection of the cases where erroneous categorization was least probable. Detection of cultivation events was possible with an overall accuracy of 86%, as checked against visually interpreted parcels. These processing steps were combined in a decision tree ruleset for checking the individual subsidy criteria.
Basic cultivation was checked based on the crop classification together with fuzzy probability of the "weed" class. Additionally, the presence of at least one mowing event and the absence of overgrazing was checked for grasslands. For crop diversification, the only parameter available for remote sensing was whether the crop identified from the imagery matched the claimed crop. Maintenance of sensitive grasslands was also checked using the classification results. Identifying fallow land ecological focus areas required a combination identifying set-aside parcels with classification and checking that no mowing has taken place. For nitrogen-fixing crops, rules include a fixed period of cultivation which was checked using the NDVI time series together with the crop class. Finally, for catch crops the same cultivation period check was performed. For this set of monitoring criteria, the number of uncertain cases identified was slightly below 5%, with most of these cases contributed by uncertain classification of rare crops or fallow land.
All in all, the monitoring pilot demonstrated how Sentinel-2 can be reliably used for monitoring these regulations. Fuzzy classification and the evaluation of individual probabilities proved crucial for separating erroneous claims from likely image misclassifications. However, there was one major limitation: the size and shape of the monitored parcels was constrained by the spatial resolution: parcels of less than 40 meters width were considered too narrow. Hungarian agriculture is characterized by relatively small parcels, therefore close to 40% of the parcels (by count) were below this threshold in 2020. By merging adjacent small parcels that were covered by similar crops, 25% of the parcels were successfully added back to the database and analysed, but the rest had to be excluded.
Additional improvements for the 2021 season included integration of Sentinel-1 SAR polarimetry data into the crop classification, deep learning-based parcel boundary detection, and a more inclusive parcel size rule. By adding Sentinel-1, the accuracy of the 2020 preliminary check (which had only 9 classes) was reached for 22 classes in 2021. Deep learning based parcel edge delineation provided a basis for quantitative evaluation of boundary cardinality and correctness. Reducing the parcel size rule to a minimum of 15 pixel centroids within the parcel outline after a negative buffer of 5 meters reduced the number of excluded parcels to only 3% of the total count.
The responses of farmers contacted during preliminary checks were overwhelmingly positive: many of the claims were successfully modified, avoiding penalty administration or unjustified payment. The monitoring pilot also allowed detection and follow-up of many cases of non-compliance that would otherwise have been undetected. In the future, deep learning based classification and the inclusion of SAR polarimetry to cultivation detection is expected to further improve accuracy, and a smartphone app will also be introduced in order to facilitate communication with farmers.

Mountain agriculture is a cornerstone of alpine identity having tremendous impacts not only on socio-economical systems but also on climate change adaptation and mitigation, biodiversity, and risk reduction. It is also because of these positive externalities that mountain agriculture is supported by the Common Agricultural Policy (CAP) and its national and regional implementations. In the last years, the unprecedented opportunities represented by EO-based approaches led to the development of many initiatives aiming at monitoring agricultural practices from space. The most relevant one is the Sen4CAP project that provides validated algorithms, products, and workflows tailored to the modernization and simplification of the CAP and to the improvement of the Integrated Administration and Control System (IACS). In this presentation, we will discuss the first results from the application of Sen4CAP in an alpine region (Aosta Valley, approximately 3000 Km2, Western Alps). We will focus in particular on the assessment of the accuracy of the mowing detection product by comparing the results with field observations (i.e. phenocam network). The seasonal course of canopy greenness computed from phenocam data can indeed provide relevant opportunities to automatically detect mowing and grazing practices and thus analyze the accuracy of Sen4CAP results: number of use and date of use. Secondly, we compare Sen4CAP outputs with the results of a methodology that we developed based only on Sentinel-2 data for the same purposes. Our approach relies on the fitting of seasonal ndvi trajectories and on the unsupervised detection of mowing and grazing events based on the differences between actual trajectories and theoretically unmanaged ones. One of the main differences with the Sen4CAP approach is that the outputs of our methodology are provided either as raster and as vector layers allowing a more sophisticated evaluation of accuracy and spatial representativity of the results. We will discuss the pros and cons of the two approaches with some suggestions of possible further developments.

The Planet Fusion Surface Reflectance (PF-SR) product is a daily, 3 m, gap-free, analysis ready data product in four spectral bands (blue, green red, NIR). It is generated using a rigorous methodology to enhance, harmonize, inter-calibrate, and fuse optical data from PlanetScope, Landsat-8, Sentinel-2, MODIS, and VIIRS. An integral part of this methodology is gap-filling to ensure a spatially complete and temporally continuous product, irrespective of the actual acquisition coverage and cloud environment. This gap-filling subprocess is informed by cloud masked (but not yet gap-filled) PF-SR data from both before and, if available, after the prediction/gap-fill date. Both spatial and temporal interpolation techniques are used to provide an informed and weighted estimate of the gap-filled pixel values. For larger gaps or when data after the prediction date is limited, vegetation growth trajectories from past growing seasons for the given area are matched with current trajectories/phenologies and used to guide predictions. The uncertainty of the gap-filled pixel values is closely related to the temporal gap between actual observation data. In this work, we explore the fusion of Sentinel-1 SAR data with the PF-SR product during the gap-filling subprocess. The main goal of incorporating SAR data into this process is to decrease the temporal gap between actual observation data and improve/validate the uncertainty estimates of gap-filled pixel values. As SAR data is unhindered by cloud cover or smoke, exploiting the SAR data in the gap-filling process also serves as a means of capturing outlier events (e.g., early harvest, flooding, fires, etc.) during extended periods of obscured or missing optical data. We present our initial results for the fusion of Sentinel-1 SAR data with PF-SR data. With a focus on agriculture, we investigate the interoperability of Sentinel-1 with PF-SR, showing the synergies that exist between these different data sources. We discuss the importance of data quality when it comes to establishing phenological relationships and patterns between SAR and optical data. We then show that exploiting the temporal cadence of Sentinel-1 data and fusing it into the gap-filling process can improve the data quality and confidence estimates of PL-SR products. This improves the capability of PF-SR products to provide a more complete and accurate panorama of phenology in the agricultural domain.

Hydrological variability is widely recognized as a key ecological factor in river ecosystems (Jacobson and Jacobson, 2013). As a result, these ecosystems have been classified according to their hydrological characteristics, but these classifications exhibit biases for intermittent or ephemeral hydrological regimes. Ephemeral rivers, characteristic of arid and semi-arid areas, are numerous in the world but only few information about them is available. An ephemeral river is defined by an absence of water flow interrupted by flooding (Jacobson and Jacobson, 2013). The Kuiseb river is one of the twelve ephemeral rivers of Namibia, in South West of Africa. It originates in a semi-arid high plateau (~1,700 m, with 200-300 mm/year of rainfall), and crosses the hyper arid Namib desert (~10-50 mm/year of rainfall, Benito et al., 2010). The Kuiseb River is the border between the South composed of sand dunes and the North with gravel plains. The rainy season, from November to April, can be at the origin of flash floods, with an average duration of few hours to few days. Floods in ephemeral rivers are the major source of water for humans and ecosystems, it is therefore necessary to study them to better understand them (Grodek, 2007; Benito et al., 2011). They present very complex longitudinal, lateral, vertical and temporal gradients. Following a flood, water quickly infiltrates the substrate and then provides the necessary humidity for vegetation and recharging of aquifers. In addition, infiltrated water provides the necessary resources for humans and wildlife. Thus, understanding floods and associated losses is crucial for better management of water resources in arid zones. The study of water flow events, including magnitude, frequency and duration, are essential hydrological components (Jacobson and Jacobson, 2013). However, quantification of water loss and flood routing is limited because very few in situ stations are located in arid environments, and few flooding occurs. Space remote sensing offers the possibility of studying these extreme phenomena in arid environments, thanks to a spatial and temporal resolution adapted to these phenomena. This study aims at monitoring the postflood behavior of the Kuiseb River using Sentinel-1-2-3 missions. We focus on the exceptional flood of January 2021, with a total rainfall three times greater than the average over the last years, which followed the rainy month of December 2020. Such a rare event occurs every ten years, last ones happened in 2011 and in 2000. Sentinel-2 multispectral images were used to produce Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) maps at a spatial resolution of 10 m. First results show a high spatial variability, with a rapid growth of the vegetation after the flood in the riverbed. Sentinel-3 radar altimetry data shows an increase of the backscattering coefficient due to to the flood, and last over time. Finally, Sentinel-1 data were used to study the radar backscattering amplitude over the study site and shows variations, specially in northern tributaries of the Kuiseb river. Finally, Sentinel-1 SAR phase data from interferograms will be used to track changes in soil moisture due to aquifer level dynamics, and then the riverbed drought over time.


References:
Benito, G., Rohde, R., Seely, M., Külls, C., Dahan, O., Enzel, Y., ... & Roberts, C, 2010. Management of alluvial aquifers in two southern African ephemeral rivers: implications for IWRM. Water Resources Management, 24(4), 641-667.
Jacobson, P. J., & Jacobson, K. M., 2013. Hydrologic controls of physical and ecological processes in Namib Desert ephemeral rivers: Implications for conservation and management. Journal of Arid Environments, 93, 80-93.
Benito, G., Thorndycraft, V.R., Rico, M.T., Sanchez-Moya, Y., Sopena, A., Botero, B.A., Machado, M.J., Davis, M., Perez-Gonzalez, A., 2011. Hydrological response of a dryland ephemeral river to southern African climatic variability during the last millennium. Quat. Res. 75, 471e482.
Grodek, T., Enzel, Y., Benito, G., Porat, N., Jacoby, Y., Dahan, O., ... & Seely, M., 2007. The contribution of paleoflood hydrology to flood recharge estimations along hyperarid channels, Kuiseb River, Namibia. In XVII INQUA congress, Cairns, Australia.

1. Introduction
Nowadays, the Copernicus Sentinel missions together provide a combination of high-resolution observation capabilities in different bands that few years ago were not yet available. If used in a coordinated way, their data can upgrade the scale and spatial and temporal resolution of investigations of small-sized volcanoes, whose eruptions can be violent and potentially hazardous for local communities.
With its peculiar type of activity and limited extent, the Stromboli volcano in southern Italy offers an ideal test case to demonstrate that the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (Sentinel-5P) satellite has the suitable spatial resolution and sensitivity to carry out local-scale sulfur dioxide (SO2) monitoring of relatively small-size, nearly point-wise volcanic sources and – in combination with Sentinel-2 multispectral observations of the situation on the ground – distinguish periods of different activity intensity.

2. Data and methods
The main input dataset of this study is the whole record of Sentinel-5P TROPOMI Level 2 SO2 geophysical products from UV sensor data acquired over Stromboli, since the beginning of the mission routine operations, after its 6-month-long commissioning phase. In particular, the TROPOMI time series for Stromboli starts on 6 May 2018, and the analyzed data cover three full years of observations, until 31 May 2021.
The latter were processed with purposely adapted Python scripts, according to a methodological workflow encompassing the extraction of total SO2 Vertical Column Density (VCD) at given coordinates (including conditional VCD for three different hypothetical peaks at 0–1, 7 and 15 km), as well as filtering by quality in compliance with the Sentinel-5P Validation Team’s recommendations. The total SO2 VCD time series for the main crater and across different averaging windows (3 x 3, 5 x 5 and 4 x 2) were compared to prove the correctness of the adopted spatial sampling criterion. As described in detail in Cofano et al. (2021), an approach for detecting SO2 VCD peaks at the volcano was trialed, and the detections were compared with the level of SO2 flux measured at ground-based instrumentation. To this scope, bulletin data were obtained from the FLux Automatic MEasurements (FLAME) and ROCcette site (ROC) Stations (Delle Donne et al., 2017; Randazzo et al., 2005). These records permitted to distinguish periods of intense activity from periods with low emissions and tremors, and were used to make a qualitative comparison between the quantity of SO2 emitted on the ground and that observed from satellite.
SO2 time series analysis was then complemented with information provided by contextual Sentinel-2 multispectral (in the visible, near and short-wave infrared) and Suomi NPP VIIRS observations. The aim was to correctly interpret SO2 total VCD peaks when they either (i) coincide with medium to very high SO2 emissions as measured in situ and known from volcanological observatory bulletins, or (ii) occur outside periods of significant emissions despite signs of activity visible in Sentinel-2 data. Finally, SO2 VCD peaks in the time series were further investigated through daily time lapses during the paroxysms in July–August 2019, major explosions in August 2020 and a more recent period of activity in May 2021. Hourly wind records from ECMWF Reanalysis v5 (ERA5) data and World Meteorological Organization (WMO) stations were used to identify local wind direction and SO2 plume drift during the time lapses.

3. Results and discussion
The SO2 time series after Quality Assurance and further filtering includes 589 observations, thus providing robust and sufficiently continuous records of volcano degassing at Stromboli.
Our spatial sampling tests suggest that in the case of volcanoes of “limited” areal extension, i.e., comparable to the pixel size of TROPOMI data, such as Stromboli, it is generally suitable to sample the single pixel and consider its value with respect to that found for larger averaging windows.
The time series of total SO2 column density for the hypothetical profile at the 0–1 km peak altitude was the profile used for reference for weak eruptions and degassing, as in those cases the bulk of the SO2 emitted by Stromboli is expected to be within the first kilometer of the atmosphere (i.e., ground-level plume). During major explosions and paroxysms, higher plume altitudes should be accounted for (e.g. ~9 km observed after the main explosion on 3 July 2019). On days of moderate to very high activity at the volcano, the data showed very clearly a series of significant total VCD at the main crater.
We also found that SO2 total VCD peaks as captured by TROPOMI data can serve as reliable proxies of volcanic activity and, as such, can be used to investigate other volcanoes when information about events is scarce or absent. However, it is not always trivial to associate SO2 total VCD peaks with moderate to very high SO2 emissions recorded by ground-sensor data, and reported in volcanological observatories bulletins. In this regard, the inspection of contextual Sentinel-2 multispectral observations in the visible, near and short-wave infrared (as well as Suomi NPP VIIRS data) provides an effective means to refine the interpretation of SO2 VCD peaks when they occur outside known periods of significant emissions. Although some limitations may occasionally constrain the analysis (i.e., cloud coverage that may hinder the visibility; time lag between the acquisition date of Sentinel-2 vs. TROPOMI data), this multi-sensor data approach adheres to the holistic concept of multi-band and multi-mission observations that are behind the whole Copernicus Earth Observation Programme.
Finally, the multi-temporal analysis of daily time lapses of SO2 VCD during the paroxysms that occurred in July–August 2019, major explosions in August 2020 and a more recent period of activity in May 2021 demonstrated that the proposed approach was successful in showing the SO2 degassing associated with these events, and warning whenever the total SO2 column density values at Stromboli may be overestimated due to clustering with the plume of the Mount Etna volcano. This geographical parameter, alongside wind direction, has to be accounted for whenever the nearly point-wise volcanic source under investigation is located in proximity to other SO2 emission sources, either natural (e.g., Mount Etna) or anthropogenic, that may interfere.

4. Conclusions
In the current context of an increasing body of literature on the use of Sentinel-5P data for volcanic studies, our presentation aims to deepen the discussion on the practical technical issues involved in the handling and post-processing of these geophysical data that are yet to become of common and standard use across the scientific community interested in volcanological applications, and how they can be used in a synergistic way with Sentinel-2 multispectral imagery to investigate eruptions, paroxysms and other events of potential concern for safety, at small-size Strombolian volcanoes. Based on the evidence gathered at Stromboli from 2018 to 2021, we propose practical recommendations for further implementation in similar volcanic environments. The proposed analysis approach is successful in showing the SO2 degassing associated with these events, and warning whenever the SO2 VCD at Stromboli may be overestimated due to clustering with the plume of the Mount Etna volcano.


References

Cofano, A., Cigna, F., Amato, L. S., de Cumis, M. S., & Tapete, D. (2021). Exploiting Sentinel-5P TROPOMI and Ground Sensor Data for the Detection of Volcanic SO2 Plumes and Activity in 2018–2021 at Stromboli, Italy. Sensors 2021, Vol. 21, Page 6991, 21(21), 6991. https://doi.org/10.3390/S21216991
Delle Donne, D., Tamburello, G., Aiuppa, A., Bitetto, M., Lacanna, G., D’Aleo, R., & Ripepe, M. (2017). Exploring the explosive-effusive transition using permanent ultraviolet cameras. Journal of Geophysical Research: Solid Earth, 122(6), 4377–4394. https://doi.org/10.1002/2017JB014027
Randazzo, D. A., Caltabiano, T., Salerno, G. G., Murè, F., Bruno, N., Longo, V., Spina, A. la, & Burton, M. R. (2005). Rapporto sullo sviluppo delle reti FLAME Etna e Stromboli, per la misura del flusso SO2, durante il periodo 2005 – 2009. https://www.earth-prints.org/handle/2122/5509

Air Quality (AQ) in Belgium is driven by complex processes covering a wide range of temporal and spatial scales, from point-like emissions of primary pollutants to intercontinental transport over an area of highly inhomogeneous land use. Therefore, the responsibility for AQ regulations is distributed among different levels of public authority, from international and federal to regional and local. Informed policymaking in terms of air quality regulation and sustainable energy consumption requires tailored monitoring of air quality and of the impact of past and future regulations taken.

AQ monitoring in Belgium has hitherto been relying mostly on in-situ measurements of surface concentration (mainly NOx and PM2.5), with geographical gaps between observations filled in with numerical modelling. However, today Belgium is mapped on a daily (or better) basis at unprecedented resolution by the new Copernicus Sentinel series of satellite sounders on sun-synchronous Low Earth Orbits (LEO), and soon also from a geostationary vantage point (GEO). Of particular interest are the observations of molecular atmospheric pollutants (NO2, CO, and CH4) by the Sentinel-5 Precursor (S5P, LEO) and soon by the Sentinel-4 (GEO) and Sentinel-5 (LEO) series, of particulate atmospheric matter (PM, observed as aerosol optical depth, AOD) by S5P and Sentinel-3, and of proxies for pollution sources such as fire radiative power (FRP) from Sentinel-3 and the land cover classification that can be obtained from Sentinel-2 (S2GLC).

We present here synergistic applications of these Sentinel data to monitor AQ in Belgium, developed in the framework of the Belgian federal research project LEGO-BEL-AQ (2020-2023, https://lego-bel-aq.aeronomie.be) funded by BELSPO. First, 1km-resolution (downscaled) maps of tropospheric NO2 (from the operational S5P and upcoming S4) are generated under various aggregation criteria. Their consistency but also particularities with respect to in-situ surface NOx measurements (i.e., the primary proxies for AQ) are assessed. To arrive at a more complete picture of Belgian AQ, in terms of atmospheric composition and attribution to potential sources, these results are cross-correlated with the AOD (particulate matter proxies), fire (FRP) and land cover (S2GLC) data sets. Case studies are elaborated on the capital (Brussels), key harbours (Antwerp and Ghent), industrial sites near Liège, and a wildfire (Groot Schietveld). As such, this work demonstrates the potential role of the Sentinel constellation in AQ monitoring and in supporting AQ policies, and its complementarity to the well-established surface monitoring programmes.

The 2017 launch of the Sentinel-5p mission marked the start of a new era of air quality earth observation using UV/VIS hyperspectral observations. The unprecedented spatial resolution of Sentinel-5p combined with its improved detectors and instrument calibration resulted in a single-satellite-pixel quality that allows for day-to-day monitoring several tropospheric trace gas and aerosol plumes.

Since then, many publications have explored this potential and have shown that Sentinel-5p indeed can be used to observe fine scale details of emission source. The main focus has been on anthropogenic emissions associated with air quality and methane in urbanized regions and affluence countries and the global impact of wide-spread massive wildfires that have occurred during the past few years.

The use of Sentinel-5p data for studying the aspects of much smaller and/or seasonal small scale localize fires and characterization of their emissions in relation to fire information derived from other Sentinel missions has not drawn much attention yet.

Here, we explore the capacity of Sentinel-5p for monitoring small scale localized fires in conjunction with fire information derived from the Sentinel-1, -2, and –3 missions. Such fires are often associated with agricultural activity as well as deforestation activities in subtropical and tropical regions. The emissions of these fires have an important contribution to the global carbon budget, but the magnitude of their emissions – and thus associated air quality – very much depends on fire characteristics such as fuel load, vegetation type, and soil moisture. How these fire characteristics and fuel characteristics interact and how emissions of trace gas and aerosols as well as the partitioning between emissions of various trace gases and aerosol types vary as a function of underlying fuel and soil characteristics is largely unexplored from an observational point of view.

We show that Sentinel-5p emissions can be used to build a large and data rich small scale localized fire-characteristics database that can be used for extensive and elaborate statistical analysis of their fire characteristics and dependencies. Understanding and quantifying these relations will further help bridge the gap between observations and model simulations. The latter often still are of insufficient spatial resolution to resolve fires at the spatial scales where they occur, nor are they capable of capturing the large variability of fire types and emissions and their dependency on fuel and soil characteristics.

Recent advances in satellite-based monitoring methods have enabled the detection and quantification of methane (CH4) point emissions. In particular, the combination of TROPOMI regional-scale data with high spatial resolution data from hyperspectral (e.g. PRISMA, high detection sensitivity with 30m pixel resolution) and multispectral (e.g. Sentinel-2, high temporal resolution and global coverage with 20m pixel resolution) optical missions creates an ideal framework to detect, quantify and monitor individual CH4 emissions of highly emitting point sources.
In this contribution, we will illustrate the use of satellite synergy to analyse the emissions from two important hot spots of industrial methane emissions. These two areas are the west coast of Turkmenistan and Algeria's O&G fields.
In Turkmenistan, we have found, during January 2017-November 2020, 29 individual CH4 sources that are directly linked to the O&G sector, of which 86% had been identified as inactive flares that vent gas. In the study period, the Sentinel-2 satellite has detected a total of 944 CH4 plumes with fluxes >1800kg/h, and the ZY1 and PRISMA hyperspectral satellites have quantified 25 plumes with emissions ranging from 1.400 ± 400 kg/h to 19.600 ± 8.100 kg/h. Moreover, the emission monitoring had shown a substantial increase in the number of methane plume detections in 2020 concerning previous years. Furthermore, according to VIIRS data, the use of flaring in the country has been decreasing significantly in recent years. This suggests a causal relationship between the decrease in flaring and the increase in venting. On the other hand, the historical Landsat record shows that gas vent emissions in this region have been occurring sporadically since at least the late 80' s.
Based on the Turkmenistan experience, we have extended the methodology to Algeria, a country with a large O&G extraction activity and favourable surface characteristics for satellite emission detection. To define the emitter search area, we have focused first on locations where TROPOMI detects (or has ever detected) super-emissions, and second on fields where, according to VIIRS data, flaring installations show anomalous or intermittent behaviour (flaring intensity peaks and troughs or periods of inactivity). Using the historical Landsat record, we have made a temporal tracking of the emission sources in order to better understand the problem behind Algeria's emissions.
These studies show that the synergy between the different satellites has been able to precisely reveal the origin of large anthropogenic CH4 emitters, which have the potential to be rapidly fixed, and monitored over time. They have also shown the outstanding and strong emissions from the O&G sector, which have remained unnoticed for decades.

This summarises the findings from the 4D Deep Dynamic Earth Science Meeting on 28-29th of September 2021 (https://www.3dearth.uni-kiel.de/en/4d-deep-earth-science-meeting). To understand the dynamics of the Earth a complete picture of the whole mantle is necessary. The 3D Earth project in ESA’s Support to Science Element showed successful possibilities of a joint study across multiple disciplines toward the construction of a thermochemical model of the upper 400 km of the Earth. For this, satellite data (GOCE and Swarm) significantly improved models from seismology. A next phase is envisioned, in which whole 4D Earth models are developed to understand the link between the deep Earth and processes at its surface.

Most of Earth’s upper mantle structure has already been properly characterised by the 3D Earth project. However, both the lower mantle and upper mantle contribute to the longest wavelengths of the gravity field, which is also affected by the dynamic deformation of density discontinuities (Earth’s surface and Core Mantle Boundary-CMB). Therefore, separating the gravity contributions of from the upper and lower mantle, as commonly done, as well as explaining the topography of the Earth’s density boundaries requires a better understanding of the lower mantle structure and dynamics and its interaction of the upper mantle model. New studies using fundamental and higher mode surface waves are needed to constrain the large-scale and fine-scale structure of the lithosphere, asthenosphere, mantle transition zone and shallow lower mantle. Integrated petrological forward and inverse modelling can be an effective means of quantitatively combining the satellite data with the different types of seismic data.

The lower mantle contains huge structures or provinces featuring low seismic velocities (LLSVP), possibly related to core-mantle interactions, but also seem to play a role in global mantle flow. Can we improve the characterisation of the LLSVP and core-mantle boundary (CMB) structures, noting the placement of paleo-slabs in the lowermost mantle, by combining different datasets (gravity and various seismic constraints)? For example, normal modes (and perhaps body wave sampling of the CMB) can give us the large-scale structure of the deep lower mantle and the core. Normal mode splitting and body wave reflection techniques can provide constraints on anisotropy (important for understanding mantle flow patterns). Furthermore, can we constrain the lower mantle electrical conductivity of these structures by combining satellite magnetic and gravity data with global seismology plus mineral/high pressure physics? Large uncertainty is present about these LLSVPs, for example are the African and Pacific LLSVPs similar in terms of their composition and dynamics?

To really understand the deeper Earth, we need to study the time-dependent (4D) nature of the structures that we detect with seismology, gravity, and magnetics. Understanding the dynamics of the system will enable us to better characterise these structures and will also allow us to utilise time-dependent observations from the geological record, placing constraints on the mantle viscosity structure. 3D viscosity characterisation should be a major output of a future 4D dynamic Earth study. Currently, we cannot consistently link dynamic topography models (which include whole mantle density and viscosity, integrated in mantle flow modelling) with residual isostatic topography estimates (inferred from lithospheric structure based on satellite and terrestrial data, e.g. 3D EARTH). It is still unclear how observations of the present-day Earth are useful for understanding 4D Deep Earth processes that are manifested at Earth’s surface. For instance, subduction-driven flow is a first order feature that must be included in a whole Earth model. In addition, other dynamic solid Earth processes, like sea-level change, glacial isostatic adjustment, polar wander, and surface deformations in general are controlled by the viscosity distribution of the Earth. A new multi-disciplinary effort is needed to consistently couple a comprehensive whole Earth model with present-day dynamic surface processes.

Along these lines a new initiative was suggested as outcome of a Science Meeting held in September 2021. A future project extending 3D Earth outcomes should be split into two main phases. I) A first explorative phase focused on the assessment of the sensitivity of the different datasets: surface wave seismology, normal modes, satellite gravity, and satellite magnetic field observations for probing the solid Earth; ii) A second phase focused on generating a complete and consistent mantle model, in parallel with the geodynamic model, CMB bottom-up probing, and surface processes studies. A feedback loop is advised between the three application studies and the overall structure study. These studies could run in parallel but should have close cooperation and timely interactions like those implemented within ESA's 3D Earth project. The end goal of the initiative would be to assimilate global terrestrial and satellite geophysical and geodetical data to construct a whole-Earth model that is consistent and able to model the major dynamical processes of Earth.

In this contribution, we present a global study of the crustal structure with emphasis to cratons. In an inverse scheme, satellite gravity gradient data of the GOCE mission are inverted for the Moho depth, exploiting laterally variable density contrasts based on seismic tomography. Our results are constrained by an active source seismic data base, as well as a tectonic regionalization map, derived from seismic tomography. For the global analysis, we implement a moving window approach to perform the gravity inversion, followed by interpolating the estimated density contrasts of common tectonic units with a flood-fill algorithm.
The estimated Moho depth and density contrasts are especially interesting for the cratons of the Earth. Our results reveal a surprising variability of patterns with average Moho depth between 32-42 km, reflecting an individual tectonic history of each craton. Statistical patterns of Moho depth and density contrasts are discussed for the individual cratons and linked to their stabilization age. For example, Australia shows the lowest average Moho depth (32.7 km), indicating early stabilization in the Archean and removal of a dense lower protocrust. This observation matches well with receiver function studies. The globally inverted Moho depth is validated by gridded seismic Moho depth information, which shows that for many cratons the inverted Moho is within expected uncertainties of the seismic Moho. Especially in remote areas, which pertains to many of the African and South American Cratons, the Moho depth can now be treated with a higher confidence than previously assumed. Moreover, the formerly connected cratons of South America and Africa are analyzed and discussed in a Gondwana reconstruction. Here, the once-connected West African and Amazonian Cratons have a shallow Moho depth, indicating that only little tectonic activity occurred during the Phanerozoic. The tectonically-linked Congo and Sao Francisco Cratons have intermediate Moho depths, with the Congo Craton having a slightly shallower Moho depth. This could reflect dynamic support of the upper mantle on the African side.

Uncertainties in GIA estimates from forward models contribute significantly to the uncertainties in ice-sheet mass balance and sea level budget studies. These models rely on a past ice loading history and a lower mantle viscosity structure, both of which are, and will remain in the future, poorly constrained. In addition, models that can address lateral variations in mantle viscosity (i.e. 3-D) are in their infancy. An alternative approach is to seek a data driven solution to GIA, with the aid of vertical land movement (VLM) estimates from GPS and gravity data from GRACE. The challenges of obtaining a data-driven GIA estimate include solving a geophysical inversion that does not have a unique solution and often requires a-prior information and several approximations to achieve. For example, VLM is not only due to GIA and present day mass loading (PDML) but also to local processes including tectonics and subsidence, while GRACE measures some, generally, unknown combination of signals due to GIA and PDML.

In this work, a novel geophysical framework is developed to utilise GPS and GRACE data to solve for GIA. Our method relies on geophysical relations between geopotential and vertical land movement (VLM) caused by GIA and PDML. For example, the elastic response of the solid Earth to a positive PDML results in a negative VLM, while a positive GIA mass signal in GRACE leads to a positive VLM. We use these relations to express GPS observed VLM and GRACE observed gravity field anomalies, expressed as VLM, in terms of the sum of GIA and PDML. The method is first tested in a closed-loop synthetic experiment where we impose a uniform synthetic GPS coverage globally. It is then applied to the Nevada Geophysical Lab database of GPS VLM and GRACE spherical harmonic coefficients provided by ITG Graz. Here, we define GIA as any viscous response of the mantle to past loading, irrespective of when that took place. The resulting GIA estimate differs significantly from commonly used GIA models (such as ICE-6G) over, in particular, Alaska and central Greenland. Since the forward models do not consider viscous deformation after the end of the Little Ice Age, they will underestimate this term over Alaska and potentially in Greenland where there is evidence for a monotonic decline in volume since 1900 (Kjeldsen, Korsgaard et al. 2015) although the mantle response is expected to be slower than for Alaska. We also discuss the uncertainties, caveats and limitations of our method and its implications. The GIA product is publicly available at one degree grid resolution.



Kjeldsen, K. K., N. J. Korsgaard, A. A. Bjork, S. A. Khan, J. E. Box, S. Funder, N. K. Larsen, J. L. Bamber, W. Colgan, M. van den Broeke, M. L. Siggaard-Andersen, C. Nuth, A. Schomacker, C. S. Andresen, E. Willerslev and K. H. Kjaer (2015). "Spatial and temporal distribution of mass loss from the Greenland Ice Sheet since AD 1900." Nature 528(7582): 396.

The Earth's magnetic field displays variations on a broad range of time scales from years to hundreds of millions of years. The last two decades of global and continuous satellite geomagnetic field monitoring have considerably enriched the knowledge on the rapid physical processes taking place in the Earth’s outer core. Identification of axisymmetric torsional Alfvén waves with subdecadal periods from observatory and satellite data has given access to an averaged intensity of the magnetic field in the Earth's core interior. A significant part of the rapid signal, however, resides in non-axisymmetric motions. Their origin has remained elusive as previous studies of magnetohydrodynamic waves in the Earth's core mainly focused on their possible signature on centennial time scales. Here, we identify non-axisymmetric wavelike patterns in the equatorial region of the core surface from the observed geomagnetic variations. These wavelike features have large spatial scales, interannual periods in the vicinity of 7 years, amplitudes reaching 3 km/yr and coherent westward drift at phase speeds of about 1500 km/yr. We interpret and model these flows as the signature of Magneto-Coriolis (MC) modes.
Their identification offers a way to probe the magnetic field structure inside Earth’s core. It follows from our work that there is no need for a stratified layer at the top of the core to account for the rapid geomagnetic field changes.

Understanding the mechanisms leading to giant earthquakes and identifying when such events become imminent remains a challenge. Over the long term, seismic hazard assessment is based on the study of the recurrence intervals of earthquakes along active fault zones. Over the short term and thanks to satellite data, various deformation transients have been detected before large subduction events. Among them, we have evidenced in a previous study an anomalous gravity gradient signal during the months before the March 2011 Tohoku earthquake. We showed that it could reflect an episode of extension of the subducted slab prior to the event, generating the giant earthquake as the deformation propagated from depth to surface. These results stress the importance of the slab pull force in driving plate motions.

Here, we conduct a systematic retrospective analysis all over the globe of time series of GRACE-reconstructed gravity gradients truncated in February 2011, in order to test whether the gravity gradient variations before the March 2011 Tohoku earthquake can be identified in real-time as singular and potentially originating from the solid Earth. For that, we increase the angular resolution in our analysis of the gravity gradients and extract fast temporal signals of large amplitude, closely aligned with the orientation of the Northwestern Pacific subduction. Among all the sources of gravity variations, solid Earth signals can be recognized from a distinctive spatial pattern at the considered timescales, different from the fingerprints of water cycle sources and preferably common to different GRACE gravity solutions. For an automatic detection, we set-up a method to assess the coherency between two sets of gravity gradient signals, observed or predicted from geophysical models. It allows us to select robust signals in a chosen GRACE solution based on their coherency with the signals of another gravity solution, and test their sensitivity to modelled hydrological, atmospheric or oceanic sources. We present and discuss the results of these analyses applied to two sets of GRACE gravity models : the GRGS03 and the CSR06 solutions, as well as their respective ocean dealiasing models. Beyond the case of the Tohoku earhquake, this approach can be systematically applied to the monitoring of major plate boundaries, to identify in real-time gravity variations potentially linked with sudden changes in the deeper slab motions.

Thanks to the increasing availability of data, coming from various satellite missions, a great variety of high-resolution high-accuracy global gravity models have been developed within the last years thus paving the way to new studies ranging from the global to the local scale. It is known that gravity field data can be proficiently used to identify the shape of geological structures characterized by different densities and one of the principal applications regards the estimate of the boundaries of different crustal geological provinces, i.e. regions of the crystalline crust which can be considered homogenous from the mass density point of view (e.g. continental, oceanic or transitional crust, etc.). The classical approach for such kind of applications mainly consists in qualitative interpretation techniques that require an expert operator to supervise, guide and interpret the results. The common practice consists in fact in applying some filters to Bouguer gravity anomalies in order to remove from the data the effects of the shallowest sedimentary structures and of the mantle, respectively, thus isolating the signal due to density variations within the crust. At this point, on the reduced signal, a general visual matching with respect to a certain geological model, defined from the knowledge on the area is applied.
The principal issue of these techniques is the strong dependency of the results from the operator who performs the study, which can choose different filters that can highlight different geological features, also influencing the visual matching and final classification.
In this work we present a first application of an automatic interpretation algorithm, based on a Bayesian probabilistic approach, aimed at retrieving a map of geological crustal provinces in the Mediterranean Sea Area from gravity data. In particular, the proposed classification method requires the definition of a set of a-priori information, mainly derived from freely available geophysical data, that are used to reduce the initial observations and to prepare a rough model of the crustal provinces. Differently from the classical technique, within the proposed Bayesian classification algorithm we strip from the observed gravity the signals of main known geological horizons. Moreover, we replace gravity anomalies with second radial derivative of the gravitational potential, thus making the solution more robust with respect to deep density variations and finally, we exploit an automatic Bayesian probabilistic approach to classify the different geological crustal provinces. The result of this application is an enhanced map of the principal crustal provinces completed by an estimate of the predicted accuracy, as well as an estimate of the average density associable to each crustal province.
The proposed algorithm is applied in the Mediterranean Sea Area exploiting the global gravity field model XGM2019e, thus obtained a map of the main crustal domains. The obtained refined map presents adjustments in the boundaries with respect to the a-priori model of the order of 150-200 km, which can be an important source of information for the scientific community, allowing for a better definition of the different crustal domains. Moreover, the proposed approach, being based on freely available datasets and being a fully automatic procedure, can be in principle enlarged to study crustal provinces at continental and even global scale.

For over 20 years, the processing facility at VITO has been processing, archiving and disseminating satellite data derived from SPOT4-Vegetation1, SPOT5-Vegetation2 and PROBA-V in order to serve a large user community providing vegetation information products with nearly daily global coverage. Since these data are widely used to monitor environmental change and the evolution of vegetation cover at global scale, the continuity of the SPOT-Vegetation and PROBA-V time series is important for a wide range of users.

The Sentinel-3 synergy “VGT-like” (SYN VGT) products were defined to provide continuity to the SPOT-Vegetation and PROBA-V time series. In the frame of the Sentinel-3 Mission Performance Center (S3-MPC), the consistency between SYN VGT products and PROBA-V Level2 products is evaluated.

In subsequent processing baseline updates, important changes were implemented in the Sentinel-3 SYN VGT processing chain, including improvements to the temporal and spatial compositing routines. As the operational phase of PROBA-V has ended in June 2020, direct product comparison is no longer possible. Therefore, for June 2019, in the frame of the S3-MPC SYN VGT data was reprocessed based on the latest processing baseline.

Although of limited size, this SYN VGT reprocessed dataset provides important insights into the current level of consistency between PROBA-V and SYN VGT. The analyses focus on the calculation of measures of similarity (geometric mean regression parameters, R²) and measures of difference (accuracy, precision and uncertainty) between paired observations. As the analyses will be based on the PROBA-V Collection 2 that is currently being processed, the results will be presented at the symposium.

PROBA-V (Project for On-Board Autonomy – Vegetation) was launched on May 7th 2013, with the objective of providing global land-coverage data continuity for the SPOT (Système Pour l’Observation de la Terre)-Vegetation user community. After more than 6 years of operations, the operational phase of PROBA-V has ended on June 30, 2020.

The reprocessing of the entire PROBA-V archive at 1 km, 300 m and 100 m resolutions to Collection 2 is currently ongoing. Compared to Collection 1, the following changes are included: (1) updated radiometric calibration, (2) a new and better cloud detection method and improved cloud shadow detections, (3) an improved atmospheric correction, (4) harmonisation of the compositing among the resolutions, (5) update of the product format and (6) a new catalogue to distribute the data.

The updates in the radiometric Instrument Calibration Parameter (ICP) ) files include the dependence on date since launch, modeled by a 2nd degree polynomial, for trending of the absolute calibration coefficients for the different strips/bands. The model also corrects for the increasing trend observed in some bands. Secondly, improvements are made in the low frequency multi-angular coefficients (i.e. equalization) for the SWIR strips of the LEFT and RIGHT camera form start of mission based on yaw manoeuvre results. In addition, a correction of inter-camera bias in the Blue band is applied.

The newly designed algorithm for cloud detection in PROBA-V data introduces major changes w.r.t. the algorithm used in Collection 1. It uses a Multi-Layer Perceptron (MLP) neural network algorithm. The training and validation data have been gathered on a much larger scale compared to Collection 1, and final performance is greatly improved compared to both Collection 0 and Collection 1. The method also removed the dependency on auxiliary input data, which was a major issue in Collection 1. The cloud shadow detection was improved by removing the 1-pixel border between cloud and cloud shadow.

As in Collection 1, the new atmospheric correction is also based on the Simplified Model for Atmospheric Correction (SMAC). Compared to the previous collections, it also includes an assessment of uncertainties of the atmospheric correction and error propagation. It uses an external dataset, namely the MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, version 2) for the inputs of the atmospheric correction. A validation of the atmospheric correction was done based on the Atmospheric Correction Inter-Comparison Exercise (ACIX) approach. Validation of the atmospheric correction was done based on the ACIX approach. The results show that the TopOf-Canopy (TOC) reflectances are better characterized and that artefacts due to the image based Aerosol Optical Thickness (AOT) retrieval are removed.

The compositing method is harmonized between the different resolutions in Collection 2. Previously, for 100 m and 300 m the radiometric quality of all 4 bands were checked prior to compositing. Since the SWIR band has quite a number of defect detectors, this resulted in composites with a striping effect. For the 1 km, the SWIR radiometric quality was not checked in the compositing. This method is now applied to all resolutions.

For Collection 1, the entire archive is available in two product formats: HDF5 and GeoTIFF. For Collection 2, however, we follow the newly becoming standard which is Cloud Optimized GeoTIFF (https://www.cogeo.org/) instead of the standard GeoTIFF format. It is backwards compatible with current GeoTIFF format. The regular HDF5 format will keep on existing too. This way, the handling of files in cloud environments and visualization of the data gets easier in the future.
The PROBA-V C2 metadata will also receive a update to allow for minimum compliancy with the CEOS Analysis Ready Data for Land (CARD4L). This allows for immediate analysis and interoperability both through time and with other datasets.

The PROBA-V Collection 2 data will be ingested into a new catalogue client instead of the legacy Product Distribution Facility. The user interface will allow several functionalities, such as time series export, viewing capabilities, additional authentication options, etc.

The presentation will focus on the first four changes. Details of the adaptations, together with a summary of verification and validation results, will be presented.

The ESA SPAR@MEP project has been designed to deliver a consistent long-term data record (LTDR) of aerosol optical properties and surface reflectance from SPOT-VGT and PROBA-V observations. The project exploits the Mission Exploitation Platform (MEP) developed by VITO, to access and process the satellite data. The LTDR is obtained by processing satellite images with the CISAR algorithm, developed by Rayference.

The CISAR algorithm has been originally developed for a consistent retrieval of surface reflectance and aerosol single scattering properties. Given the large number of issues associated to clouds in traditional aerosol retrieval algorithms, CISAR has been extended to the retrieval of cloud single scattering properties as well, although the main focus remains the aerosols. Standard aerosol retrieval algorithms make use of a cloud mask to discriminate cloud-free atmosphere and only perform the inversion on these pixels. Within the SPAR@MEP project, the PROBA-V cloud mask is only used by CISAR to build a prior information on the aerosol or cloud optical thickness (AOT/COT), but both cloudy and cloud-free observations are processed.

One interesting advantage of this innovative approach for aerosol retrieval is the possibility of retrieving high aerosol load, that are often discarded by traditional algorithms due to erroneous classification of these pixels as cloudy. This feature will be illustrated during a dust storm moving from North Africa to Greece, flagged as cloud by the PROBA-V Collection 1 cloud mask, but correctly retrieved as high aerosol load by CISAR thanks to its innovative approach.

One year (2019) of PROBA-V data is processed at 1 km resolution over Europe. Such a high-resolution aerosol product provides the opportunity to appreciate in detail aerosol spatial features. This advantage will be illustrated over few case studies, such as during the wildfire season in Turkey and the ship trails in the Biscay region. The CISAR product will be evaluated against AERONET observations in terms of error and correlation over selected stations, and against MODIS MCD19A2 product to observe spatial and temporal cross-sensor consistency. The algorithm capability of discriminating between fine and coarse mode will be evaluated over the selected case studies. Processing both aerosols and clouds, the CISAR algorithm could represent a powerful tool to study aerosol-cloud interactions, such as the Twomey effect, from satellite images at high resolution.

As advances in technology make payloads and instruments for space missions more efficient and accessible, a need for the rapidly developed, low-cost missions on very small satellites emerge. Among that class of spacecraft, the Cubesats have distinguished themselves among several distinct missions. A Cubesat is a class of nanosatellites, built to standard dimensions – or Units, “U” - of 10cm x 10cm x 10cm.
The PROBA-V Companion Cubesat (PV-CC) mission aims at flying the spare Spectral Imager from PROBA-V on a tailored 12U Cubesat. One of the major challenges of this mission is to balance the performances of the payload with the limitations of the cubesat platform. Indeed, the downsizing from a microsatellite towards a 12U Cubesat implies some major impacts in terms of mechanical and thermal path from the environment to the payload is restricted. A lower mass also implies higher mechanical loads and reduced thermal inertia, with less radiative surfaces. Moreover, the altitude limitation for satellites without propulsion prevents from operating the mission at a similar altitude, impacting both GSD and acquisition rates.

This presentation is about the development status of the mission as well as the major design and mission drivers that arise from the mentioned points for the satellite hardware and the ground segment.

The Proba-V mission was designed to cover the gap between the Spot-VGT and ESA Sentinel-3 satellite missions and has exceeded all expectations, providing more than 7 years of data. Now that Proba-V has reached the end of its operations, the new PV-CC mission, to be launched in 2022, aims to extend the Proba-V mission objectives. The number of Earth observation satellites carrying optical sensors with similar characteristics is constantly growing, and these new modest-size space missions may complement institutional missions, such as the Copernicus Sentinels, to develop Earth observation products at unprecedented temporal and spatial resolutions. However, cross-mission applications require a level of data harmonization that is not easy to meet, since differences in the radiance/reflectance products usually hamper data sharing across sensors.

It is worth noting that the second goal of Proba-V was to demonstrate the feasibility to get the same observations as other much bigger satellites with a 1-cubic meter satellite and, on the other hand, that the main goal of PV-CC is to demonstrate that an even smaller 12U CubeSat platform can carry the same Proba-V vegetation instrument. Despite that, the performance of small cost-effective satellites is usually lower in terms of both calibration and processing algorithms. In this context, we expect that the synergistic exploitation of PV-CC in combination with Sentinel-2 might be beneficial for improving and validating the radiometric calibration and atmospheric correction of PV-CC. In particular, we propose a deep learning (DL) method that learns a general transformation model to convert PV-CC top of the atmosphere (TOA) images into S-2 bottom of atmosphere (BOA) images. Basically, it can be considered as a domain adaptation model that learns how to atmospherically correct PV-CC images by learning to reduce the statistical differences between PV-CC TOA images and S-2 TOC images.

Standard atmospheric correction (AC) of multispectral images usually involves the estimation of atmospheric transmittance at the sensor spectral bands using radiative transfer models and taking into account the atmospheric conditions at the time of image acquisition. Therefore, atmospheric correction is a complex, difficult and ill-posed problem that we will approach following a two-stage process. In the first stage, we will develop a data-driven atmospheric correction model using Sentinel-2 MSI data only. This first DL-AC model will be trained using Sentinel-2 Level-1C TOA images as inputs and Sentinel-2 Level-2A BOA images, obtained with Sen2Cor, as reference outputs. It means that we are developing a machine-learning-based emulator of the Sen2Cor processor using the four spectral bands in common with PV-CC, which will be used as a pretrained model for the second stage. In the second stage, we will fine tune the DL-AC model to convert PV-CC TOA images into S-2 BOA images. At this stage, we will use Proba-V data as the best proxy for PV-CC, which presents different but compatible spatio-spectral characteristics. Training these deep learning models requires a comprehensive dataset of TOA/BOA images, so we will create a geographically diverse database for the whole 2019 year covering all seasons and biomes.

Acknowledgements: This work was supported by the Spanish Ministry of Science and Innovation (PID2019-109026RB-I00) and by the European Space Agency (ESA IDEAS grant).

Developed by prime contractor Aerospacelab in Belgium for ESA, PROBA-V Cubesat Companion (PV-CC) In-Orbit Demonstration (IOD) mission is a small low-cost 12-unit CubeSat, satellite built up from standardised 10-cm boxes. It will fly a cut-down version of the vegetation-monitoring instrument aboard the Earth-observing PROBA-V to perform experimental observations. This IOD mission will be used to explore the harmonisation and synergistic exploitation of datasets captured by different platforms, in a different orbit, but with the same sensor. Additionally, it aims at developing and testing innovative Calibration/Validation (Cal/Val) strategies for small satellites. The mission is supported through the Fly in-orbit testing element of ESA’s General Support Technology Programme.

PV-CC will be flying at 550km in a Sun-synchronous orbit providing a similar Earth observation dataset as PROBA-V, but with an improved spatial resolution (65m at nadir). VITO will be in charge of the development and the hosting of the data processing facility and the Cal/Val chain. As PV-CC is carrying the same Vegetation imager, the PV-CC data processing and calibration will largely reuse the existing PROBA-V ground segment infrastructure. Processing adaptation and fine-tuning are however needed to accommodate for the PV-CC specifications and differences.

Considering the smaller size of the PV-CC platform, temperature effects might be more significant for PV-CC causing more scattering in the in-flight radiometric calibration result. Additionally, PV-CC mission will be using Cubesat platform, which is known to be less accurate in terms of geo-pointing performance and thus reducing the on-ground geolocation accuracy. To overcome all these challenges, a Cal/Val plan has been prepared by VITO, where adaptation to the existing PROBA-V Cal/Val methods and tools were detailed.

In this paper, the overall PV-CC Payload Data Ground Segment (PDGS) development status and milestones will be detailed and the proposed Cal/Val strategy presented. The latter will rely on the experience gained with the PROBA-V mission with respect to performance and applicability of the different vicarious Cal/Val methods. It considers furthermore the specific characteristics of this Cubesat Companion mission such as the longer revisit time at nadir (approximately 12 days) and the possible larger sensitivity to thermal environment changes.

The timings of key physiological events in the Earth’s terrestrial biosphere are crucial to controlling the relationship of the carbon, energy and water cycles between the land surface and the atmosphere. By understanding the timings and spatial distributions of different phenological events such as the start of the growing season (SOS), length of the growing season (LOS) and the end of the growing season (EOS), the impacts of climate change can be better understood and assessed. Previously, studies have shown temperature to be a driving factor behind changes in northern latitude phenology, whilst precipitation drives tropical regions. As global temperatures continue to increase, this is expected to lead to a growth in vegetation productivity in the Northern Hemisphere due to an earlier SOS and an increased LOS.
Time series of vegetation indices derived from satellite imagery have been used to monitor changes in the phenological development of terrestrial vegetation over the past four decades at a number of spatial scales. Indices such as the normalized difference vegetation index (NDVI), have a strong correlation with green vegetation biomass, but lack physical meaning, suffer from sensitivity to non-canopy factors, and saturate at higher values.
The Global Climate Observing System (GCOS) defined the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) as one of the fifty Essential Climate Variables (ECVs), as it is a key biological quantity that contributes to the characterization of Earth’s climate. Using an ECV to derive and monitor the phenology of terrestrial vegetation has distinct advantages over vegetation indices, since FAPAR is a bio-physical property of vegetation describing the presence and state of the photosynthetic activity. As such, we processed FAPAR products from Envisat MERIS and Copernicus Sentinel-3 OLCI covering the period 2002 – 2020 to obtain phenological metrics.
The main objectives are to:
(1) Assess the feasibility of using satellite derived FAPAR time series for deriving phenology over the Northern Hemisphere;
(2) Discuss the spatiotemporal patterns and changes in three phenological metrics: SOS, LOS and EOS,
(3) Validate the performance of the satellite products using in-situ FAPAR measurements obtained from the national ecological observatory network.

The European Environment Agency (EEA) recently presented the high-resolution vegetation phenology and productivity (HR-VPP) product group as part of the Copernicus Land Monitoring Service. These datasets serve the European Union with 10-day seasonal data for monitoring of terrestrial ecosystem dynamics, and seasonal parameters describing key phenological stages of vegetation, such as onset, end, length, amplitude, productivity, and several others. The seasonal data are derived from from 10-m Sentinel-2 observations and the novel plant phenology index (PPI), which responds to the photosynthetic production of leaf biomass. To tackle cloudiness, an improved cloud screening algorithm was implemented. Furthermore, A new time-series processing sequence was developed based on the TIMESAT software system, to generate gap-filled and smoothed seasonal trajectories, phenological parameters, and quality assurrance layers. HR-VPP also includes near real-time observations of fAPAR, LAI and NDVI.

HR-VPP presents spatial and temporal data of vegetation productivity, providing opportunities for monitoring crop yield variations, forest growth, and ecosystem productivity and health. It has also been shown to indicate vegetation responses to drought and other climate disturbances. To ensure accuracy of the final products our team tested a range of methodological approaches and datasets, and validated the data against a broad range of reference data, including PhenoCam imagery, GPP estimated from ICOS eddy-covariance data, ground-observed phenological observations of the PEP725 network, and other ancillary data. An independent validation against ground references and other satellite-based phenology products was carried out following CEOS/LPV best practices.

The HR-VPP product group provides free and user-ready continental data at the highest spatial and temporal resolution presently available for detailed assessment of local vegetation conditions across the European continent. The production currently covers the years 2017 – 2021. Outcomes are relevant to EU and international policies such as the European Green Deal Biodiversity Strategy 2030, the EU Soil Strategy, the EU Climate Adaptation Strategy and the LULUCF regulation. In an era of rapid global change it will provide an invaluable data source for innovations and science to understand and mitigate future climate and human induced vegetation changes.

High-quality estimation of land surface phenology (LSP) is becoming crucial for understanding the impacts of climate change on the ecosystem functioning. Reliable extraction of photosynthetic phenology using remotely sensed vegetation indices is important for quantifying the seasonal cycle of atmospheric CO₂ concentrations and modelling the responses of vegetation to climatic warming. However, there are still uncertainties about time series pre-processing, the accuracy and robustness of LSP methods, the impact of spatial resolution, and the interpretability of LSP metrics. We present a line of research that demonstrates the relevance of reducing the uncertainties when studying vegetation phenology responses to climatic warming.

We firstly analysed four state-of-the-art phenology methods and different time series smoothing techniques. These methods were applied to the Copernicus Global Land Service (CGLS) SPOT-VEGETATION and PROBA-V leaf area index (LAI) 1 km V2.0 time series. The resulting LSP metrics were validated with near-surface PhenoCam, eddy covariance FLUXNET data, and in situ observations of phenophases (PEP725 and USA-NPN). We found that the threshold-based method performed the best in terms of root mean square error for the start of season (30% threshold of the annual amplitude) and the end of season (40% threshold).

Based on these findings, we developed a novel threshold-based technique named maximum separation (MS), which we implemented on a cloud-based platform (Google Earth Engine). The main advantage of the MS method is that it can be directly applied to daily non-smoothed time series without any additional pre-processing steps, reducing the uncertainty associated with excessive smoothing. The implementation of the proposed method in GEE allowed for the quick processing of phenological maps produced from MODIS and Sentinel-2 at the continental scale. Such maps highlighted the necessity of decametric resolution satellite data for proper monitoring of vegetation phenology at the canopy scale in heterogeneous landscapes.

We also examined the relevance of selecting the proper spectral indices for accurate tracking forest photosynthetic phenology. We found that structural vegetation indices (NDVI and EVI) were suited for estimating the start of the photosynthetically active season in deciduous broadleaf forests, but a physiological vegetation index (chlorophyll/carotenoid index) was better for predicting the end of the photosynthetically active season in deciduous broadleaf forests and both the start and end of the season in evergreen needleleaf forests. The divergent performances were rooted in the combined control of structural and physiological regulations of carbon uptake by plants. We propose revisiting the dynamics of photosynthetic phenology using physiological vegetation indices, which has substantial implications for global plant phenology and carbon uptake investigations.

Finally, we proposed a machine learning framework for modelling phenology metrics as a function of temperature, water availability, and radiation at the global scale. Machine learning captures non-linear and non-parametric relationships between different factors that control leaf unfolding, outperforming standard processed-based and statistical models. Such framework enables the inclusion of multiple input climate variables, defined in experimental and field studies, and ranking the relevance of factors that control leaf unfolding. These results also highlight the importance of variable and LSP method selection for effectively monitoring the responses of vegetation to climatic factors.

The timing of crop development is an important information for crop modelling and crop yield forecasting. Indeed, most crops experience significant lags in space (e.g. from one country to another) and in time (from one year to another) as a function of varying agrometeorological conditions and agricultural practices. However, only a few countries monitor crop phenological stages at large scale with regular and harmonized reporting. In France, the FranceAgriMer institute publishes the weekly Céré'Obs bulletin throughout the whole agricultural season: a report of the crop development stages from sowing to harvest based on in situ observations by experts. Reported data includes estimates of the percentage of parcels for each French NUTS-3 region that have reached a given development stage. In France, the bulletin covers five crops, i.e. winter soft wheat, winter barley, durum wheat, spring barley, and grain maize. Depending on the crop, six to seven crop management and physiological stages are considered (e.g. for winter cereals: sowing, emergence, beginning of tillering, beginning of stem elongation, node 2, inflorescence, harvest). The European Union Copernicus program and the launch of the Sentinel constellations are offering a unique source of data with the capacity to perform large-scale crop monitoring at parcel level. Many authors have shown the potential of Sentinel-2 data to construct time series and to analyse their variations in space and in time. Most studies have not specifically focused their analysis on cropland. They have developed generic phenometrics, describing basic phenological stages, such as the start and the end of the season, which are rarely connected to physiological crop development stages. In this study, we propose to design and assess an automatic crop stages identification model based on Sentinel-2 time series that is calibrated with the Céré'Obs expert observations. The use of Sentinel-2 is constrained by a mandatory a-priori knowledge of the crop type in order to extract crop-specific Sentinel-2 time series. This is possible in France, using the Geospatial Aid applications (GSAA) data, publically available for past seasons.
The proposed model is based on phenology-driving variables (PDV) on which thresholds are defined to identify crop stages at parcel level. We assessed four PDV as input of the model:
a) Growing degree days (GDD) derived from weather data, not involving the Sentinel-2 data
b) Leaf area index (LAI) derived from Sentinel-2 time series
c) Green vegetation ratio, computed from the ratio of LAI as local (parcel) minimum and maximum of the LAI time series
d) Percentage of CSDM (calibrated on LAI time series) progress
Among the four PDVs, only (a) has the benefit to not depend on any a-priori knowledge, therefore can be run easily in near real time. The same applies to PVS (b), except that crop types need to be identified to extract crop specific LAI along the season. PVSs (c) and (d) require modelling of nearly the full LAI time series. The thresholds on PDV are calibrated by optimizing the matching between the distributions of the estimated stages for all parcels in a given NUTS-3 region and the distributions as provided by Céré'Obs. Kolmogorov–Smirnov test is used for the distribution matching.
For this study, we focused on soft winter wheat and grain maize over all NUTS-3 regions of France covered by the Céré'Obs report. We analysed six cropping seasons from 2016 to 2021. The Sentinel-2-based smooth time series of LAI were derived from the in-house processing of Sentinel-2 Level-2 data, involving a biophysical variable processor, aggregation at parcel level (using LPIS/GSAA data), time series outlier detection, and the Whittaker time series smoother.

Seasonal crop phenology allows understanding a crop’s metabolic cycle, its reactions to environmental influences, and its buildup of biomass, among others. Crop phenology is hence a valuable input for numerous agricultural monitoring tasks, including the assessment of climate impacts, management practices, and yield estimation. As area-wide and field-based collection of related data is not feasible, it is of great interest for the EO research community to leverage the potential of remote sensing data for deriving phenological information on cultivated crops.
Studies so far usually had reference data available for limited regions. This is sufficient for the validation of phenology estimations on fine scales. However, this leads to an insufficient error assessment when area-wide estimations are made. Thus, approaches developed for broad-scale applications often lack precise validation by using reference data or prediction units beyond the plot level (e.g, Meroni et al., 2021). Methods for estimating phenology for large areas while providing reliable plot level information are therefore needed.
We address this need by exploiting a comprehensive data set on phenological observations across Germany provided by the German Weather Service (DWD) that we match to winter wheat plots identified from the German-wide 10 m crop type map by Blickensdörfer et al. (2021). This allows to relate time series from Sentinel-1, 2, and Landsat 8 data to field observations for 700 to 800 locations for every year since 2017. We complement our remote sensing data by meteorological data provided by the DWD.
From the remote sensing imagery, we summarize the pixel values for each plot and calculate time series of the Normalized Difference Vegetation Index (NDVI), γ0 backscatter coefficient (VH & VV), and backscatter cross-ratio. We derive daily precipitation sums from DWD precipitation radar (RADOLAN) and temperature and global radiation measurements by spatially interpolating observations from DWD weather stations. To suppress noise in the remote sensing time series, we use locally weighted scatterplot smoothing (loess) and ensure a common temporal interval of the different data sources by linear interpolation. We feed the resulting time series into a one-dimensional convolutional neural network (CNN), that is trained to deliver the entry dates of four different phenological stages for winter wheat, namely stem elongation, heading, ripening, and harvest.
Preliminary results show that the chosen method is suitable for deriving phenological metrics from the combined optical, radar and meteorological time series. The time-consuming tuning of the model architecture and the selection of the most suitable input time series, however, are still pending prior to an in-depth validation. Further tests will be performed to investigate the suitability of the approach in comparison to established methods to identify strengths and limitations.
The presented approach is new to the field of crop phenology analysis in two respects: first, the use of machine learning and, in particular, deep learning to derive crop phenology has only laterally been explored, and second, the chosen model architecture allows the direct combination of optical, radar and meteorological data instead of establishing separate rule-sets and combining them ex-post. After finalizing the model, area-wide predictions will allow to analyze spatial patterns and trends of phenology in the agricultural landscapes across Germany.
References
Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., Hostert, P., 2021. National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2017, 2018 and 2019). https://doi.org/10.5281/ZENODO.5153047
Meroni, M., d’Andrimont, R., Vrieling, A., Fasbender, D., Lemoine, G., Rembold, F., Seguini, L., Verhegghen, A., 2021. Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2. Remote Sens. Environ. 253. https://doi.org/10.1016/j.rse.2020.112232

The integrated European Long-Term Ecosystem, critical zone and socio-ecological Research Infrastructure (eLTER RI) is now in the preparatory phase (started in 02/2020) of formal implementation as a pan-European Research Infrastructure. The eLTER RI will comprise about 250 eLTER Sites and eLTSER Platforms from over 20 participating European countries covering a vast majority of biogeographical regions. It will offer a wide range of European-level Central Services for different users, including access to sites and site data from multiple sources. eLTER RI will enable standardized observations and analyses of the environment in a holistic manner, encompassing biological, geological, hydrological and socio-ecological perspectives.

Among others eLTER considers the remote sensing community in general as one of its standard users. The reason is twofold: First, eLTER anticipates monitoring various variables which are of also of high relevance for Earth Observations (e.g. vegetation phenology, LAI, Biomass, soil moisture etc) (Zacharias et al. 2021), and enables open access to these data for third party usage (e.g. upstream service). Second, the strategic European priorities listed in the ESFRI roadmap provide a political framework for formalized RIs like the future eLTER RI. This enables the long-term coordination and sustainability of eLTER RI at both national and continental scales, and facilitates the co-design of services according to user needs.

We will share details of the current status and future eLTER RI and highlight how eLTER’s contribution to EO cal/val activities could be realized along the examples of currently pursued integration of standardised phenology into long-term Sentinel-2 (Copernicus) data series. Moreover, eLTER RI has apparent potential to provide upstream service for required standardised ground-truth measurements (including protocols, open access etc.) and for the usage of remote sensing data for eLTER users (downstream service). To achieve this, eLTER RI has initiated continuous collaboration between the remote sensing community including most prominently EO data/product providers such as Copernicus and the communities providing essential ground-truthing over time spans of decades This interface will be another aspect we will touch upon to foster the fruitful exchange and initiate discussions regarding the requirements and concrete actions from both sides.

[Zacharias, S. et al. (2021). Discussion paper on eLTER Standard Observations (eLTER SOs). Deliverable D3.1 EU Horizon 2020 eLTER PLUS Project, Grant agreement No. 871128.]

Land Surface Phenology (LSP) is stated in Skidmore et al (2021) as one of the 30 most important Remote Sensing enabled Essential Biodiversity Variables (RS-enabled EBVs) that are core in assessing and monitoring global biodiversity changes. Within our work, we access LSP characterized as regular, recurrent biological events depending on the annual vegetation activity of vegetated land surface observed from remote sensing. LSP is already widely used either regionally in high-resolution or globally in lower resolution (e.g., based on MODIS data) as a measure for terrestrial ecosystems’ responses to changes of environmental conditions or for characterising species composition and biodiversity of an ecosystem.
Having access to a global, high-resolution LSP data set would allow a homogenous and standardized use of information such as start of season or length of season. However, only since the launch of the Sentinel-2 satellites, with a temporal resolution of 5 days
globally, is a global LSP product on 10m resolution reliably feasible based on this dense
time-series. Due to the high amount of data (pixels and number of images per pixel) needed, a fully automated processing chain implemented on a cloud computing platform is crucial. Within this project, we thus implement a LSP algorithm for global and automated use including an automated validation and verification approach on the Google Earth Engine. Next to a flexible implementation, an accuracy measure is crucial for a reliable and correct use of the output data and the interpretation thereof. Therefore, a system for verification and validation is used resulting in quality layer as an interpreted measure for assessing the reliability of each individual pixel based on the input data and processing as well as an independent validation approach, e.g., with phenocams or alternative satellite data sets (e.g., MODIS LSP product).
The output should therefore be available and easily implementable for anyone needing such a data set on a transparent, open-source basis and thus is intended to provide the basis for a better use of remote sensing data for biodiversity conservation efforts and policy making within the EBV framework.


Skidmore, A. K., Coops, N. C., Neinavaz, E., Ali, A., Schaepman, M. E., Paganini, … (2021). Priority list of biodiversity metrics to observe from space. Nature Ecology and Evolution. https://doi.org/10.1038/S41559-021-01451-X

The ever-increasing pressure on tropical forests implies the need for better characterisation of their properties. This information is crucial for the delineation and protection of the most vulnerable regions. The remote sensing community has reacted by developing new methods for biodiversity variables estimation as potential contributions to the Essential Biodiversity Variable (EBV) framework. Methods based on spectral variation hypothesis and linking spatial heterogeneity of optical imagery with different components of biodiversity show good potential for wide scale biodiversity monitoring. These spectral based methods allow for both alpha (local diversity) and beta (community distribution) diversity estimates at a regional scale. They also make possible the upscaling of biodiversity monitoring results based on costly field surveying efforts. At this time, it is crucial to show that the results from these new remote sensing-based biodiversity indices are robust to atmospheric correction artefacts.
Sentinel-2 satellite data are particularly relevant for vegetation monitoring: the spectral information acquired from Earth surface integrates vegetation chemical, structural and taxonomic information at high spatial resolution, with a five-day revisit period. The exploitation of such data strongly depends on its spatial and temporal consistency after correcting the spectral information from atmospheric effects. Such a consistency is critical for tropical forest monitoring, where cloud cover strongly reduces the availability of operable data, and regional monitoring may require multiple acquisitions.
In this study, we evaluated the stability of the Level-2A (bottom of atmosphere) reflectance products obtained from four different atmospheric correction methods - Sen2cor, Maja, Overland and LaSRC – as well as spectral indices and biodiversity products computed from these reflectance data. When considering pixels with constantly high NDVI values, we made the hypothesis that dense tropical forest should not undergo drastic reversible changes over a short period of time. We selected five Sentinel-2 acquisitions from a forested area located in northern Cameroon over five weeks and compared reflectance data, spectral indices and biodiversity indices derived from the four atmospheric correction methods.
Our results showed that the temporal consistency in reflectance, vegetation indices and biodiversity products over the five-week time step was strongly dependent on the choice of the atmospheric correction method. The temporal stability of L2A-reflectance, spectral indices and diversity indices obtained strongly varied depending on the atmospheric correction method. We found only moderate agreement when comparing the results between methods, even when comparing the aggregated mean values over the full period. Validation is in preparation to allow the comparison between biodiversity patterns with ground observations.
These results highlight the importance of considering the atmospheric correction method used in tropical forest studies. Both reproducibility and validity of biodiversity estimation methods will benefit from systematic explanation and justification of the atmospheric correction method used.

Ice-free areas within the northern Antarctic Peninsula region are found along the coast lines and are mainly influenced by glacial, periglacial and paraglacial processes. Although rock outcrops, sediments and water bodies are the dominant surface covers in such areas, there is a concentration of fauna and flora where mosses and lichens are the main vegetation cover and bird and seal colonies have their breeding grounds. Numerous studies have shown that these ice-free areas contain fragile ecosystems with a potential biodiversity that is influenced by an active hydrologic cycle during the austral summer period. Remote sensing data and techniques are ever more useful for characterizing and monitoring biodiversity and ecosystems location. The objective of this work was to identify different indicators of biodiversity using remotely sensed data within the South Shetland Islands.
The ice-free area of Byers Peninsula in the western part of Livingston Island has an extension of 60 km2, being the largest ice-free area in the archipelago, and is designated as an Antarctic Specially Protected Area (ASPA 126). This area contains a particularly wide diversity of species in comparison to other areas in Antarctica. In this case, biodiversity indicators include spectral features of vegetation covers, surfaces areas saturated with melt water and water bodies, and areas under the influence of penguins and elephant seal colonies that are determined from data obtained with different optical satellite sensors and with spectroradiometer measurements in the field and laboratory. The field and laboratory spectra are used as reference spectra that validate the signature and for the interpretation of the data obtained from the multispectral sensors. Combining the spectral information obtained from the field and lab spectra and using the multispectral spatial resolution, the aim is to obtain an improved characterisation of the indicators. This information is used to simulate information obtained from current and upcoming high spectral resolution satellite sensors such as PRISMA and EnMAP.
Multispectral Landsat-8 and Sentinel-2 data were obtained on the 23/03/2018 and 20/12/2019, respectively. Furthermore, field spectroradiometer measurements with an ASD FieldSpec3 and samples for laboratory analysis were taken during the campaign carried out in February of 2018. The spectral measurements were taken with a contact probe in the field and with a turntable in the laboratory. A digital elevation model was created from topographic information available for the region and used for determining the present fluvial drainage system of the different watershed. Preliminary results from the multispectral sensors for the vegetation show an extensive distribution of moss on the upper Holocene raised beaches. The moss in vigour is easily identified, but this will be reduced depending on the snow cover that affects the vegetative cycle. Areas dominant with lichens are less well identified due to their structure and density. Water saturated sediments as well as water bodies are well identified providing an overview of the different extensions of the numerous watersheds. Bird and elephant seal colonies are also determined due to the guano that is present in such areas. All these surface covers act as indicators of higher biodiversity. This is shown by soil processes evolving under a vegetation cover where for example microbiological communities are active in the soil. These latter communities also play an active role within the animal colonies. Furthermore, available water for seasonal nutrient dynamics and particle transport throughout the watersheds are key in activating biogeochemical processes.

Climate change and human activities are rapidly impacting biodiversity around the globe, posing concerns on the loss of ecosystem function and the critical goods and services they provide. Though, the conservation efforts are hindered by the limited availability of spatially and temporally contiguous information on key aspects of biodiversity. Remote sensing encompasses a wide array of tools capable of providing consistent observations across space and over time. In particular, hyperspectral imaging spectroscopy may advance the assessment of biodiversity due to its spectral dimensionality. The recent and upcoming availability of spaceborne hyperspectral sensors may pave the path towards an integrated global biodiversity monitoring system, providing the possibility to overcome the limitations of the ground-based and airborne hyperspectral sensors in terms of spatial and temporal coverage.
In this contribution, we explore the potential of the new generation PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral sensor of the Italian Space Agency for biodiversity estimation in forest ecosystems located in different geographic contexts. Emerging techniques based on vegetation functional traits as well as on spectral information content for estimating the ecosystem heterogeneity were applied to PRISMA images acquired on contrasting forest ecosystems in Europe in the summer of 2021. The selected sites for this study are a plain mid-latitude mixed forest (Hardt forest) located in Alsace (France) and mountain forests along an altitudinal gradient located in the Gran Paradiso National Park in the western Alps (Italy). The functional diversity patterns obtained were analysed based on the expected drivers of the vegetation heterogeneity in the considered study areas and on complementary in-situ data. We found that the main factors determining the biodiversity patterns are the species composition and the forest structure and maturity stage in the plain temperate forest, while the morphological and altitudinal factors play a fundamental role in the mountain alpine forests. Future directions and possibilities offered by the newly available streams of Earth Observation data are also discussed in this contribution.

Biodiversity change has many dimensions and occurs at a range of spatial and temporal scales rendering traditional in situ biodiversity monitoring inadequate for regular global monitoring. Direct human observation of species and their traits is both expensive and time consuming, with poor consistency in spatial and temporal coverage when upscaled to country or global level. This poses a challenge as UN Convention on Biodiversity (CBD) obligates its 196 parties (the EU included) to monitor the 20 Aichi targets, such as target-5 to halve the rate of loss of natural habitats by 2020 and target-15 to restore 15% of degraded ecosystem by 2020. Remote sensing continually surprises by 'seeing the unexpected' in wavelengths ranging from the optical (for instance, seeing the biochemical content of plants4) to shortwave- and thermal-infrared (e.g. seeing how water content varies within a tree canopy and beyond to radar (seeing flooding under trees). In this presentation we demonstrate how the relative abundance of bacterial family taxa in different ecosystems (boreal, tundra and savanna) can be mapped using hyperspectral imagery remote sensing. The technology to achieve this has been developed using hyperspectral imagers combined with eDNA profile data of microbiological taxa. Environmental DNA (eDNA) can rapidly profile taxonomic groups at order, family, and genera levels across the entire tree of life using barcoding methods that use a short genetic marker in an organism's DNA to match it with DNA belonging to a known species. An innovation here is to go from point data (eDNA) to interpolating continuous genetic composition biodiversity products over large geographical areas and then to understand how biodiversity in general and ecosystem function in particular respond to biotic conditions, competition, and stress. A key question is whether such genetic composition biodiversity products and Essential Biodiversity Variables mapped using remote sensing are indeed relevant to policy indicators. The improved biodiversity information will more completely inform our progress in meeting CBD Aichi targets and UN Sustainable Development Goals, as well as generate more accurate biodiversity maps required to further improve predictions from cutting-edge ecological and climate models.

Land remote sensing capabilities in the optical domain have dramatically increased in the past decade, owing to the unprecedented growth of space-borne systems providing a wealth of measurements at enhanced spatial, temporal and spectral resolutions. Yet, critical questions remain as how to unlock the potential of such massive amounts of data, which are complementary in principle but inherently diverse in terms of products specifications, algorithm definition and validation approaches. Likewise, there is a recent increase in spatiotemporal coverage of in situ reference data, although inconsistencies in the used measurement practices and in the associated quality information still hinder their integrated use for satellite products validation. In order to address the above-mentioned challenges, the European Space Agency (ESA), in collaboration with other Space Agencies and international partners, is elaborating a strategy for establishing guidelines and common protocols for the calibration and validation (Cal/Val) of optical land imaging sensors [1]. Within this paper, this strategy will be illustrated and put into the context of current validation systems for land remote sensing. A reinforced focus on metrology is the basic principle underlying such a strategy, since metrology provides the terminology, the framework and the best practices, allowing to tie measurements acquired from a variety of sensors to internationally agreed upon standards. From this general concept, a set of requirements are derived on how the measurements should be acquired, analysed and quality reported to users using unified procedures. This includes the need for traceability, a fully characterised uncertainty budget and adherence to community-agreed measurement protocols. These requirements have led to the development of the Fiducial Reference Measurements (FRM) concept, which is promoted by the ESA as the recommended standard within the satellite validation community. The overarching goal is to enhance user confidence in satellite-based data and characterise inter-sensor inconsistencies, starting from at-sensor radiances and paving the way to achieving the interoperability of current and future land-imaging systems.

[1] Niro, F.; Goryl, P.; Dransfeld, S.; Boccia, V.; Gascon, F.; Adams, J.; Themann, B.; Scifoni, S.; Doxani, G. European Space Agency (ESA) Calibration/Validation Strategy for Optical Land-Imaging Satellites and Pathway towards Interoperability. Remote Sens. 2021, 13, 3003.

The global market of Small UAV is knowing a fast growth, in Europe is valued at $197 million in 2015 and is forecast to reach as much as $3 billion by 2025 [1]. The recent advancements in technologies and sensors make possible a wide range of UAV models in various sizes, weights, and payload carrying capabilities to support the broad range of commercial and military applications. Moreover, since 2021 the emanation of a unique and agreed regulations made by the European Union Aviation Safety Agency (EASA) are supporting a more rational development and deployment of drones in various sectors, like land mapping, infrastructure, agriculture, transport, entertainment, and security. Toward more specific applications, we proposes an innovative approach for in-situ validation of optical satellite products, using small UAV system (hexa-rotors) equipped with a multispectral camera Sentinel-2 likes. In particular, we focus on the surface reflectance and Bidirectional Reflectance Distribution Function (BRDF) [2] validation using a peculiar payload and programming an acquisition plan capable to provide enough data and automatic, repeatable surveys on different surfaces.
BRDF describes the level of surface anisotropy, the correction techniques is conceived to normalize the observations to a standard angular configuration, or alternatively, as an intrinsic signature of the considered surface that can be used to retrieve relevant bio-geophysical variables. The parametrization of the Bidirectional Reflectance Distribution Function (BRDF modeling) is based on the inversion of the model with observed data (multi-angular measurements made by different type of devices such as field instruments or satellites) in order to find the model’s parameters which best fit the measurements. The calibrated model is then applied to predict reflectance at any sun-view geometry conditions. Although reflectance at any observation angle can be obtained using a ground-based multi-angular instrument, the complicated measurement process is time-, labor- consuming, and only "point" measurements can be retrieved [3].
At present, there is a lack of extensive in-situ validation data to assess the quality of BRDF correction. The proposed experimental activity demonstrates that the combination of a multispectral camera Sentinel-2 likes and the UAV hexa-rotor flight potentials (as the capability of setting different view azimuth angles and view zenith angles), can improve the capability of BRDF modelling and validation. In order to pursuit our goals, we identified the following implementation steps: the Integration of multispectral camera on UAV platform; the system calibration; the flight planning; the dataset processing; the BRDF modeling.
The payload carried by the UAV is the multispectral camera MAIA S2 [4], which permits the simultaneous acquisition of high-resolution images at various wavelength intervals in VIS/NIR (Visible/Near-Infrared) electromagnetic spectrum regions. In particular, the multispectral camera is designed to acquire in the same wavelength intervals of the ESA Copernicus Sentinel-2 (S2) satellite constellation. The imaging sensors has 1.2 Mpixel resolution, high sensitivity and global shutter technology, allowing the simultaneous acquisition of images free from motion artefacts. Moreover, the acquisition system includes a further module, the Irradiance Light Sensor (ILS), which measures the level of the down-welling light in each band, allowing the correction for light changes during the survey, such as those caused by clouds. The use of the ILS is crucial for radiometric correction when passing from digital number (DB) to reflectance. ILS provides irradiance data at the exact time of shooting for each image and in each spectral band, substantially improving the accuracy of reflectance values in multi-temporal surveys.
The UAV system is a hexa-rotor with a maximum take-off weight up to 6 kg. It is equipped with a Plug & Play System, which allows the pilot to switch between payloads. Real-time mission management is possible as well Autonomous Waypoint navigation. The flight unit has integrated Global Positioning System (GPS) and Inertial Measurement Unit (IMU) sensors and auto-stabilization of flight in manual mode. The system is characterized by a maximum flight time up to 20 minutes. The Italian Civil Aviation Authority (ENAC) has given the Certification of Design attesting the compliance to Italian and European (EASA) laws. Remote controlled gimbal for orienting MAIA camera between 0° and 90° respect to surface normal direction is possible as well as controls of orientation on the horizontal plane. These two latter properties are very useful for a complete BRDF characterization.
In this work, we present the results obtained by testing our UAV system over two different land cover type: vegetation and asphalt. The flight plan designed for BRDF modeling survey, provides acquisition in 12 view azimuth angles (0 °, 30 °, 60 °, 90 °, 120 °, 150 °, 180 °, 210 °, 240 °, 270 °, 300 °, 330 °) and for each of those 7 different view zenith angles are set on the camera gimbal (0 °, 10 °, 20 °, 30 °, 40 °, 50 °, 60 °), for a total of 84 measurements for each survey. After the preprocessing stage, we evaluate the variation of the spectral signature varying the view azimuth and zenith angle, producing the polar plot of the reflectance for each band. The dataset have been used to compare two semi-empirical models: the kernel-driven RossThick-LiSparse (Ross-Li) linear model [5], which has been used for the processing of MODIS land surface measurements [6], and the Rahman-Pinty-Verstraete (RPV) non-linear model [7]. The set of parameters have been derived for both the models and then validated respect the UAV measurements for each band, showing an overall R-squared of about 0.8.
The development and test activities here described have been carried out as part of the ESA-funded Quality Assurance for Earth Observation (IDEAS-QA4EO) framework contract (WP-2175, UAV for BRDF characterization) [8] and will be further implemented during the second phase. The UAV system is considered to be part of the SRIX4VEG initiative that will be held in Barrax (ES) during summer 2022 [9].


[1]: de Miguel Molina, B., & Oña, M. S. (2018). The drone sector in Europe. In Ethics and civil drones (pp. 7-33). Springer, Cham.
[2]: Bartell, F. O., Dereniak, E. L., & Wolfe, W. L. (1981, March). The theory and measurement of bidirectional reflectance distribution function (BRDF) and bidirectional transmittance distribution function (BTDF). In Radiation scattering in optical systems (Vol. 257, pp. 154-160). SPIE.
[3]: Müller, C., Hosgood, B., & Andreoli, G. (1998). Sensitivity analysis and quality assessment of laboratory BRDF data. Remote Sensing of Environment, 64(2), 176-191.
[4]: Nocerino, E., Dubbini, M., Menna, F., Remondino, F., Gattelli, M., & Covi, D. (2017). GEOMETRIC CALIBRATION AND RADIOMETRIC CORRECTION OF THE MAIA MULTISPECTRAL CAMERA. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42.
[5]: Wanner, W., Li, X., & Strahler, A. H. (1995). On the derivation of kernels for kernel‐driven models of bidirectional reflectance. Journal of Geophysical Research: Atmospheres, 100(D10), 21077-21089.
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During the last few years, unmanned aerial vehicle (UAV, or drone) technology has evolved tremendously. At the same time, miniaturization of hyperspectral sensors has made it possible to use these sensors on board small drones, providing completely new possibilities to carry out high quality remote sensing measurements with high spatial and spectral resolution. Yet a new drone application is performing calibration and validation (Cal/Val) measurements for optical satellite sensors, such as Sentinel-2. ESA has created the concept of Fiducial Reference Measurements (FRM), which have been defined to be a subset of in situ measurements used for satellite Cal/Val with robust and demonstrable traceability to SI or community standards following documented procedures with a clear uncertainty budget. In the context of the EMPIR (The European Metrology Programme for Innovation and Research) -funded MetEOC-4 project (Metrology for Earth Observation and Climate) lead by National Physical Laboratory NPL, UK, we will present our results on performing multiple hyperspectral drone campaigns for Sentinel-2 FRM measurements over one hectare forest site located in southern Finland.

The overall goal of the MetEOC-4 is to develop calibration and validation standards and methods, covering pre- and post- launch of observation systems as well as complimentary in situ networks, for land, ocean, and atmosphere. This will extend the capabilities of the SI beyond the laboratories, into the ‘field’ and build on the framework needed to underpin a global climate observing system. The challenge in several current satellite Cal/Val test sites is that the object is bright and non-vegetated (e.g. sand). We need extension of test sites to biophysical surfaces requiring challenging spectral/spatial corrections. Additionally, these vegetated sites need to be monitored under different illumination and phenological conditions over multiple time steps.

Our research group at the Finnish Geospatial Research Institute FGI has been utilizing drones in various remote sensing tasks since 2008. We have been developing quantitative and SI-traceable image processing chains for hyperspectral drone imagery. The outcomes of our processing chain are geometrically accurate 3D point clouds, radiometrically corrected homogeneous reflectance image mosaics and bi-directional reflectance distribution function (BRDF) information of the object.

In this study, we have performed seven hyperspectral drone campaigns between June and October 2021 with solar zenith angles between 37° and 70°. The test site is one hectare forest site located in Kirkkonummi, Southern Finland. The forest consists of mainly full-grown coniferous trees (height about 20 m), Norway spruce (Picea abies) and pine (Pinus sylvestris), with some deciduous trees (birch (Betula pendula)). We used a DJI Matrice 600 Pro drone with Specim AFX10 hyperspectral camera and Gremzy T7 gimbal. AFX10 is a pushbroom sensor with 400 to 1000 nm spectral range, 224 spectral bands, 5.5 nm spectral resolution, 1024 spatial pixels, integrated GPS/IMU, internal SSD storage and 2.1 kg weight. Each flight was performed to match Sentinel-2 overpass time within +-15 minutes, with drone flight time about 15 minutes. Flights were performed from the 90 meters flying height resulting in a 6 cm ground sampling distance (GSD). Illumination conditions were monitored before, during and right after the flights by making aerosol optical thickness (AOT) measurements with Microtops-II sunphotometer. Four reference reflectance panels of 1 m2 size were installed next to the drone take off area to be used as radiometric calibration reference panels for the AFX10 data. Additionally, we performed one drone liDAR campaign with DJI L1 lidar over the test forest to generate accurate 3D point cloud, digital terrain and surface models of the area. The flying height of the liDAR campaign was about 90 meters above ground and 70 meters above treetops, producing approximately 500 points / m2.

In this presentation, we will introduce our FRM drone campaigns, data processing methodologies and first results. We will discuss the impact of key factors such as solar geometry, atmospheric conditions, and phenology to the Cal/Cal process. We will analyse the impact of the uncorrelated errors in the comparison of ground to satellite observations and different ground measurement methods to derive an estimation of the uncertainty improvements with more flights. This work will extend the application of these techniques beyond the current single site, single time step scenario which will improve its scientific relevance and uptake by the international community.

Over the past decades, many radiative transfer models have been developed and are widely used for e.g. vicarious calibration and look-up table generation for atmospheric correction. Many of these models ship atmospheric property databases. Sub-components of these models have been extensively tested in ideal conditions but so far, no initiative similar to RAMI has been undertaken to systematically compare models when they are used to simulate actual satellite observation. The accuracy of these models has not been clearly assessed in realistic usage conditions. As well as for RAMI-1, the primary goal of RAMI4ATM will be to document the variability between coupled surface-atmosphere models when run under well-controlled, but realistic, conditions.

This new phase is oriented toward the support of calibration and validation activities relying on the use of radiative transfer models for the simulation of satellite observations in the visible, near and shortwave infrared spectral regions. It is therefore primarily directed at users of models used for calibration and validation activities. A series a standard cases have been foreseen including:
• The following Surface types will be included : Lambertian, RPV, Ross-Li, homogeneous discrete canopy;
• A standard atmospheric profile will be considered with the possibility to rescale the water vapour concentration;
• Fine and coarse aerosol types with different low and high optical thickness;
• Simulations will be performed in the blue, green, red, NIR and SWIR spectral regions corresponding to Sentinel-2A MSI bands B02, B03, B04, B08A, B11 and B12.

The expected outcome of this exercise is as follows:
• to allow users to cross compare their simulations with respect to the other participating models over a variety of ideal atmospheric scenarios including gas absorption, Rayleigh and Mie scattering;
• to inform the user community on the performance of the participating available models and the differences in their results;
• to help developers improve their models;
• to progressively build community consensus on the best ways to simulate the radiative transfer below and above the Earth’s atmosphere.

First comparison results will be presented at LPS22.

The correction of the atmospheric effects on optical satellite images is essential for quantitative remote sensing applications. Open and free data access to Copernicus Sentinel-2 (EC/ESA) and Landsat 8 (NASA/USGS) missions increased significantly the scientific interest on atmospheric correction (AC) and several approaches have been introduced by involving different radiative transfer models, single or multitemporal images, various algorithms to estimate aerosol properties and water vapour content, constant or diverse aerosol models, various sources of ancillary data, etc. These methodologies are usually validated independently by developers and/or users based on a certain number of sites with available reference data and/or are compared with results of other AC processors.
In order to investigate all the AC aspects and issues in an integrated way, a benchmark exercise (Atmosperic Correction Inter-compariosn eXercise, ACIX) was initiated in 2016 in the frame of CEOS Working Group on Calibration & Validation (WGCV) with the aim to compare the state-of-the-art AC processors. ACIX is a voluntary and open-access initiative to which every AC processor’s developer is invited to participate. ACIX-I was an initial attempt to study the variability of AC performances over diverse atmospheric and land cover conditions using Landsat 8 and Sentinel-2A input data. It was highly appreciated by the participants and considered as a useful tool to discover not only the assets and flaws of the approaches, but also ways to improve them. Thus, a second implementation of the experiment was requested to inter-compare the enhanced versions of the participating processors, but also to be expanded by including additional AC processors. In this second implementation, ACIX was split in two categories: Land and Aqua, with focus on the processors performing over land and water correspondingly. In this presentation, attention is given only to the Land part of the exercise.
The sites for the inter-comparison analysis over land were defined by investigating the full catalogue of AERONET sites for available measurements within 30min (±15min) from the satellites’ overpass. Eventually a total of 123 and 110 AERONET sites, which were distributed globally and representing various land cover types, made the site list for Sentinel-2A, -2B and Landsat 8 acquisitions correspondingly. Based on these available AERONET measurements, Aerosol Optical Depth (AOD) and Water Vapour (WV) retrievals were validated with the help of various statistical metrics. Regarding Surface Reflectance (SR) validation, as there is not yet any global network of systematic ground-based measurements, alternative approaches had to be adopted. Therefore, simulated SR reference dataset was computed over all the test sites by using the 6SV full radiative transfer code, with the required aerosol and water vapour information to have been acquired from AERONET. Moreover, measurements from the calibration dedicated network RadCalNet over La Crau (France) and Gobabeb (Namimbia) were involved in the SR validation. The observations in this case were processed to the same sun and sensor geometry, as well as spectrally integrated to the corresponding sensor spectral bands of Sentinel-2 and Landsat 8. The analysis results varied depending on the AC product compared, the reference dataset and the metrics. In this presentation an overview of the analysis and results will be given and discussed.

The validation of the Sea and Land Surface Temperature Radiometer (SLSTR) Land Surface Temperature (LST) operational product is one of the main objectives of the ESA Copernicus Space Component Validation for Land Surface Temperature, Aerosol Optical Depth and Water Vapour Sentinel-3 Products (LAW) project.
Five new ground LST stations were deployed in the LAW project to validate SLSTR LST over previously unrepresented biomes, which were selected after a land cover gap analysis study. The LST LAW stations are located in: KIT Forest – Germany, classified as closed broadleaved deciduous forest according to the ATSR LST Land Cover classification, which is used in the SLSTR LST product; Svartberget – Sweden, classified as open needle leaved deciduous or evergreen forest; Hyytiälä – Finland, classified as closed to open mixed broadleaved and needle leaved forest; Robson Creek – Australia, classified as closed to open broadleaved evergreen and/or semi-deciduous forest; and Puéchabon – France, classified as sparse vegetation. The KIT Forest station was deployed in August 2020 since it is in the premises of KIT Campus North, however the other stations were deployed in October 2021. Thus, all five stations are operative since November 2021. Due to the earlier deployment of the KIT Forest station, it provided a valuable dataset to test the tools created for monitoring and evaluating the LAW insitu LST data.
The main instruments of all five LST stations are two narrowband Thermal Infrared Heitronics KT15.85 IIP radiometers, which measure the ground brightness temperature and the sky brightness temperature at a zenith angle of 53°, respectively. These radiometers are used to estimate the in-situ LST via the radiative transfer equation. The Heitronics KT15.85 radiometer covers the spectral range from 9.6 to 11.5 µm and has an associated uncertainty of ±0.3 K. All radiometers used in the LAW stations were calibrated at laboratory against a primary reference blackbody at the Physikalisch-Technische Bundesanstalt (PTB – Germany) and against the KIT LandCal P80P blackbody certified by PTB. Additionally, the stations are equipped with a HydroVUE10 air temperature and relative humidity sensor, which provide useful information for monitoring the radiometric data.
The in-situ LST uncertainty for each station was estimated via propagation of errors in the radiative transfer equation. Additionally, the uncertainty introduced by each uncertainty source was analysed. For the larger KIT Forest dataset, it was obtained a mean in-situ LST uncertainty of 0.32 ±0.03 K, with values up to ±0.4 K. The main contribution to the total in-situ LST uncertainty was introduced by the ground KT15.85 radiometer, with a mean uncertainty of ±0.3 K, although the emissivity also showed a significant contribution, with uncertainties up to ±0.29 K. The lowest uncertainty contribution was introduced by the sky radiometer measurements, with a mean value of ±0.035 K. Similar results were obtained for the available period of the other four LAW stations, which showed a mean (maximum) in-situ LST uncertainty of ±0.33 K (±0.44 K), ±0.38 K (±0.46 K), ±0.32 K (±0.43 K) and ±0.35 K (±0.44 K) at Hyytiälä, Puéchabon, Svartberget and Robson Creek stations, respectively. Thus, the mean in-situ LST uncertainty for all new LAW stations is lower than ±0.4 K. The in-situ LST uncertainty for each LAW station will be reprocessed for a larger dataset before the ESA Living Planet Symposium 2022.

This presentation will introduce this session which is devoted to platform and communication space-to-ground technologies for future Earth Observation (EO) systems. As starting point, an overview of the Technology strategy in ESA for EO will be provided, but aspects related to Microware and Optical Instruments for EO will be further elaborated in complementary sessions within the same Space Technologies Theme.

A very important point of this EO Technology Strategy is related not only to de-risking technologies and enabling new and more performing capabilities such as allowing the downlink of higher data volumes, but also to reducing recurring costs and to the faster adoption of innovative technologies. In this respect, the standardization of the platform, enabling the availability by multiple supplying sources of not only full platform sub-systems, but also of their modules and their easy replacement, is of paramount importance. The adoption of spin-in technologies e.g. COTS and new engineering approaches to adopt is part of this game changer. Higher lifetime and more flexibility (e.g. with more on-board reprogrammable FPGAs devices) are other very important points that require technology advances.

All these aspects become more and more important in a context of system-of-systems, and the coexistence of institutional EO flagship satellites acting as reference and complemented by NewSpace constellations of less costly and smaller and possibly smarter spacecrafts providing the required temporal revisits, thanks to more integrated and miniaturized technologies. This opens a number of additional requirements on more autonomous platforms and operations, as well as on synchronization and co-registration aspects. More distributed and scaleable ground segments are expected to support this paradigm shift. On the other side, all this growth in capability to observe climate changes needs to be supported by cleaner and demisable technologies.

The presentation will close with specific examples of game-changing technology developments in for example electronic components, or related to specific platform sub-systems like the data downlink communications or the game-changer Advanced Data Handling Architecture (ADHA) project, which will be further complemented by the other presentations in the session, by Large Scale Integrators but also by ESA regarding clean space and ground technologies.

AstroBus is Airbus' LEO avionics product. It comprises a suite of flight hardware, software, documentation, test benches and tools, implemented and managed as an avionics product. It is continuously maintained and adopts improvements/lessons learned from individual projects, shared and addressed for the benefit of current and future projects. Projects based on an avionics product benefit from proven elements already developed, validated and flown together. This limits non-recurring development costs, and offers attractive recurring costs. This also brings savings in verification activities, software development and validation, procedures, and testing platforms.

AstroBus is being used on many Airbus export or commercial EO missions and several ESA missions since around 2007. Different variants have been in operation with a large level of commonality in the equipment suite and software, as well as tools, documentation and processes.

Following an initiative to rationalise the product variants to create a more harmonised and efficient overall product, and supported by ESA’s 2018 Standard EO Platform study, a new unified reference is currently being implemented for Copernicus HPCM on LSTM and CRISTAL missions. The Generic Platform study gave Airbus a valuable forum to clarify and fully understand generic Copernicus requirements. Adaptations to for instance File based operation, CFDP (CCSDS File Delivery Protocol) or PUS-C require additional software functionality and some limited improvements to the OBC used in all AstroBus missions. Furthermore, new equipments have been added to the portfolio, integrated and qualified at avionics product level with the aim to increase the flexibility to the demands of different missions, technically or industrially – so, for example, double sourcing and SME participation already available on AstroBus, have been strengthened thanks to Copernicus HPCM missions. It also brings new technologies on the communication to ground.

For future ESA EO Future missions, Airbus intends to propose ESA to get full benefit of the Astrobus product. Additionally this will also include upgrades from the on-going developments such as the ADHA (Advanced Data Handling Architecture), which will bring further modularity and competitiveness to the platform product.

Besides, Airbus has also developed a highly competitive COTS based avionics for small platforms needs, which completes the Astrobus Product line.

OHB’s Standard Earth Observation Platform, Eos, serves the current and future needs for LEO Earth observation applications. Eos has been developed, among others, in the frame of the ESA Copernicus Standard Platform Study and will be implemented first for the Copernicus mission CO2M. The Eos platform is founded on common and well-established state-of-the-art technologies, thereby providing the opportunity of a fast track and low risk platform adaptation to different mission needs. The essential element of Eos is its standard avionics core, capable of implementing File Based Operations and the associated developed SCSW with its PUS-C customization.
With the ever increasing demand for faster and more comprehensive data and new instruments transferring data at a speed of more than 1Gbps, an evolution in the platform avionics is essential to occur to keep serving the Earth Observation missions.
For the next generation of Eos, OHB is investigating multiple aspects to meet future mission needs, such as:
• a more centralized avionics architecture, where multiple SW functionalities are combined into one on-board computer (e.g. GNSS SW, Star Tracker). Therefore assessing high performance multi-core processors.
• Control & Command interfaces to match the high data rates
• Implementation of a scalable standard backplane as designed in the Advanced Data Handling Architecture.

To meet in particularly the high data rates, a new very high-speed data link has been introduced named SpeceFibre designed specifically for on-board spacecraft adoptable as on-board data handling protocol in next generation space missions, from earth observation to science satellites.

SpaceFibre is able to operate over fibre-optic and electrical cable and to fulfil a data rate of 6.25Gbps together with the possibility to use multiple Virtual Channels over the same physical link, each one configurable with flexible Quality of Service (QoS) parameters (priority level, reserved bandwidth, and assigned time-slots). This would also bring to a great harness reduction.
SpaceFibre provides three interfaces to the user application:

• A packet interface used to send and receive packets.
• A broadcast message interface used to broadcast short messages over the SpaceFibre network.
• A management interface used for configuring, controlling and monitoring the status of the SpaceFibre interface.

The presentation will also introduce complementary platforms to Eos available in the OHB group, that can be used especially for Earth Observation new space missions and constellations ranging from a platform mass of 50 – 250 kg. A brief product description will be given as well as some examples of implemented mission such as the Arctic Weather Satellite (AWS) and the European IOD/IOV Satellite (EIS).

Following an initiative to rationalize the LEO platform product line in Thales Alenia Space, our M.I.L.A platform product line has been defined as the new European platform solution to address the high end LEO market. M.I.L.A stands for Multiapplication Innovative and moduLAr platform. As the Thales Alenia Space European platform solution, this platform product line can be proposed by each TAS entity as primeship : Thales Alenia Space France, Thales Alenia Space Italy or Thales Alenia Space UK. Each entity is in position to propose this platform product line under its satellite primeship and instantiate independently and with a full mastery the product. This solution has been contracted for the three Copernicus expansion missions CHIME, ROSEL and CIMR with under Thales Alenia Space France prime for CHIME and Thales Alenia Space Italy for ROSEL and CIMR. This platform product line is based on a building blocks approach to manage a large variability and scalability, such as platform mass 300 kg up to 3T, power capacity up to 6 KW and lifetime up 12.5 years. Variability of the product line covers TTC in X band, gyro or gyroless AOCS, modular power architecture …. This allows it to cover any kind of payloads including optical, radar or others. The development has started in 2018 and is running with the core qualification foreseen end 2023. Effectiveness and competitiveness of the solution has been implemented through product line engineering methods, innovation and state of the art technical solutions. The variability covers also the supplier list where some dual supplier scheme are possible without impact. The development is managed with a standalone project that owns the development of the product line with its first instantiation for the three Copernicus missions. This project named our “common platform” steers the M.I.L.A product line platform development and also the common solution developed for PDHT equipments. As a transnational team composed of French and Italian engineers manages the current development. The roadmap of the product line foresee electrical propulsion, AI, autonomy, and also Digital technologies... Thanks to its building block approach and flexibility, the use of this product line is not limited to Earth Observation but is foreseen also for Exploration and Science missions.

ESA CleanSpace, since its creation in 2012, has been proving the relevancy of its mandate - to maintain Europe’s leadership in innovating for a safer and cleaner space environment - with notable achievements such as the first ever active debris removal mission planned for launch early 2026, the set-up of the first worldwide frame for space ecodesign and the maturation of many technologies to manage spacecrafts’ end-of-life.

As part of its ecodesign branch, ESA CleanSpace has carried out a significant number of Life Cycle Assessment (LCA) studies covering different elements of a space mission (space, ground and launch segments) through its entire development sequence (phase A to phase E), allowing quantification of their environmental impacts on Earth. The LCA ¬is a standardised methodology (ISO 14040 and 14044) used to assess the environmental impacts of a product, process or service across all its stages and according to multiple environmental indicators (ozone depletion, resource depletion, global warming, human toxicity, etc.). ESA has pioneered the use of the LCA methodology with the objective to support the required evolution of space missions in response to the environmental regulations and challenges. Up-to-today, the ‘Space system Life Cycle Assessment Guidelines’, developed based on the results of LCA studies carried out at ESA and published by the Agency in 2016, are still the only guide to apply LCA to space activities world-wide. During the presentation, ESA will give a brief overview of the document as well as an introduction to the ESA developed LCA space database.
Further than just a ‘Green appeal’ approach, ESA has created some serious and significant knowhow in implementing LCAs in its own projects such as Ariane 6 or the Copernicus Expansion missions.

The second branch of CleanSpace tackles the reduction of debris in space. Proper management of End of Life (EOL) of satellites and launcher upper stages is key for the sustainability of space activities. Through the CleanSat initiative Europe has gained in the last years the competitiveness edge in several areas related to management of EoL, e.g. design for demise and passivation. This is a growing new market for space technologies, as Space Debris Mitigation (SDM) requirements embedded in the ISO24113 are evolving and now broadly applicable to institutional and commercial missions.
The Agency keeps thinking ahead of what is strictly required by the SDM requirements to ensure the competitiveness of the European space sector. A good example of this principle are the passivation solutions set forth by ESA. Every spacecraft operating in Earth orbit is required to deplete or make-safe all the energy stored on board (passivation) to avoid break-ups in orbit. As a showcase of the agency’s commitment to clean space, Copernicus Expansion missions are also requiring power passivation to be implemented even if the mission baseline is to perform a controlled re-entry and therefore passivation is not strictly needed.
In-Orbit Servicing is undergoing a very rapid development. In the scope of End-Of-Life, ESA has adopted a proactive approach towards Design for Removal i.e. prepare satellites to ease their capture and removal from orbit through an external service. In fact, the Copernicus Expansion missions will include features that will ease their removal from orbit in case of failure in-orbit. ESA will present the solutions currently under development to support the management of satellites including their removal with a particular focus on those planned to fly onboard EO missions in the coming years.

(R)evolution of EO satellite communications

Satellite communications for Earth Observation applications have been after some time a mature market with well established technologies. S Band for TT&C communications and X Band for the download of payload data are well established and this capacity is presently offered by many commercial operators.

Despite the high capacity of Earth Observation satellite links, compared to other satellite links, new missions require even higher communication capacity and the development and implementation of new technologies capable to download the ever-increasing amount of data captured by the various spacecraft instruments.

The presentation will introduce a quick overview of the present systems, to set a reference point. Then new technologies that are under development and implementation will be quickly introduced, indicating their benefits to the EO users. The following technologies are presented among others:

• Consolidation of K Band Links
• Weather forecast operations
• Variable Coding and Modulation/Adaptive Coding and Modulation
• CCSDS File Delivery Protocol in a distributed setup specific to Earth Observation
• Disruption Tolerant Network
• A full end-to-end communication protocol stack for a variety of Earth Observation scenarios
• Scalability and automation of operations for constellations
• Optical communications
• New Frequency Bands

Missions already under development like Copernicus Expansion (or HPCM) already rely on the timely implementation of many of these technologies. Other elements point at more distant dates, but are already in the roadmaps of future EO communication systems and shall be considered.

In the context of system of systems combining institutional and New Space missions and constellations, the ground segment shall evolve towards higher levels of automation, scalability, modularity and interoperability enabling growth thanks to more efficient operations.

The Integrated Maritime Policy of the EU has the objective to ‘support the sustainable development of seas and oceans and to develop coordinated, coherent and transparent decision-making in relation to the European Union’s sectoral policies affecting the oceans, seas, islands, coastal and outermost regions and maritime sector’. Furthermore, European directives such as the Water Framework Directive and Marine Strategy Directive require member states to reach good ecological status in their coastal and inland waters. Finally, the goals of the Helsinki Convention (HELCOM) – ‘Protect the Baltic Sea from all sources of pollution from land, air and sea, as well as to preserve biological diversity and to promote the sustainable use of marine resources’ – are very relevant for this topic.

Due to the large and industrialized population living in its drainage basin, years of pollution emissions (e.g. nutrients from agriculture), small water volume, and limited exchange of water with the Atlantic, the objectives mentioned above have not yet been reached in the Baltic Sea. Marine Spatial Planning (MSP) is a process that aims for the mitigation of the impacts of human activities and eventual improvement of the state of the environment through coordination and implementation of various practices and policies. Thus, one important action for the improvement of the state of the Baltic Sea is to improve the territorial and maritime spatial planning capabilities of the organizations operating in the area.

Earth Observation can help spatial planning processes by providing information about the state of the coastal environment, where most of the human activities take place. With this in mind, the objective of the ESA funded (EO4society programme) BalticAIMS project (www.syke.fi/projects/BalticAIMS) is to demonstrate an integrated data approach for essential processes of land and coastal water areas to better analyse and visualize the interactions. This goal will be reached by creating data access, visualization and analysis systems and tools. The system requirements have been defined together with national authorities and experts working with Marine Spatial Planning, and HELCOM groups (VASAB-MSP, Pressures) and reflect actual information needs. Based on a large number of interviews we defined the information requirements (EO and other) and in spring 2022 the project will launch a demonstration service that includes:

• EO and model datasets – Water quality, land use and land cover
• GIS material – Marine Spatial Planning, human impacts and pressures
• In situ water quality information – Monitoring stations, automated stations, Alg@line ferrybox

The demonstration service will cover selected areas in Finland, Sweden, Germany and Poland. The services will be built with the tools available in CREODIAS to utilize the advantages of the cloud environment. Various datasets will be made available through OGC interfaces which allow the users to access the data with their legacy systems, and through map applications such as SYKE’s TARKKA system (www.syke.fi/tarkka/en). The definition and implementation of respective interfaces is one key for the success of integrating the different data sets.

The presentation will show examples of how EO and other datasets can be combined to help users with their spatial planning tasks. The use cases include monitoring the effects of nutrient flow from the drainage basin to the coastal waters, monitoring the impacts of coastal activities and monitoring of temperature anomalies.

Estonian Weather Service at Estonian Environment Agency performs duties of Ice Service during winter navigation period. Responsibilities of Estonian Ice Service include daily production of (1) Ice report - an overview of ice situation on the fairways in text format and (2) Ice сhart by mapping the ice cover extent including information about ice type and thickness. The ice conditions information is required for winter navigation and planning of ice breaking activities in Estonian waters. There is also wide public interest towards the ice situation in Estonia during winter.
The project named „Development of new ice information system” was initiated in 2019 by Estonian Environment Agency. The aim of the project was to make new solutions for the (1) collection and processing of various ice data sources - observations and satellite data, (2) creation of daily ice chart and (3) sharing ice conditions information to the public.
Within the project, an operational processing and visualization of high resolution Sentinel-1,-2 and -3 data was started in the national satellite data center ESTHub. Enhanced visualizations of Sentinel-2 and 3 data were applied for ice detection. To assist the creation of ice charts a machine learning method was developed retrieving the following ice products from EO data: sea ice types (fast ice, strong ice, weak ice, open water), ice features (ridges, leads) and lake ice products.
The ice charting tool that is based on the QGIS desktop application was updated during the project. The developed QGIS based tool automatically integrates operational satellite data and ice observations and thus enables to create a high-resolution, georeferenced ice chart and publish it through the interactive web application.
Since 2021 winter season the new interactive web application with ice information https://jaakaart.envir.ee is available for the public. Users can monitor various high-resolution satellite images on a daily basis from the web interface. In addition, it is possible to view the Estonian ice chart composed by Estonian Weather Service and follow the ice thickness observations from volunteers (citizen science) collected using the mobile application.

The Baltic Sea is a strongly stratified semi-enclosed sea with a large freshwater supply from rivers, net precipitation and high-saline water exchange from the North Sea. In the Danish Straits the water exchange is hampered by bathymetric and hydrodynamic restrictions. The shallow depth yields to highly variable ocean dynamics. The water exchange with the North Atlantic Ocean is restricted by the narrows and sills of the Danish Straits and river outflows. The bottom water in the deep sub-basins is ventilated by major Baltic saltwater inflows. These complex oceanographic conditions are not well described in current model simulations. Moreover, the available in situ data in the region is temporally
and spatially very sparse.

Earth Observation salinity measurements have a great potential to help in the understanding of the dynamics in the Baltic Sea. However, this basin is one of the most challenging regions for the satellite sea surface salinity (SSS) retrieval. The available EO-based global SSS products are quite limited in terms of spatio-temporal coverage and quality. This is mainly due to technical limitations that strongly affect the brightness temperatures (TB), such as the high contamination by interferences and the contamination close to land and ice edges. Moreover, the sensitivity of TB to SSS changes is very low in cold waters and dielectric models present limitations in this low salinity regime.

In the ESA Baltic+ Salinity Dynamics project, new regional SSS products derived from the European Soil Moisture and Ocean Salinity (SMOS) measurements were developed. In this work, we describe the enhanced algorithms used in the generation of SMOS SSS fields. A complete quality assessment with respect to in situ measurements is also presented. Finally, we compare the satellite SSS measurements with a Baltic reanalysis and in situ measurements time series, focusing on the SSS dynamics captured by Baltic+ SSS products and the added-value that can provide for the understanding of the salinity dynamics. Scientific applications identified from the interaction with users working in the Baltic Sea will also be presented.

Earth Observation (EO) provides a wide range of different types of environmental information that is key to the effective planning and implementation of knowledge-based approaches in society. During the last years, Estonian public authorities have strongly supported the greater use of EO derived information to advance sustainable development goals according to their specific needs and challenges. Scientists are increasingly being called upon to provide scientific information to support decision-makers and stakeholders. Four ministries initiated a specified study to introduce satellite data in their areas of responsibility. The following themes were involved:

Prevention and eradication of landscape fires were aimed at the needs of the Estonian Rescue Board (which is the responsibility of the Ministry of the Interior). The results include a more precise fire hazard map, fire propagation algorithm, and landscape damage assessment.

Monitoring of floods and water levels – the information is essential for the Estonian Rescue Board and the Estonian Weather Service (which is the responsibility of the Ministry of the Environment). The results include water height assessment, altimetric data validation and moisture regime of wetland (the latter gives us information about mining operations).

Monitoring of the use of agricultural land was aimed at the needs of PRIA (Agricultural Registers and Information Board), which is the responsibility of the Ministry of Rural Affairs. The results include the Crop identification model methodology with precision estimates and Classification of Estonian plant groups.

Planning and monitoring construction activities was a theme raised by the Ministry of Economic Affairs and Communications. The results include a prototype for monitoring the construction process.

The project results gave new perspectives on using EO derived information to develop public services in Estonia. In addition to the algorithms developed, a plan with specific guidelines for the sustainable implementation of remote sensing was developed. An important step forward has been made to enhance the cooperation between universities, the private sector and public authorities towards developing applications and data management prototypes to make public authorities more efficient in the field of EO.

For sea level studies, coastal adaptation, and planning for future sea level scenarios, regional responses require regionally-tailored sea level information. Global sea level products from satellite altimeters are now available through the European Space Agency’s (ESA) Climate Change Initiative. Through the efforts of ESA-funded projects such as Baltic SEAL, the regionalisation of these mission datasets is now possible. The project established a state-of-the-art altimetry processing chain for extracting sea level measurements, and improved measurement retrieval, in areas with complex coastlines and prone to sea-ice coverage. It developed a new suite of dedicated along-track and gridded sea level datasets for Baltic Sea stakeholders, spanning the years 1995-2019.

Advances in waveform classification, altimetry echo-fitting, expansion of echo-fitting to a wide range of altimetry missions (including Delay-Doppler altimeters), and Baltic-focused multi-mission cross calibration, enabled all mission data to be integrated into a final gridded product, which is now freely available (www.balticseal.eu). Such advances could be exported to other key areas, particularly those with complex, jagged coastlines. In addition, there are opportunities for these developments to be progressed further within the Baltic Sea test region, and beyond. However, a strategic approach is needed to identify advances made, chart their potential future trajectories, and maximise the benefits for society, scientific advancement, and the researchers themselves.

Identifying opportunities for inter-regional knowledge transfer, and applications development, is a complex and delicate process. It must capture and convey the scope, scale, diversity and relevance of advancements, while distilling it into actionable areas. It must also empower and protect the innovators responsible for these developments. The Baltic SEAL consortium used a participatory foreground-mapping approach, coupled with context analysis to frame strategic recommendations. Capacity building within the consortium was also required, to ensure foreground mapping was harmonised across the 5 international partners. The review process also engaged with actors in the wider Baltic + programme, to maximise identified synergies, and as a form of external review. The final product was an actionable Baltic SEAL Roadmap (also available at www.balticseal.eu), which captured the breadth of scientific development achieved, identified future development needs, opportunities and applications, and potential synergies going forward.

This presentation gives a brief outline of the approach used, and aspects which could be integrated as good practice in other initiatives going forward. It then explores the range of recommendations, and synergy areas which were identified under three themes: (i) further advancements needed in the science; (ii) opportunities for Baltic Sea applications synergies, and (iii) opportunities for inter-regional knowledge transfer.

We are beginning to understand that nature is the foundation upon which the global economy is built. Today, nature degradation and exceeding planetary boundaries in relation to natural capital is recognised as a systemic risk to economic stability and resilience (Steffen et al. 2015) with an estimated $44 trillion of economic value threatened by biodiversity decline and ecosystem collapse – equal to over half of the world’s total GDP (World Economic Forum, 2021). Steffen also emphasises the impact of humanity on biodiversity loss. A steep acceleration in degradation is already occurring: around a million species are currently at risk of extinction due to human activity, with declines in biodiversity threatening over 80% of UN Sustainable Development Goal targets (IPBES, UN, 2019). The European Union recently put in place the first phase of its Sustainable Financial Disclosure Regulation, requiring that asset-management companies provide statutory information about their impact on society and environment. Such reporting will require objective data, information and assessment of the impacts of investee company operations in all their aspects.
In the wake of the United Nations Climate Change Conference (2021), the next 12 months are critical for international environmental policy as governments and businesses orient their activities to achieve global commitments. A critical issue facing global leaders of the private and public sector alike is engineering the systemic change required towards a more sustainable relationship between humanity and our planet; one key lever is changing business decision-making. Commitments, including the Glasgow Financial Alliance for Net Zero (GFANZ), which saw over $130 trillion of private capital committed to transforming the economy towards net-zero, underscore the increasing relevance of private sector investment alongside public policy in achieving positive change (Gfanzero.com, 2021). The financial aspects of GHG emission management and security of biodiversity are also closely linked.

Yet although climate is starting to feature in business strategies, disclosures and investment decisions, biodiversity is still largely absent. The situation is changing as governments and businesses begin to recognise the importance of the issue. For example, the 2020 World Economic Forum Global Risks Report ranks biodiversity loss and ecosystem collapse as one of the top five threats humanity will face in the next decade, based on its potential impact on supply chains, food and health. There is both regulatory and internal pressure on companies to report and reduce their impacts on nature (e.g. through the Taskforce on Nature-related Financial Disclosures, and outputs resulting from the Dasgupta Review. Consumer research in 2019 by FNZ, a major investment platform, revealed that 78% of end-investors are keen to know the environmental and societal impact of their investments; understanding their impact on nature was among their top three concerns.

Unfortunately, biodiversity impact measurement lags far behind other areas, including climate change, with limited data to support investment decisions available, and no trusted standard for biodiversity performance measurement that spans the universe of holdings considered by investors in global capital markets. Yet for planning and action to be effective, the development of science-based metrics enabling investors to integrate nature into their risk and due diligence decision-making is crucial. Measuring biodiversity losses and gains, particularly where data are limited, would support tracking and reporting of the net outcomes of human development and nature restoration. This could guide decision-making at the international and national policy levels, as well as internally by corporates and investors (Bull et al. 2020). Satellite data can be a vital component in providing objective and accessible evidence of the impact of companies operations on biodiversity through, for example, the provision of data from which Essential Biodiversity Variables (e.g. Pereira et al 2013, Pettorelli et al 2016) can be defined and measured.

Leclere et al. (2020) demonstrate that if nature is to be restored, conservation action needs to be supplemented by rapid shifts towards sustainable supply chains; Williams et al (2020) give a similar message focussed on agriculture. Pertinent to reducing future pandemic risk, over half of emerging human infectious diseases are linked to land conversion for agriculture (Rohr et al. 2019), and anthropogenic changes to ecosystems are key drivers of zoonotic disease risk (Allen et al. 2016). This research and more suggests that a robust, standardised approach to measuring and reporting corporate impacts on biodiversity is urgently required.

Sustainability reporting is increasingly used by both investors and customers to gain a better understanding of a company’s non-financial risks and opportunities, and informs investment decision making. However, very few financial institutions manage biodiversity or nature-related risks (Refinitiv 2020), though (Mace et al. 2018) argue that finance and business sectors could become drivers of positive change for biodiversity impact across all aspects of investment. Existing research on corporate accounting and accountability suggests that corporate biodiversity accountability is in its infancy (Adler et al. 2017; Addison, Bull, and Milner-Gulland 2019). Yet systematically mispricing nature has resulted in misallocation of capital, especially in land-based sectors, and has exposed the financial sector to nature-related risks (Refinitiv 2020).

It is widely accepted that measuring changes in biodiversity is more complex than measuring climate change (Dasgupta 2020). However, for corporates and financial institutions, there is an increased interest in the role that AI could play in integrating data on both biodiversity impacts and broader ESG data itself into the investment process, improving sustainability assessment and acting as a catalyst for sustainable investing at scale (Generation Investment Management 2019). Investors are now increasingly looking to apply AI to sustainability reporting, as some investment funds and consultancies, including pension funds such as APG and Pension Danmark, already use machine learning in ESG investing (Dohle 2020). In the context of retail investor decision-making, technological innovations from fund platforms will deliver this information into the hands of public long-term savers for the first time at scale.

With the arrival of GPS, satellite remote sensing, and personal computers, the last three decades have witnessed rapid advances in the field of spatially-explicit ecological modelling. (Roberts et al. 2010). The increasing availability of digital images and other satellite based data, coupled with sophisticated artificial intelligence (AI) techniques, presents an exciting opportunity for biodiversity researchers to create new datasets of species observations (August et al. 2020; Garske et al. 2021). But with this innovation has come complexity. Understanding the complex relationships and processes that regulate the world’s balance of biodiversity impacts and dependencies is critical to our understanding of how to mitigate the grand challenges of environmental sustainability. Studies have considered that machine learning can help with this complex problem. (Talbert et al. 2017)
For example, developments in earth observation data (Talbert et al. 2017) coupled with location data for companies and their supply and value chains can be combined with both machine learning and biodiversity science, surfacing new insights into the impacts and dependencies of companies on nature. This enables impact, risk and value-at-risk to be calculated, alongside relevant qualitative detail such as information on controversial activities. Probabilistic causal chain models may then be applied to link the activities of specific businesses or sectors to biodiversity impact in specific locations (with associated uncertainties). With the integration of additional datasets to link supply chain and spatial data with impact, financial institutions may be able to measure the biodiversity impact and risk associated with a given investee company, alongside making comparisons with peers in a given sector and geography. This can be used for a range of applications, including credit risk pricing, disclosure and reporting, portfolio management, risk management, portfolio stewardship.
The convergence of sustainability and digital imperatives is beginning to gain traction in the private and public sectors (George et al. 2019) but has yet to galvanize systematic and rigorous academic research. Exploratory research has seen entrepreneurial actors employing digital technologies to tackle crucial sustainability challenges. They have done so, not only through technological innovation, such as developing digital sustainability tools, but also by developing business models that infuse innovations with new purpose (George et al. 2019). Trends now combine in the actions of entrepreneurial actors employing digital technologies to tackle crucial sustainability challenges through technological innovation (George et al. 2012, 2019).
In tackling the challenge of accurately mapping nature and biodiversity impacts for financial decision-making, the engineering of highly integrated tools and frameworks will be crucial for the implementation of appropriate investment decisions for both public and private actors. This paper will examine the opportunities for novel combinations of earth observation data and machine learning in this domain, identify challenges encountered with efforts, and chart a path forward. If incorporated into our $120trillion global capital markets, it is these tools which could engender a paradigm shift in capital allocations at scale - one that is net-positive for nature. The outcome is beneficial for all: an improvement in biodiversity and more sustainable, and hence financially successful, decision making in companies.

Detailed and complete data on physical assets are required in order to adequately assess environment-related risk and impact exposure and the diffusion of these risks and impacts through the financial system. Investors need to know where the physical assets (e.g. power plant, factory, farm) are located of companies in their portfolios, and what their polluting characteristics are. This is essential to manage these environment-related risks and to channel investments to more sustainable alternatives. At present, data on physical assets is typically incomplete, inaccurate or not released in a timely manner. As a result, key stakeholders including asset owners, asset managers, regulators and policymakers are frequently forced to make crucial decisions with incomplete information.

Accurate and comprehensive global asset-level databases are a prerequisite for meaningful innovation in green and digital finance. They provide the link between the financial system and the “real economy” and allows the wealth of EO datasets and insights that we have available to be made actionable for sustainable finance decision making.

We created a framework to derive a global database of pollutant plants, such as cement, iron and steel, which represent about 15% of the global CO2 emissions. Our solution makes use of state-of-the-art deep learning architectures coupled with Earth observation data. In this talk we will show the technical journey, from the initial feasibility study to the deployment at scale, which brought to the definition of 3,117 cement production assets, accounting for ~90% of global cement production capacity, and 1,598 iron and steel assets, accounting for ~70% of global crude steel production. Specifically, the solution is based on a two-staged approach. Convolutional neural networks are initially used on open access EO data in both a computer vision approach with Sentinel-2 data and a physics-aware one with Landsat-8. This gives a first broader indication of the location of the assets. The precise characterisation is then performed with high-resolution EO imagery from the WorldView missions through collective intelligence. This approach has been tested against a pure machine learning one on both medium-resolution and high-resolution EO data and found of comparable or better accuracy. Several parameters are inferred with this approach: the precise localisation of the assets, their production capacity, the plant type and the production type. An extensive manual validation concluded that the production capacity has been inferred with continent statistics within the 5% error. The training dataset has been built by using public data from Open Street Map and dedicated databases, e.g. Global Cement Directory, and it has been thoroughly verified and refined. We also identified asset ownership through company disclosure of major producers and further verification with the OpenPermID database.

The datasets are now publicly available through the GeoAsset project of the Spatial Finance Initiative, established by The Oxford Sustainable Finance Group, Satellite Applications Catapult, and the Alan Turing Institute: https://www.cgfi.ac.uk/spatial-finance-initiative/geoasset-project/geoasset-databases/

Environmental, Social and Governance (ESG) data are the basis for monitoring impacts or risk, and how they change over time at the asset level and at aggregated levels across enterprises, portfolios or administrative areas. We elucidate to how Earth Observation data and derived products such as Copernicus services along with other geospatial information can be used to assess potential physical, transition or liability ESG impacts and risks.
We suggest a geospatial-based approach to provide ESG relevant insights into a specific asset and call it spatial finance. We focus on spatial ESG data as the basic (fundamental) level. Through combining earth observation (EO), Geographic Information Systems (GIS) with artificial intelligence (AI) we enhance the availability of information in financial systems, and change how risks, opportunities and impacts are measured and managed by financial institutions and civil society. This data renders possible to assess the asset against observational data, such as environmental, climate, governance and social variables. This way, financial decisions shall become more transparent.
Sub-asset monitoring (e.g. power smart meters) or voluntary reporting (e.g. ESG company reports) can be integrated for even greater insights. Merging results of multiple assets at the subsidiary, parent company, portfolio or national, or sector level, provides insights at scales relevant to different financial applications, ranging from project finance to sovereign debt.
Continuous advancements in data processing technologies such as AI, specifically machine learning, computer vision and edge computing, will enable dynamic and timely spatial analysis, both at a global scale and at local to regional scales. The current ESG data affordances require already some modelling, but predictive modelling capabilities will expand spatial finance applications to new markets. The authors believe that a harmonisation of the methodologies and increased availability of asset-level data will overcome the current ESG data bottleneck and will allow translating existing environmental and climate datasets into suitable indicators for financial sector use.
The EU taxonomy regulation and green banking regulations from bodies like EBA accelerate the developments in this field. The EU taxonomy is a classification system, establishing a list of environmentally sustainable economic activities. It shall play an important role helping the EU scale up sustainable investment and implement the European green deal. The EU taxonomy provides companies, investors and policymakers with appropriate definitions for which economic activities can called environmentally sustainable. It shall protect private investors from greenwashing, help companies to become more climate-friendly and help shift investments where they are most needed.
In addition to the legislative stimuli, technologies mature and more sophisticated applications become available and the amount of studies based on spatial analysis expand rapidly. In principle, asset managers and banks are already able to make decisions and to report on ESG risks in a granular way but so far not in a harmonized way. A comparison between different rating systems shall become possible when all technical annexes of the EU taxonomy regulation will be available.
In the full version of this article, we will report on a study in Austria that creates methods and prototype tools to assess environmental exposures at asset (parcel) level. We exploit the increased temporal and spatial resolutions and the availability of EO to assess Tier 3 indicators on soil sealing, biodiversity and climate change risks. We link EO techniques to the locations of private and company assets and develop methods for assessing the exposure to deforestation, biodiversity loss and water risk over time and across sectors of activity. Tier 3 is defined as high-level asset-level insights covering the full extent of the specific assets, resting primarily on the comparison of asset datasets (location of factories, farms, mines) against spatial datasets and remote sensing insights sourced independently. Insights generated by GIS define the proximity of commercial assets to other assets such as World Heritage Sites or Protected Areas, rivers, wetlands, endangered species, and indigenous areas. Secondly, we combine this data with other modelled layers and EO products to estimate risk and impact aspects of the asset, such as water risk of the site, nearby deforestation or carbon emissions. In addition, we show exemplarily utilization from Tier 4 datasets for Tier 3 and vice versa, while aggregating or dis-aggregating or disclose insights of the status of an asset. We use a range of data from freely available open datasets to highly detailed data from commercial companies. It turns out that sustainable investors rely on the quality and relevance of environmental (the “e” in ESG) data but the availability of some observational data is the bottleneck in such a methodology. As of today, even the most transparent companies disclose almost no data on their environmental impact and exposures. Therefore, combining explicitly spatial data with spatial data science will help to replace self-disclosure based environmental sustainability data with a fact-based, objective, reliable and timely assessment. We conclude that the tremendous improvement and increased availability of EO data offer options to derive essential data at asset level addressing EU taxonomy aspects such as land use, soil sealing, biodiversity or water data.

From the United Nations Decade on Biodiversity 2011 - 2020, a new consciousness on ecosystem-friendly investments has evolved in the financial world, finding a regulatory manifestation in the EU taxonomy for sustainable finance. New metrics and tools have been developed that allow for the assessment of businesses, economic sectors, or entire portfolios with regard to their surrounding ecosystem services and natural capital assets. Most existing initiatives focus on the impacts of economic activities on ecosystems and local biodiversity. To close the gap, our consortium is developing a prototypical platform that allows investors and financial institutions to assess biodiversity-related risks towards their portfolio, using EO technology and remote sensing based data products as well as available company-specific supply chain and asset level data from publicly listed companies.
Users will be able to upload their portfolio composition, for which different components of biodiversity-related risk are automatically assessed. These are in particular physical risk, reputational risk, regulatory risk, and market risk. This presentation focuses on the physical risk module, where EO data products represent a valuable source of information. The module builds on fundamental work done in the ENCORE project, in which the relationships between drivers of environmental change, natural capital assets, ecosystem services and production processes have been described exhaustively and weighted by economic sector. Additionally, established databases containing information on the status of natural assets, e.g. the WRI Aqueduct 3.0 dataset on the spatial distribution of water-related risks, are used as a baseline. Up-to-date EO data products are then used to assess drivers of environmental change to investigate dynamics and trends, and to identify potential threats to the natural capital assets and the provision of corresponding economy-relevant ecosystem services. Here, we focus on drivers responsible for biodiversity loss, for example by using annual global land cover data from the Copernicus Global Land Service to assess habitat modification due to land cover change. The spatial scale of the analysis is determined by the granularity of available data on publicly listed companies. If asset-level data is available, e.g. coordinates of specific production sites, drivers and ecosystem services can be assessed on a location-specific level, such as catchment areas or certain radii around the locations. For such “spotlight” analyses, further high-resolution options are envisaged that employ Sentinel-1/2 data and derived products. If the available company data does not contain specific location information, relevant statistics can be calculated on country or state level or they can be derived from official sources. Depending on the respective economic sector, an importance weighting of ecosystem services is performed, and an overall indicator for physical risk of the entire portfolio is calculated from all company-specific assets.
The first prototype tool will contain routines for the physical risk calculation for a subset of those drivers and ecosystem services with the highest relevance to biodiversity and economic impact (e.g. the provision of surface water as an ecosystem service is crucial to many production processes). First models of driver and ecosystem service assessment at different spatial scales are currently being developed and consolidated in a proof of concept, taking into account a variety of spatial data sources. First results indicate that continuity and global coverage are the most relevant data requirements to develop and continuously apply a stable methodology. Therefore, existing and future Copernicus data products represent an essential input to this project.
Perspectively, this project will advance the understanding of how biodiversity-related risks affect financial institutions and will thereby a) contribute to the divestment from assets with high biodiversity-related risks; b) incentivize investments in economic activities that substantially contribute to biodiversity protection and ecosystem restoration; and c) provide a starting point for shareholder engagement on biodiversity issues.

Cloud-native geospatial technologies are key to scaling the geospatial data sector, and for the efficient discovery and computation of relevant data. However, they are built in a centralized computing paradigm, with roots still in the client-server model. Data networks designed in this way are brittle - links can break, file content can be changed unbeknownst to users, and data must be held by trusted custodians who may abuse their role as maintainers of server systems holding geospatial data.

A more secure, resilient and user-centric vision for the Internet is evolving with Web3. Decentralized identifiers, peer-to-peer storage and transfer, and content identifiers resolve the most critical fragilities that exist in the incumbent web.

The geospatial data sector is not immune to these problems - in fact, the ecosystem is rife with inefficiencies. Many organizations hold multiple versions of the same reference spatial datasets on various internally-operated servers. Each of these servers requires computing resources and skilled workers to maintain. It seems unlikely that each of these datasets is kept up to date based on update release cycles, meaning the reference data upon which the organizational data is overlaid is not current. Deep architectural wastefulness pervades geospatial data storage systems in the public, private and third sectors worldwide.

The Web3-Native Geospatial vision seeks to integrate the learnings of the incumbent geospatial data practitioners with the principles underpinning the design of the decentralized web.

- Data must persist — we cannot try to resolve a mission-critical dataset a few years after its creation only to receive a 404 error. This is especially true for spatial finance applications where decisions dealing with large monetary values are made based on insights derived from geospatial data.
- Datasets must be verifiable — all parties can have cryptographic confidence when accessing a dataset around the world and into the future that they are looking at the same information as others.
- Data networks must be antifragile, — they must be self-healing and resilient to the failure of any actor.
- The right to privacy must be built in on the technical layer, not added on top of system design at the policy layer.

These are challenging problems to solve, and the community is early in the process of developing the standards, tools and protocols that will unlock the potential of the decentralized, location-based web.

In this talk, the core team from the Astral Protocol will outline their vision for a Web3-native geospatial data architecture, including data storage and computation systems that are decentralized and fault tolerant, and in which all participants are cryptoeconomically-aligned. The Astral team will touch on data storage systems they are designing based on IPFS, Filecoin, Arweave and Ceramic, and will share insights into decentralized geospatial data processing and discovery systems including dClimate, Ocean Protocol and Algovera AI. They will also touch on their early research into privacy-preserving geospatial technologies.

These technologies are designed to interoperate with distributed ledgers, smart contracts and digital currencies, with an eye towards underpinning the emerging regenerative finance ecosystem being built on public blockchains.

Glacial isostatic adjustment in Greenland is the result of Holocene ice thickness changes in North America and Greenland and late Holocene deglaciation in Greenland. There is also evidence for substantial lateral variation in viscosity, such as below viscosity in East Greenland possibly due to the passing of the former Iceland hotspot. Here we investigate how viscosity changes influence the uplift rates in Greenland, and we focus on the contribution of North America and the effect of stress-dependent viscosity.

We derive viscosity maps from temperature estimates which are obtained in two ways. One is based on gravity, seismic and petrology data (WINTERC-G). The other is based on seismic velocity anomalies from a new seismic model for the north Atlantic (NAT2020) that are converted to temperature anomalies. Viscosity maps are dependent on parameters in olivine flow law. From the various 3D maps, the ones with best fit to GPS velocities in East Greenland are selected. All models lead to a lithosphere thicker than 100 km, with a viscosity below that < 1019 Pa s.

We show that the viscosity estimates change in time, with 1 order of magnitude lower viscosity between 12 and 8 kyear before present. Stress due to North American deglaciation has negligible contribution to these viscosity changes, so these are caused by ice melt in Greenland. A relative uniform forebulge effect across Greenland is found of 1-2 mm/year. Both lateral viscosity variation and stress-dependent viscosity have a significant effect on present-day uplift rates.

The Aerosol Index (AER_AI) as measured by the Tropospheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor (S5P) platform maintains heritage with aerosol index data records from other satellite instruments including TOMS (EP and Nimbus-7), GOME(-2), OMI and OMPS. The operational TROPOMI AER_AI dataset is available from May 2018 through to the present. It is useful not only for tracking ultraviolet (UV) absorbing aerosol plumes of desert dust, volcanic ash, and smoke from biomass burning but also for monitoring the quality of the TROPOMI Level 1b (L1b) data since the AER_AI calculation is very sensitive to the absolute calibration of irradiance and radiance. This work will also include a description of the impacts on AER_AI due to the observed, wavelength-dependent degradation in the TROPOMI measured irradiance and radiance. Results of recent updates in the L1b data which incorporate corrections for these degradation-driven features will be discussed.

With a nadir pixel size of 3.5 x 5.5 km, TROPOMI’s high spatial resolution presents specific challenges as non-Lambertian cloud features, cloud shadows, and 3-D effects of clouds are now visible in the TROPOMI AER_AI data. An overview of plans to address these features in future AER_AI updates will be given including a description of planned implementation of a new AI datafield utilizing two rather than one LER surfaces-- a so-called bi-reflector approach.

Because TROPOMI AER_AI contributes to the continuation of aerosol index measurements from other missions, it is important to compare TROPOMI to other data records. OMPS and OMI data will be used in case studies to lend insights about known biases in the TROPOMI AI dataset. Comparisons will be conducted for several aerosol plume and scene types.

FP_ILM algorithm applied to UVN sensors for the retrieval of GE_LER climatologies for UV/VIS trace gases and clouds

Ana del Águila(1)*, Pascal Hedelt(1), Klaus-Peter Heue(1),(2), Ronny Lutz(1), Víctor Molina García(1), Fabian Romahn(1), Jian Xu(1),(3), Ka Lok Chan(1),(4), Diego Loyola(1)

(1) Remote Sensing Technology Institute, German Aerospace Center (DLR), 82234 Weßling, Germany
(2) Technical University of Munich (TUM), 80333 Munich, Germany
(3) National Space Science Center, Chinese Academy of Sciences, Beijing, China
(4) Rutherford Appleton Laboratory Space, Harwell Oxford, United Kingdom

The accurate retrieval of trace gases, aerosols and clouds from UVN sensors require precise information on the surface properties at global scale which are traditionally obtained from Lambertian Equivalent Reflectivity (LER) climatologies. New satellite missions usually use LER climatologies based on previous missions encountering a number of drawbacks, such as: (a) lower spatial resolution, (b) different satellite-viewing geometry dependencies or (c) strong differences from the actual surface conditions under snow/ice conditions.
The accurate knowledge of surface reflectance at global scale is of great importance since the neglection of surface anisotropic effects can lead to sources of error in the retrievals. The well-stablished full-physics inverse learning machine (FP_ILM) algorithm [1] allows to retrieve the geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) from UVN sensors in a monthly basis. The FP_ILM algorithm consists of a training phase with four steps: (1) forward model, (2) smart sampling, (3) feature extraction and (4) machine learning; and a retrieval phase. The GE_LER retrieval is solved using neural networks trained with radiative transfer model simulations with inputs similar to the AMF calculations [2]: DOAS fitting polynomial coefficients, fitted trace gas slant column amounts and satellite viewing geometry.
The FP_ILM GE_LER algorithm has been applied to TROPOMI/S5P measurements and it is being used for operational retrieval of total ozone and cloud properties. The TROPOMI GE_LER climatology results for the fitting windows in the UV/VIS corresponding to SO2 (311-325 nm), O3 (325-335 nm), HCHO (328.5-346 nm), NO2 (405-465 nm), H2O (430-466.5 nm) and clouds (755-771 nm) are presented. In addition, the retrieved GE_LER climatologies with grid resolution 0.1ºx0.1º from this study are compared with climatological GOME-2 and OMI LER data with grid resolutions of 0.25ºx0.25º and 0.5ºx0.5º, respectively. The results of the comparison show effective high-resolution GE_LER climatologies for UV/VIS trace gases and the operational UVN cloud product.

[1] Loyola, D. G., Xu, J., Heue, K.-P., and Zimmer, W.: Applying FP_ILM to the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) daily maps from UVN satellite measurements, Atmos. Meas. Tech., 13, 985–999, https://doi.org/10.5194/amt-13-985-2020, 2020.
[2] Chan, K. L., Valks, P., Slijkhuis, S., Köhler, C., and Loyola, D.: Total column water vapor retrieval for Global Ozone Monitoring Experience-2 (GOME-2) visible blue observations, Atmos. Meas. Tech., 13, 4169–4193, https://doi.org/10.5194/amt-13-4169-2020, 2020.

The validation of water vapour isotopologue satellite data products (such as SRON's scientific TROPOMI HDO product and the S5P+I H2O-ISO product) is currently hindered by known biases in ground-based Fourier transform infrared (FTIR) reference data. For example, the HDO product by the Total Carbon Column Observing Network (TCCON) is lacking a so-called in situ correction factor that corrects biases due to uncertainties in the spectroscopy which tend to be highly reproducible. In order to derive this calibration, in situ profile measurements concurrently to TCCON observations are necessary, however no such profile measurements are available to date. Conventional in situ techniques utilize aircraft or large balloon platforms that are costly and need significant effort and infrastructure. Instruments for aircraft have to go through an extensive permission procedure. Large balloons can only be launched at a few dedicated launch sites and cannot be steered, making them unsuitable for reference measurements at many places such as most ground-based remote sensing stations. Due to these limitations, profile measurements of water vapour isotopologues are sparse. In the project Water vapour Isotopologue Flask sampling for the Validation Of Satellite data (WIFVOS), a novel balloon-borne instrument for small meteorological balloons (< 20kg payload) capable of measuring profiles of water vapour isotopologues is developed. The new instrument is much more cost-effective and flexible than previous ones. It is based on a flask-sampling technique proven on drones that is adapted to lower pressures and water vapour mixing ratios up to the tropopause. Air is sampled in flasks at pre-programmed pressure levels during descent of the payload. Sampling on descent avoids potential contaminations by humidity brought up by the balloon. After landing and recovery, the samples are analysed with a cavity-ringdown spectrometer. This contribution presents the instrument design, the calibration at a laboratory in Bergen and first results from a field campaign with concurrent TCCON measurements at Sodankylä in spring 2022.

FTIR Team: Carlos Aquino Bauer 2, Thomas Blumenstock 3, Martine De Mazière 1, Michel Grutter 4, James Hannigan 5, Nicholas Jones 6, Rigel Kivi 7, Emmanuel Mahieu 8, Maria Makarova 9, Isamu Morino 10, Isao Murata 11, Tomoo Nagahama 12, Justus Notholt 13, Ivan Ortega 5, Mathias Palm 13, Markus Rettinger 14, Amelie Röhling 3, Dan Smale 15, Wolfgang Stremme 4, Kim Strong 16, Youwen Sun 17, Ralf Sussmann 14, Yao Té 18, Pucai Wang 19, Tyler Wizenberg 16

1 Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium; 2 Instituto Federal de Educaçao, Ciência e Tecnologia de Rondônia (IFRO), Porto Velho, Brazil; 3 Karlsruhe Institute of Technology (KIT), IMK-ASF, Karlsruhe, Germany; 4 Universidad Nacional Autónoma de México (UNAM), 04510 Mexico City, México; 5 National Center for Atmospheric Research (NCAR), Boulder, CO, USA; 6 Centre for Atmospheric Chemistry, University of Wollongong, Wollongong, Australia; 7 Finnish Meteorological Institute (FMI), Sodankylä, Finland; 8 Institute of Astrophysics and Geophysics, Université De Liège, Liège, Belgium; 9 Saint Petersburg State University, Atmospheric Physics Department, St Petersburg, Russia; 10 National Institute for Environmental Studies (NIES), Tsukuba, Japan; 11 Graduate School of Environment Studies, Tohoku University, Japan; 12 Institute for Space-Earth Environmental Research (ISEE), Nagoya University, Nagoya, Japan; 13 Institute of Environmental Physics, University of Bremen, Bremen, Germany; 14 Karlsruhe Institute of Technology (KIT), IMK-IFU, Garmisch-Partenkirchen, Germany; 15 National Institute of Water and Atmospheric Research Ltd (NIWA), Lauder, New Zealand; 16 Department of Physics, University of Toronto, Toronto, Canada; 17 Hefei Institute of Physical Sciences, Chinese Academy of_Sciences (CAS), Hefei, China; 18 LERMA-IPSL, Sorbonne Université, CNRS, PSL Research University, 75005 Paris, France; 19 Institute of Atmospheric Physics, Chinese Academy of Sciences (CAS), Beijing, China

Within the NIDFORVal project (S5P NItrogen Dioxide and FORmaldehyde VALidation using NDACC and complementary FTIR and UV-Vis DOAS ground-based remote sensing data), we have successfully obtained a harmonized HCHO data set from the FTIR network (Vigouroux et al., 2018). This has led to a comprehensive validation of the S5P HCHO product (Vigouroux et al., 2021), showing the strength of using more than 20 sites covering clean and polluted conditions. Within this NIDFORVAL project, the total, tropospheric, and stratospheric NO2 S5P products have been validated, against Direct-Sun, MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy), and zenith-sky DOAS measurements, respectively (Verhoelst et al., 2021).
Fourier Transform Infrared (FTIR) instruments have the capability to measure NO2, with a sensitivity mainly located in the stratosphere (e.g., Hendrick et al., 2012, Bognar et al., 2019). However, only a few FTIR sites exploited this until now, using different retrieval settings. For the present work, we have optimized the NO2 retrieval settings and applied them consistently to the whole FTIR network (mostly from NDACC, Network for the Detection of Atmospheric Composition Change, but also including additional NDACC candidate sites and TCCON sites operated in NDACC mode). We have obtained a unique harmonized NO2 data set covering 25 FTIR sites, ensuring consistency of the results if used as reference data for validation. This stratospheric NO2 data set can complement the zenith-sky DOAS data. Indeed, the zenith-sky DOAS observations are made during sunset and sunrise which imposes the use of a photochemical box model to adjust the observations to the time of the TROPOMI overpasses, while the FTIR measurements are made during the whole day, allowing direct comparison between measurements that are collocated in time.

In this presentation, we will show the validation results of more than three years of S5P stratospheric NO2 data, allowing robust statistics on the comparisons, and the seasonal cycles to be compared at 25 FTIR sites globally distributed. Diurnal cycles comparisons can also be obtained at high latitude sites. Conclusions about the accuracy and the precision of the S5P stratospheric NO2 products will be drawn and compared to the ones obtained using Zenith-sky DOAS data (Verhoelst et al., 2021).


Bognar et al.: Updated validation of ACE and OSIRIS ozone and NO2 measurements in the Arctic using ground-based instruments at Eureka, Canada, JQSRT, 238, https://doi.org/10.1016/j.jqsrt.2019.07.014, 2019.
Hendrick et al.: Analysis of stratospheric NO2 trends above Jungfraujoch using ground-based UV-visible, FTIR, and satellite nadir observations, Atmos. Chem. Phys., 12, 8851–8864, https://doi.org/10.5194/acp-12-8851-2012, 2012.
Verhoelst et al.: Ground-based validation of the Copernicus Sentinel-5P TROPOMI NO2 measurements with the NDACC ZSL-DOAS, MAX-DOAS and Pandonia global networks, Atmos. Meas. Tech., 14, 481–510, https://doi.org/10.5194/amt-14-481-2021, 2021.
Vigouroux et al.: NDACC harmonized formaldehyde time series from 21 FTIR stations covering a wide range of column abundances, Atmos. Meas. Tech., 11, 5049–5073, https://doi.org/10.5194/amt-11-5049-2018, 2018.
Vigouroux et al.: TROPOMI–Sentinel-5 Precursor formaldehyde validation using an extensive network of ground-based Fourier-transform infrared stations, Atmos. Meas. Tech., 13, 3751–3767, https://doi.org/10.5194/amt-13-3751-2020, 2020.

In the framework of the BAQUNIN (Boundary-layer Air Quality-analysis Using Network of Instruments, https://www.baqunin.eu) ESA project, three lidar system characterised by different design and measurement modes are operated in an urban (Rome downtown) and a rural (Montelibretti) sites of the Tiber Valley. This experimental setup is particularly useful for the evaluation of the impact of different environmental conditions (e.g. surface reflectivity, pollution load) on atmospheric composition satellite products. In this contribution we will discuss in detail the methods applied and the preliminary results of the validation of TROPOMI Level-2 Cloud products (offline), with particular focus on the estimatedcloud boundaries (top/bottom).
Two Lidar systems located in the Atmospheric Physics Laboratory (APL) of the Physics Department of Sapienza University (Rome downtown). At APL, a powerful multi-wavelength and multi-polarisation Lidar (MWL-LIDAR) has been designed and assembled using both custom made and commercial devices, while the controlling software has been developed in house.The system includes a large power pulsed laser, emitting pulses at 355, 532,1064 nm wavelengths, four receivers and twelve acquisition channels. MWL-LIDAR can be operative in no rain day and, for what concerns the Raman channels, in night conditions. In addition, a Raymetrics Aerosol Profiler (RAP) system, emitting at 1064 nm, is installed on the roof of APL a few meters apart from the MWL-LIDAR. Differently from the latter, RAP operates continuously (24/7). For the APL systems, the reference cloud boundary data are obtained from high time/space resolution Lidar range-corrected signal, providing very accurate estimates of the position of the cloud edges. The third system is a Vaisala CT25K ceilometer, located in the “Liberti” experimental campus of CNR-IIA, about 25 km distance from APL. This ceilometer is also operated in continuous mode and can provide reliable cloud bottom altitudes and, in case of optically thin clouds, the cloud top altitudes.
The TROPOMI Level-2 offline products are gathered from the ONDA-DIAS system using a dedicated selection/extraction tool called TROPEX (TROPomiEXtraction) developed in the context of the BAQUNIN project.
The validation approach consists in selecting the TROPOMI products for which the satellite observing view around the selected sites is almost nadir (viewing zenith angle < 10 degrees) and a variable horizontal search radius. The inter-comparison results are finally analysed in terms of satellite viewing geometry and cloud fraction, of relative distance from the site, and, in the case of APL, of the aerosol load retrieved from AERONET and EUROSKYRAD collocated systems (also part of BAQUNIN).

Antarctic geothermal heat flow (GHF) has often been derived indirectly from geophysical data with assumptions about a simplified and undifferentiated lithosphere, which resulted in weakly constrained and inconsistent models. From other continents, we know that thermal parameters and GHF can exhibit large spatial variations depending on geology and tectonic history.
Combining gravity and magnetic data in a joint inversion approach yields information on the crustal structure of East Antarctica and possible geological features become more evident. Both datasets are combined through a coupling method which increases the mutual information to get similar and statistically compatible inversion results. Therefore, we minimize data misfit and model similarity under the coupling constraint. The results show matching features of high magnitude density and susceptibility anomalies. Prominent structures are visible in NW – SE direction along the edge of the Mawson craton and at the presumed Australo-Antarctic and Indo-Antarctic terrane boundary.
With the structural similarity and resulting parameter relationship between inverted susceptibility and density, we aim to define a spatially variable heat production map, which in turn would lead to improved GHF estimates. For this, we rely on existing petrophysical and geochemical databases to correlate and confine thermal parameters with our results.

The observed secular variation of the geomagnetic field is assumed to reflect the convective dynamics of the Earth’s fluid outer core. Time-dependent models of the field are used to invert for the fluid flow just beneath the core surface. However, it is unclear how well rapid core motions on interannual or shorter time scales can be resolved with current geomagnetic field models.
To address this question, we present a study in which we use a state-of-the-art numerical geodynamo simulation that exhibits such rapid dynamics. From the simulation data, we generate synthetic magnetic field observations with a temporal and spatial distribution equivalent to ground and satellite records. We then construct a magnetic field model by solving the inverse problem constrained by this synthetic data. The obtained model is in turn inverted for the core surface motions. Based on the comparison of the inverted flow with the reference flow provided by the geodynamo simulation, we discuss how accurately we can expect to recover the Earth's core surface motions when applying the same methods to geomagnetic records. In particular, we focus on whether we can detect hydromagnetic waves such as geostrophic torsional Alfvén waves (torsional oscillations) and quasi-geostrophic Alfvén waves, the latter of which have been associated with geomagnetic jerks.

Due to improvements in earth-system modelling as well as to the increasing accuracy of geodetic observation systems, there is a rising demand for improved deformation and load-correction models. Usually, load deformations are quantified applying load Love numbers or Green's functions which are based on one dimensional (1D) parameterisations of the earth's structure. Improvements due to local deviations in deformational response can be achieved by adjusting the 1D parameterisation to the region of interest (e.g., Dill et al., 2015). Drawback of this procedure is that a specific deformation model holds only for a specific region and, therefore, it cannot be applied consistently to large scale processes like ocean-tide loading or when deriving global quantities like the geocentre motion. In a recent publication (Huang et al., 2021), we discussed the impact of a laterally heterogeneous anelastic mantle structure on ocean-tide loading and its feedback on ocean-tide dynamics applying the Dynamic ocean-tide model TiME (Sulzbach et al., 2021). Therein, we limited the forcing setup to the M2 ocean tide.

In this study which will be presented also in Huang et al. (2021, in prep.), we extend the model set up. In addition to mantle anelasticity, we consider additional structural features inside the lithosphere like sediment layers, differences in crustal composition and variations especially between continental and oceanic lithosphere. We discuss the impact on loading at selected sites as well as the feedback to tidal dynamics, and extend the forcing to the fortnightly Mf tide. Furthermore, the comparison of two different lithosphere models, WINTERC-G and LITHO1.0, resulted already in differences of up to 1 mm concerning tidal deformations. This result together with the interplay between different structural features of lithosphere and mantle demands a careful choice of the appropriate lithosphere structure.

This study contributes to the national Earth system modelling initiative, ESM Innovation Pool and PalMod, and is considered in the GRACE-FO project within the ESA Third Party Mission Program.

Lit.:
Dill R, Klemann V, Martinec Z, Tesauro M, 2015. Applying local Green's functions to study the influence of the crustal structure on hydrological
loading displacements, J. Geodyn. 88:14-22. https://doi.org/10.1016/j.jog.2015.04.005
Huang, P., Sulzbach, R. L., Tanaka, Y., Klemann, V., Dobslaw, H., Martinec, Z., Thomas, M. (2021). Anelasticity and lateral heterogeneities in Earth's upper mantle: Impact on surface displacements, self-attraction and loading and ocean tide dynamics. J. Geophys. Res.: Solid Earth, 126:e2021JB022332. https://doi.org/10.1029/2021JB022332
Sulzbach, R., Dobslaw, H., Thomas, M. (2021). High-resolution numerical modeling of barotropic global ocean tides for satellite gravimetry. J. Geophys. Res.: Oceans, 126:2020JC017097. https://doi.org/10.1029/2020JC017097

Several alternative gravity forward modelling methodologies and associated numerical codes with their own advantages and limitations are available for the Solid Earth community. With the upcoming state-of-the-art lithosphere density models and accurate global gravity field data sets it is vital to understand the opportunities and limitations of the various approaches. In this paper, we discuss the four widely used techniques: spherical harmonics, tesseroid integration, triangle integration, and hexahedral integration approach. A constant density shell benchmark shows that all four codes can produce similar gravitational potential fields suitable for reproducing satellite-acquired gravity data. Two additional shell tests were conducted with more complicated geological structures: lateral varying density structures and a Moho density interface between crust and mantle. The differences were all below 1.5 percent of the modeled gravity signal, where the tesseroid and global spherical harmonics approach produced the most similar results (less than 0.3 percent).

To examine the usability of the forward modelling codes for realistic geological structures, we use the global lithosphere model WINTERC-G constrained, among other data, by satellite gravity field data computed using a GSH approach. Short-wavelength noise is present between the spectral and tesseroid forward modelling approaches likely related to the different way in which the spherical harmonic analysis of the varying boundaries of the mass layer is performed. The latter produces small differences especially at sharp interfaces, introducing mostly short-wavelength differences. Nevertheless, both approaches result in accurate solutions of the potential field with reasonable computational resources. Differences below 0.5 percent are obtained, resulting in residuals of 0.076 mGal standard deviation.

The other two spatial forward modelling schemes (triangle-based and hexagonal-based) have more difficulty in reproducing similar gravity field solutions. They need to go to unrealistic high resolutions, resulting in enormous computation efforts. The biggest issue for the triangle approach is the characteristic pattern in the residuals that is related to the grid layout. Increasing the resolution and filtering allows for the removal of most of this erroneous pattern, but at the expense of higher computational loads with respect to the other codes. The hexahedron-based code performs worst in the forward modelling of the gravity signature of a density contrast of a depth-varying boundary. Care must be taken with any forward modelling software as the approximation of the geometry of the WINTERC-G model may deteriorate the gravity field solution, if the limitations of this model are not taken into account.

As of today a series of low Earth orbiting satellites (with the finished missions Oersted, Champ, CryoSat-2 and currently Swarm) have been almost continuously monitoring the geomagnetic field for about 2 decades. Using these observations along with ground-based measurements, we infer a model of the time-varying magnetic field above the core surface, dynamically related to core surface motions, using the pygeodyn data assimilation algorithm [1,2]. Assimilation of magnetic data is performed using an ensemble Kalman Filter (EnKF) where the forward stochastic model is constructed upon spatio-temporal statistical information extracted from a dynamo simulation of the latest generation [3]. Our algorithm is able to collect data presenting varying temporal coverage at any point in space. Following an augmented state approach, our re-analysis gives access to the core flow together with errors of representativeness (primarily composed of subgrid induction). Both contribute significantly to the observed secular variation (rate of change of the field). On top of ground-based observatory records, observations consist of geomagnetic virtual observatory series that interpolate magnetic data in a set of fixed locations at satellite altitude in four-monthly bins [4, 5]. These data are corrected as much as possible from contributions of the lithospheric field and external sources. From these the secular variation is computed as annual differences of these 4-monthly means, without further smoothing.
Based on a finite ensemble size, EnKF estimates may suffer from biases, such as ensemble collapse, or noise introduced by spurious correlations within the ensemble state members. In order to avoid these problems, we modify the canonical EnKf algorithm [6]. Rather than using localization (awkward for an application in the spectral domain) or ad hoc covariance inflation, we clean spurious correlations induced by the finite sampling size using the graphical lasso algorithm (“G-LASSO”, [7]). The method requires no fine tuning, as the extra parameter introduced by G-LASSO is estimated by cross-validation. This novel implementation first reduces significantly spurious high frequency variations otherwise introduced in the field and flow changes, in link with gaps present in the datasets, either in time towards the end/start of satellite missions, or in space towards high latitudes at epochs where no night-time data are available. G-LASSO furthermore enables the use of information contained in cross-correlations between in particular the core surface flow and errors of representativeness. These latter, also present in geodynamo simulation, are crucial to exploit as much as possible the information at interannual periods, of particular interest given the two decades covered by satellite data.
Our magnetic models are consistent with previous models projected in time onto splines and regularized, such as CHAOS-7 [8], although exhibiting some higher frequency signals linked with the absence of smoothing. Low order Gauss coefficients, particularly sensitive to high latitude patterns, do not show obvious signs of high-frequency oscillations, as sometimes seen in link with external field leakage. Core flows are dominated by long periods, characterized by the eccentric westward gyre [9]. Transient motions at first order satisfy to the quasi-geostrophic constraint, and are more intense toward the equator. We recover interannual Magneto-Coriolis modes of period 7 years discovered recently from the analysis of Gauss coefficients data from CHAOS-7 [10]. Our approach calls for further investigation of core flow variations on shorter periods.
[1] Huder, Gillet & Thollard, pygeodyn 1.1. 0: a Python package for geomagnetic data assimilation. Geoscientific Model Development, 12(8), 3795-3803, https://doi.org/10.5194/gmd-12-3795-2019 (2019)
[2] Gillet, Huder & Aubert, A reduced stochastic model of core surface dynamics based on geodynamo simulations. Geophys. J. Int., 219(1), 522-539, https://doi.org/10.1093/gji/ggz313 (2019)
[3] Aubert & Gillet, The interplay of fast waves and slow convection in geodynamo simulations nearing Earth’s core conditions, Geophys. J. Int., 225, 1854–1873, https://doi.org/10.1093/gji/ggab054 (2021)
[4] Hammer, Finlay & Olsen, Applications for CryoSat-2 satellite magnetic data in studies of Earth's core field variations, Earth Planets Space 73, https://doi.org/10.1186/s40623-021-01365-9 (2021)
[5] Hammer, Finlay & Olsen, Secular Variation Signals in Magnetic Field Gradient Tensor Elements derived from satellite-based Geomagnetic Virtual Observatories, Geophys. J. Int. (in revision)
[6] Evensen, The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean dynamics, 53(4), 343-367.2003, https://doi.org/10.1007/s10236-003-0036-9 (2003)
[7] Friedman, Hastie & Tibshirani, Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432-441, https://doi.org/10.1093/biostatistics/kxm045 (2008)
[8] Finlay et al, The chaos-7 geomagnetic field model and observed changes in the south atlantic anomaly, Earth Planets Space, 72(1), 156, https://doi.org/10.1186/s40623-020-01252-9 (2020)
[9] Pais & Jault, Quasi-geostrophic flows responsible for the secular variation of the Earth's magnetic field. Geophys. J. Int., 173(2), 421-443, https://doi.org/10.1111/j.1365-246X.2008.03741.x (2008)
[10] Gillet et al, Satellite magnetic data reveal interannual modes in Earth’s core, Proc. Nat. Acc. Sci. (in revision)

Models of glacial isostatic adjustment (GIA) simulate the viscoelastic response of the solid Earth to surface loading induced mainly by mass re-distribution between ice and ocean during the last glacial cycle. These models predict relative sea-level change and surface deformation. The GIA component of present-day uplift is responsible for crustal deformation at rates over 10 mm/year in areas such as Churchill (Canada) and Angermanland (Sweden). As the building blocks of such GIA models have a degree of uncertainty, the model output needs to be validated against observational data. Conventionally, such a validation has been done by employing paleo and recent observations such as sea level data, surface deformation data, and gravity data, which feature errors of their own. Since sea level observations sparsely cover coastal areas and GIA inference from satellite observations is challenging, in this work, we validate displacements predicted by a new GIA model, VILMA-3D, by utilizing GNSS-derived vertical land motion. VILMA-3D solves the sea-level equation accounting for the redistribution of water between ice and ocean, deformation and gravitational effects, rotational feedback, floating ice and moving coastlines. We have created a GIA model ensemble using geodynamically constrained 3D Earth structures derived from seismic tomography to consider more realistic lateral variations in the GIA response. For the validation of VILMA-3D, we employ a multi-analysis-centre ensemble of GNSS station and geocentre motion coordinate solutions that are currently assimilated into ITRF2020, where similar data processing strategies were adopted. In estimating GNSS-derived velocities, tectonic and weather signatures were reduced, the former by the estimation thereof and the latter by employing non-tidal geophysical loading displacement models developed at the GFZ. The trend signal is extracted from these GNSS time series with the STL method (seasonal-trend decomposition based on Loess). Vertical deformation rates from stations in regions where the GIA signal exhibits large gradients is crucial for the GIA model validation. To overcome the obstacle imposed by the lack of GNSS station clusters in such regions, we employ a weighting scheme that involves the network density and the cross-correlation of the station displacement time series. That is, a station with a low correlation to all other stations represents a more unique location and, thus, gets a higher weight. As measures of agreement, we employ weighted root mean square error and weighted mean absolute error. With this validation, we can determine which parameter is most suitable for modelling present-day uplift rates. Further, regional differences in the model performance can be evaluated.
The results of this GIA model validation study will be the basis for further investigations within the GRACE-FO project of the ESA Third Party Mission Program as well as within the German Climate Modeling initiative PalMod.

The Wilkes Subglacial Basin (WSB) host one of the largest marine-based sectors of the East Antarctic Ice Sheet (EAIS), which stretches for ca 1600 km from the George V Coast towards South Pole. This sector is potentially more susceptible to climate change due to the sub sea-level topography and is predicted to be one of the largest contributors to sea level rise in the future. However, understanding the long-term stability of the EASI in the WSB area requires better quantification of the solid Earth contribution to the cryosphere, in particular the geothermal heat flow and the influence heterogeneity in subglacial geology and the lithospheric architecture have on the thermal structure in the WSB region.

Here we present subglacial geothermal heat flow estimates, one of the least constrained parameters of the entire Antarctic continent, based on lithospheric scale geophysical models. We use a combination of airborne radar magnetic and gravity data compilations, crustal and lithosphere thickness estimates from both satellite and airborne gravity complemented with independent passive seismic constraints and Curie depth point estimates to develop new geophysical models for the WSB area. We model a major boundary along the eastern margin, separating the Ross Orogen from a composite Precambrian Wilkes Terrane. Precambrian basement is modelled as both shallower and of more felsic bulk composition. We suggest based on the similarity in aeromagnetic anomalies that the basement represents late Paleoproterozoic to Mesoproterozoic igneous basement as exposed in South Australia. The formerly contiguous basement of South Australia is associated with high geothermal heat flow (80-120 mW/m2) due to anomalously high intracrustal heat production within highly radiogenic Paleo to Meso Proterozoic granitoids.

First order differences in basement depth, bulk composition and metasediment/sediment cover coupled with spatial varying intracrustal heat production is likely to give rise to significant heterogeneity in geothermal heat flow beneath different parts of the WSB. A hypothesis we will test further with our newly developed thermal models for this area.

We present WINTERC-G-v2-beta, a model derived from Waveform tomography and Gravity INversion for the TEmpeRature and Composition of the crust and mantle at global scale. Like its predecessor, WINTERC-G, this preliminary second version is constrained in the crust and upper mantle by: (a) Rayleigh and Love dispersion fundamental mode curves, (b) filtered satellite gravity field, (c) surface elevation and (d) heat flow data. The updated model incorporates additional constraints to image the the transition zone (410-660 km) and the lower mantle: (e) two surface wave overtones, (f) travel times of P and S phases (from the ISC-EHB Bulletin) and P-to-S receiver functions (from the project GLImER, Rondenay et al., 2017; http://stephanerondenay.com/glimer-web.html), (g) full spectrum satellite gravity field data (geoid anomalies and gravity gradients from ESA’s GOCE mission), and (h) a geodynamic estimate of the Earth’s surface elevation and CMB topography compatible with the model. We jointly invert the full data set to estimate 3D variations of the Earth’s crust and mantle physical properties: compressional wavespeed (Vp), shear wavespeed (Vs), density (ρ) and attenuation (Qs) derived as a function of temperature, pressure and composition (a thermodynamically self-consistent framework). The crust is parameterized based on its thickness (depth of the Moho discontinuity) and lithology. The Mantle is separated into four sections: the lithospheric mantle, the asthenospheric or sublithospheric upper mantle, the transition zone, and the lower mantle. Each layer is initially assumed to have a homogeneous composition (in terms of the amount of Al2O3 and FeO major oxides), and a reference geotherm that is perturbed during inversion. Standard features observed in other global models (slabs, hot spots, etc) are present in the final product.

The Greenland ice sheet is one of the largest contributors to global sea level rise, and it is constantly monitored. However, still little is known about the heat flux at the glacier bedrock, and how it affects the dynamics of the major outlet glaciers in Greenland. Recent studies suggest the hotspot currently under Iceland was located beneath eastern Greenland at ~40 Ma BP and that the upwelling of hot material from the Iceland plume towards Greenland is ongoing. A warm upper mantle has a low viscosity, which in turn causes the solid Earth to rebound much faster to deglaciation.
In the area of the Kangerlussuaq glacier, a large GPS velocities residual after removing predicted purely elastic deformations caused by present-day ice loss suggests the possibility of such fast rebound to little ice age (LIA) deglaciation. Here we investigate the lithospheric thickness and the mantle viscosity structure beneath SE-Greenland by means of model predictions of solid Earth deformation driven by a low viscosity mantle excited by the LIA deglaciation to the present day. From the comparison of such modeled deformations with the GPS residual, we conclude that
1) a rather thick lithosphere is preferred, i.e. between 90 and 100 km 2) and the upper mantle most likely has a viscosity that changes with depth. Assuming a two layer upper mantle, our conclusion is that upper mantle viscosity has to be quite low, but it is not well constrained which part of the upper mantle has to be low, with a preference for low viscosity in the deeper upper mantle.
To understand such results we implemented forward modelling with more realistic earth models, relying on improvements in seismic models, petrology and gravity data. This yields 3D viscosity maps that can be compared to inferences based on the 1D model and forms the basis for 3D GIA models.

The conclusion based on the 1D model can be explained with 3D Earth models. In the area of the Kangerlussuaq glacier the seismic derived viscosities prefer a higher viscosity layer above a lower viscosity one. This stems from the slow decrease in viscosity with depth. The layer that is characterized as shallow upper mantle still contains shallow regions with low temperatures, while the deeper upper mantle reaches low viscosities. Generally, for GIA earth models the “higher above lower” viscosity layering is unusual. However, the analysis of the 1D model clearly shows this to be one of the preferred model regions, in combination with a large lithosphere thickness of 100 km. This is a notable result that draws attention to the importance of shallow layering in GIA models.

Geophysical observables (e.g. surface elevation, gravity field anomalies, seismic data, surface heat flow, etc.) are one of the main sources of information used to make inferences about the interior of the Earth. Obtaining consistent models requires combining simultaneously different observable datasets into joint inversions. Among geophysical data, gravity data from ESA’s GOCE satellite mission provides key information in properly constraining the Earth’s density distribution. WINTERC-G is a new global thermochemical model of the lithosphere and upper mantle (currently being extended into the transition zone and lower mantle) based on terrestrial and satellite gravity data (Fullea et al., 2021). The inversion procedure behind WINTERC-G is twofold. In step 1, 1D inversion of surface wave tomographic, surface elevation (isostasy) and heat flow data is carried out. Then, in step 2, the output model from step 1 is used as prior information for the inversion of GOCE’s gravity field data (geoid anomalies filtered and gravity gradients from GOCE at satellite height) to refine the 3D crustal density and upper mantle composition. The residual, non-isostatic surface topography originated in the model can be regarded as a proxy for dynamic topography although currently there is no explicit dynamic calculation consistently linking both topographies. Within a rigorous framework, residual topography and computed dynamic topography (i.e. solving the Stokes equation) should be consistently integrated into a joint inversion of the gravity field and the terrestrial observations with feedback from both the static and dynamic sides. This is currently missing in WINTERC-G and the goal of this study is to add a third step into the global inversion scheme that consistently integrates dynamic mantle flow, rheology, the deformation of density discontinuities (surface and Core Mantle Boundary, CMB) and the full geoid anomaly (including static and dynamic components). To do that, we will explicitly compute dynamic topography solving the associated Stokes equation fed by WINTERC-G 3D distributions of densities and viscosities obtained from the output of step 2.

Including an explicit computation of mantle flow into an inversion scheme at global scale requires solving a large number of forward large-scale 3D Stokes flow problems. Their high computational cost has been so far a bottleneck for global geophysical inversions accounting for mantle dynamic effects. The proposed methodology will use Model Order Reduction techniques, specifically the Reduced Basis (RB) method, as a tool to alleviate the burden of such expensive computations, by creating surrogate models that approximate the solutions at a much lower computational cost. An efficient implementation of the RB method at a global scale requires a high-fidelity solver as well as a good error estimator. PETSc libraries (https://petsc.org) will be used to build a Stokes high-fidelity and parallel solver which will allow us to create the very fast RB surrogate model essential for the inversion. In doing so, WINTERC-G will be able to simultaneously and consistently explain Earth’s surface elevation (both its isostatic and dynamic contributions) and CMB topography while matching at the same time the constraining satellite gravity data. Moreover, not only we will have a better understanding on how/whether to filter satellite gravity data, but also the feedback from the dynamic/isostatic topography balance will allow us to properly constrain crustal density and lithospheric mantle composition.

Fullea, J. Lebedev, S., Martinec, Z., Celli, N. L. (2021). WINTERC-G: mapping the upper mantle thermochemical heterogeneity from coupled geophysical-petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data, Geophysical Journal International, 226(1), 146–191.

The Earth's core field, a primary driver of the geomagnetic field, is shaped by multiple internal and external sources. Nowadays, information on this complex system is provided by ESA's three-satellite mission Swarm. Near the core-mantle boudary (CMB) there might be large mass transport involved. The satellite mission GRACE-FO (NASA/GFZ) has two intruments dedicated to tiny Earth gravity variations due to global mass transport – a microwave and laser ranging system. These variations might contain also a signal coming from the very deep regions like the CMB. In this contribution the time serious data from the two missions (satellite magnetometry and randing data) is compared to look for common patterns and correlation.

The Earth’s core is made of a solid inner core and a liquid outer core. In the Earth’s outer core, a dominant Coriolis force causes the fluid to form columnar structures aligned with the rotational axis. With a large-scale background magnetic field, a small disturbance may cause oscillations of these fluid columns, which then form torsional waves travelling perpendicular to the rotational axis. Inverse models have shown that the variation of the day length is correlated with the period of torsional oscillations due to the angular momentum transport. Since 1999, monitoring of the Earth’s magnetic field from satellites (e.g. CHAMP and the Swarm constellation) has provided us with a detailed description of the changes in the geomagnetic field. Over more than 20 years of satellite observations are now very much appropriate to the study of torsional oscillations as these waves have periods of about 6 years. An accurate description of torsional waves is therefore essential for the interpretation of geomagnetic data. Many past studies on torsional waves have assumed a simple 1D profile, and did not distinguish the region above and below the inner core. Recent observations and analytical models of the outer core have suggested that this is not necessarily the case. In this study, we take into account the realistic geometry of the outer core, and include the effects of conductivity and equatorial symmetry of the background field as well. We run 3D numerical simulations to quantify how much an initial Gaussian torsional wave pulse can be modified as it propagates, particularly at the tangent cylinder (a special location where the geostrophic cylinder intersects the inner core) where an inward propagating wave splits into two—above and below the inner core. We find that the width of the wave pulse is important in determining how the wave reflects. The wider the pulse, the more effects it feels from the presence of the inner core. The conductivity also plays a role: a conducting inner core generates more reflection compared with an insulating one. The combined effect is likely both geometrical and conductivity-dependent. There can also be a hemispherical difference in the wave speed inside the tangent cylinder if the background field is equatorially asymmetric. This is especially interesting from an observational perspective, since the difference in inverted wave speed can provide an estimate of the field asymmetry, which is otherwise unknown from deep inside the core. Given that the inverted Earth’s torsional wave signal has a relatively wide wavelength with respect to the core radius, and the inner core has a conductivity close to that of the outer core, we need to carefully consider these features when modelling the Earth’s core dynamics.

Greenland’s lithospheric structure underneath the up to 3 km thick ice sheet is still poorly known. Direct observations from the outcropping bedrock geology are limited to the ice-free coast and the seismic station coverage in Greenland is sparse. Models based on different geophysical methods often even lead to contradicting results, as for example deviating Moho depth estimates based on gravity inversion compared to receiver function analysis.
Our study aims to build a lithospheric model for Greenland which focuses on data integration and is consistent with multiple observables. In a self-consistent framework, we use petrological information of the mantle to model coherently seismic velocities, densities, and temperatures down to a depth of 400 km. During the modelling process, we adjust the lithospheric structure to reproduce velocities from the new regional tomography model NAT2021. The forward calculation of the corresponding lithospheric densities provides information on the gravity field and isostatic elevation conforming to the seismic structure. This link allows us to estimate which sources of the observed gravity field are as well represented in the tomography model. In a second step, we jointly invert the residual gravity field from the lithospheric background model with airborne magnetic data to obtain consistent crustal variations of densities and susceptibilities. Furthermore, we compare the modelled geoid and isostatic elevation with the observed values and the surface heat flow output of the model with the few heat flow measurement points in Greenland.
Our approach makes it possible to combine a wide range of datasets. The two-step modelling process allows us to model a higher resolution for the crustal part than for the lithospheric mantle, which is necessary to consider the varying data resolutions.

Surface deformations, e.g. soil compaction, soluble and swelling rock formations, groundwater extraction, natural gas extraction, mining, cavern storage operation and landslides. These processes can cause damages to houses and infrastructrure and can even lead to loss of life. Spaceborne interferometric SAR techniques, e.g. based on Copernicus Sentinel-1, are able to measure surface deformations for entire nations or even continents. In order to support the use of this technique the Ground Motion Service Germany (BBD) is operated, since 2019, by the Federal Institute of Geosciences and Natural Resources (BGR). The service provides mosaicked wide area Sentinel-1 Persistent Scatterer Interferometry (PSI) datasets of the entire nation (~ 360,000 km²). The PSI datasets are based on six ascending- and six descending- Sentinel-1 tracks. Line-of-Sight (LoS) datasets are provided, with an open data policy, via a WebGIS. In addition, since 2021, the service provides nationwide 2D-decomposed datasets (vertical and East-West velocities and time series). All PSI datasets are updated on an annual basis.
The presentation shows the characteristics of the recent update of the nationwide Sentinel-1 PSI datasets. The spatial measurement density and LoS, vertical and East-West velocity precision is reported. Based on selected case studies the information content and limitations of the wide area PSI dataset are discussed. Both, anthropogenic- and natural surface deformation processes are in the focus of the case studies. In order to show the capabilities of the dataset, the case studies include spatial scales from a few hundred of meters to more than 50 kilometers. Field observations, in-situ measurements and thematic data are used to assess the information content of the wide area PSI dataset. Finally, the presentation gives an overview of the BBD WebGIS, used for dissemination and visualization of the big PSI datasets. The WebGIS functionalities include, e.g. time series plots, queries, download, print and superposition with ancillary data.

Measurements of the gravity field can be used to estimate the density distribution inside the Earth at many different scales. Satellite gravty data are well suited for investigations at a continental scale and are sensitive to both crust and mantle density variations, although the resolution becomes worse with depth. However, such inversions are inherently non-unique, so additional constraints are needed. Most often these constraints come from seismic tomography models.

While tomographic models provide crucial information, there are some problems when connecting them to the gravity field. One problem is that tomographic models are affected by both lateral and vertical smoothing, which might also be different depending on the distribution of seismic stations. Furthermore, how to relate seismic velocities and density is the subject of an ongoing debate, especially in the presence of compositional variations.

To help alleviate these issues, we can use the Rayleigh wave phase velocity instead of the seismic tomography as constraints directly. The Rayleigh wave phase velocity at a geographical location depends only on the vertical distribution of seismic velocities and (weakly) density. Each Rayleigh wave frequency has distinct sensitivity to a specific depth range, with lower frequencies being more sensitive to deeper structure. Thus, the Rayleigh wave dispersion curve, which gives the relation between frequency and phase velocity, is - to some extent - an equivalent representation of the vertical velocity structure. The benefit of the dispersion curves is that they do not fix velocity anomalies at specific depths, so in a joint inversion of gravity and dispersion curves the different depth sensitivities can be exploited more fully. Finally, Rayleigh wave phase velocity is weakly sensitive to density as well, so some insights might be gained into the velocity-density relation.

We demonstrate the idea of this joint inversion in a transdimensional framework, where the Earth's mantle is parametrized in terms of a variable number of discrete anomalous volumes. Synthetic dispersion curves are calculated for recent seismic tomographic models and jointly inverted with synthetic gravity data simulated at 225 km height.

We present an approach to estimate local time series of the magnetic field gradient tensor elements at satellite altitude. These time series are derived from magnetic observations taken by the Swarm satellite constellation, and are computed using the Geomagnetic Virtual Observatory (GVO) method. The GVO time series are distributed in a worldwide network, which enables global investigations of spatial-temporal variations in the gradient tensor elements. We find that the Swarm constellation is beneficial for deriving the GVO gradient series compared with a single satellite mission. Our results support previous findings that gradient series could help to better resolve SV for harmonics above degree 6. We find evidence for a regional SV impulse (jerk) event in 2017 in the first time derivative of the gradient tensor elements. This event is located at low latitudes in the Pacific region, and its signature is independent of adopted data selection criteria. Striking patterns of localized
acceleration changes in global maps of the gradient tensor elements, are found to be associated with this event. The GVO gradient tensor series provides an effective way of compressing spatio-temporal information of geomagnetic field variations as gathered by the Swarm satellite mission. Such gradient tensor series may prove useful for studying geomagnetic jerks, core flows and in geodynamo data assimilation.

A sharp change in the trend of the secular variation of magnetic declination measured in Europe during 1969-1970 was the first identified instance of geomagnetic impulse (or jerk).  This 1969 geomagnetic impulse has effectively set the standard against which other such events are compared.   Recent studies of geomagnetic impulses during the era of global satellite observations, and in numerical geodynamo simulations, indicate that such geomagnetic impulse events are often associated with dipolar foci at the core-mantle boundary, where the radial field acceleration locally changes sign on an interannual timescale,  and that these foci typically occur at low latitudes.  This raises the question as to whether the 1969 event was fundamentally different from more recent events, and whether or not it is compatible with the hydromagnetic-wave driven mechanism seen in geodynamo simulations. 

In an effort to address these questions we here revisit the 1969 impulse, reprocessing the available ground observatory data and using measurements of field intensity, and along-track differences of field intensity, from the OGO-2, 4 and 6 missions to derive a geomagnetic field model spanning 1960-1980 using a field modelling technique similar to that employed for the CHAOS field model series (Olsen et al., 2006; Finlay et al., 2020).  We find that the 1969 impulse is closely associated with an intense change in the core-mantle boundary radial field secular acceleration with a dipolar structure, located under central and Northern America in 1969-1970.  This has a similar amplitude to recently observed secular acceleration changes and is longitudinally localised. However it extends meridionally up to mid-to-high latitudes under north America.  We find evidence for this feature in models build only using POGO satellite data  but it is most clearly seen in the ground observatory data, particularly from San Juan (vertical component) and Fredericksburg (northward component).  The famous V shaped features in the East-west component of secular variation in Europe also largely resulted from these changes taking place below North America, a consequence of how the Green's function for the east-west component of observatories in Europe samples the core-mantle boundary .  Based on our findings regarding its signature at the core-mantle boundary, it seems the 1969 impulse may be compatible with a mechanism similar to that which produced impulse events in recent years.  The idea that the 1969 impulse is fundamentally different to more modern events may in part be related to the uneven distribution of the available ground observatories, and due to the fact that in some prominent records it appears unusually well separated from other impulses.

Modern geomagnetic field models, derived using satellite and observatory magnetic data, are now covering more than twenty years. They provide information on the numerous external and internal sources generating the field and in particular on the core field. The presented work aims at providing reliable models that describe the short-term evolution of the core magnetic field and of the core surface flow. We use a sequential modelling approach – a Kalman filter, combined with a correlation-based modelling step, together with strong prior information on the spatial behaviour of the geomagnetic field and core flow contributions. A sequence of snapshot models has been built describing the core magnetic field and its secular variation together with their associated flow at the core-mantle boundary, allowing for a core field temporal resolution of the order of the year. Prior information on the flow and magnetic field models rely on the statistics of the Coupled Earth numerical dynamo model (Aubert et al., 2013). The resulting time series of core field and flow models present the general characteristics of the models based on more classic modelling techniques, thus supporting the reliability of this method. The flow models present strong quasi-geostrophic structure. New interesting features were found in both the core field and flow time series, especially at small spatial scales. The results obtained depend on the quality of the data inputs and particularly on the availability of satellite data. The model presented were derived from Oersted, Champ, Cryosat and Swarm satellite data. For recent years the modelling process rely exclusively on observatory and Swarm data, the latter being absolutely necessary to describe precisely the fast evolution of the core field. Recovering faster variations of the core field is a high priority research activity, that may be possible thanks to new generation of satellite constellations such as NanoMagSat.

The interaction of the oceanic tidal flows with the Earth's main magnetic field provides a powerful natural source of electromagnetic energy suitable for sub-oceanic upper-mantle electrical conductivity sounding. We have developed a new frequency-domain, spherical harmonic-finite element approach to the inverse problem of global electromagnetic (EM) induction. It is set up for an effective inversion of satellite-observed tidally-induced magnetic field (TIMF) in terms of three-dimensional (3-D) structure of the electrical conductivity in the sub-oceanic upper mantle.

In this contribution, two independent TIMF datasets, both derived from the continuous measurements of the geomagnetic field by the Swarm multi-satellite mission, are employed (Grayver & Olsen, 2019, and Sabaka et al., 2020). Using several regularization methods and a-priori conductivity models, we have obtained the 3-D conductivity structures below the global ocean in the 150-400 km depth range. Figure 1
shows the electrical conductivity in S/m at the depth of 230 km, obtained by the inversion of M2 TIMF from the first dataset.

In the next step, the recovered 3-D conductivity structures are locally compared with the 1-D conductivity profiles below selected coastal and island geomagnetic observatories. We also present a statistical verification of the recovered conductivity models using thermodynamically self-consistent compositional models coupled to the electrical-conductivity laboratory measurement of individual mantle constituents. Bayesian method based on the sampling of the laboratory conductivities and thermochemical parameters allows us to perform an error analysis.

Finally, we explore prospective benefits of improved spatio-temporal coverage of the planned NanoMagSat mission for TIMF recovery. For this purpose, we use the forward modelling of TIMF along synthetic NanoMagSat trajectories.

We present results of cluster analysis and geophysical modelling of the West and Central African rift system, where we integrate seismological and satellite data. For a description of lithospheric domains, two different methods based on seismic tomography and satellite gravity data have been used. First, the terracing method using the shape index has been applied to the gravity field (XGM2019) in order to enhance the signal of the large-scale tectonic units. In addition, the K-means cluster method (which is an unsupervised machine learning algorithm) has been applied to a tomography model over the study area.
Both models are compared and interpreted towards similarities and differences. The preliminary analysis based on K-means clustering of seismic tomography shows that the West and Central African rift system and its surroundings can be divided into at least three clear and distinct tectonic domains: The Northern part of the Congo craton, the Eastern part of the West African craton and a large area going from Cameroon’s littoral (Atlantic Ocean) to the Egypt’s Northern border (Red Sea). In addition, the preliminary analysis of the terracing of satellite gravity data, first confirms the localization of both, the Congo craton and the West African craton, but also splits the large area from Cameroon to Egypt into two known tectonic units: the Southern part of the Saharan meta-craton and the West and Central African rift system in the middle of the three others units.
The cluster analysis is also pointing to differences at crustal and upper mantle level and is the first step towards the evolution of a lithospheric scale model. In the model, we integrate our tectonic domain analysis with the existing seismic Moho depths estimate and other information. Magnetic data will be considered in addition to study link of the lithospheric architecture to the large and enigmatic Bangui anomaly.

Detailed study of the time-dependent internal magnetic field produced in the Earth’s core requires that one can appropriately describe and separate external magnetic fields generated by the electric current systems in the Earth’s ionosphere and magnetosphere. In particular, the magnetic field associated with the strong currents in the polar ionosphere, driven by the interaction of the solar wind and the magnetosphere, may contaminate core field models if not properly taken into account.
Earlier studies have developed several techniques to include the ionospheric field in geomagnetic field modelling. However, these approaches have only been able to account for specific periodicities in the field at non-polar latitudes or have used relatively little information on the underlying physical processes.
Here, we describe an approach for representing the ionospheric field during geomagnetic field modelling with a focus on the challenging polar regions. Magnetic apex coordinates are employed and the time-dependence of observed solar driving parameters is exploited. We include the new ionospheric field parameterization in the CHAOS framework for modelling the geomagnetic field and derive test models from satellite magnetic data to investigate the impact on the core field and to study the polar ionospheric current system under geomagnetically quiet conditions.
We find that the misfit in the polar regions is reduced, indicating success in accounting for previously unmodelled ionospheric signals. An ambiguity is however found in the zonal parts of the ionospheric and the internal field models. But this can be managed through model regularization. We also find that the divergence-free part of the horizontal ionospheric currents is weak compared to models of the ionospheric currents derived under more disturbed geomagnetic conditions than used here for the field modelling. We find that co-estimating the ionospheric field can results in an improvement in the high-degree and low-order components of the core field Secular Variation at the core-mantle boundary in the polar regions.
Our approach may allow us to relax the strong temporal smoothness constraints usually applied to the core field during geomagnetic field modelling and, thus, help to better resolve its time changes.

We present a post-obductional geodynamic model explaining the differentially elevated ~150-km-long passive margin of northeastern Oman based on multi-proxy analyses. The combination of methods such as multi-criteria analysis of the landforms, through the post-processing of very high resolution CartoSat-1 DSM, field work-samplings and laboratory XRF dating’s, together with semi-automated geomorphological mapping, differential GNSS measurements, and SAR interferometry (InSAR), are part of the proposed investigation. The results help understanding the geomorphological evolution of the Late Cretaceous to Quaternary relict landscape, where landforms are presented as indicators of the evolution of the area from Saih Hatat Dome (SAD) to Sur and define the processes that formed the current relief of northeastern coasts of Oman. At least six tectonic terraces were determined in shelffuls carbonate rock formations of Eocene age. This passive margin exhibits unusually high late Pleistocene uplift rates at an average of ~2.2 mm/a. InSAR-derived ground displacement documents that the uplift is still ongoing with an average rate of +1.3 mm/a. Geomorphological and stratigraphical analyses show that the area was heterogeneously uplifted following the Late Cretaceous. Thus, a fault-based tectonic model of differential and compartmentalized (“block-like”) uplift history of the studied passive margin of Oman is proposed.

Vast regions of North America were covered by ice during the Last Glacial Maximum (about 20,000 years ago) depressing the Earth. After the ice melted, the lithosphere started rebounding and this still ongoing uplift is measured, among others, by Global Navigation Satellite System (GNSS) observations and geological sea level indicators. While the vertical velocity (uplift) component is often used to constrain dedicated models of glacial isostatic adjustment (GIA), the horizontal motions as observed by GNSS have received little attention in such investigations in the past. However, horizontal motions are more sensitive to lateral lithospheric structures and viscosity variations in the mantle, which are known to vary significantly within the Earth

To realistically capture the correct horizontal motion due to GIA, numerical models need to be more complex by including lateral heterogeneity of the mantle’s viscosity structure, lithosphere variations and, most importantly, compressibility. Compressibility is shown to affect the results significantly. For example, a ten-thousand-year loading with a polar cap of 10 latitudinal degrees in extent and maximum 1500 meter in height, results in a horizontal displacement that in compressible models is about 20 meters lower as compared to that in incompressible models. This effect is significant as the maximum horizontal deflection in this scenario is only 40 meters. Using an improved Finite-Element model, called FEMIBSF, we can model the deformation of a compressible Earth due to glaciations, while also incorporating a laterally varying viscosity structure. Lateral variations in viscosity are calculated from 3D temperature fields of WINTERC-G, an upper mantle model that uses surface waveform tomography, surface heat flow, satellite gravity and topography to derive mantle temperature, among others. The temperature fields are converted to viscosities using an olivine flow law, varying the grain size and water content in order to create multiple options for the 3D viscosity fields.

In this study, we focus on the effect of lateral heterogeneities on the modelled horizontal displacements. Finally, we attempt to match modelled horizontal displacements with those observed by GNSS to constrain the mantle viscosity.

In times of global warming, the understanding of key processes for ice sheet melting and sea level change is of high relevance. The Antarctic ice sheet alone could potentially contribute about 60 m of sea-level rise. Glacial isostatic adjustment (GIA) – the viscoelastic response of the solid Earth to surface loading – is a key feedback mechanism for bedrock deformation, sea level, grounding-line position, ice sheet elevation and stability. Observational data reveal large viscosity contrasts in Antarctica characterized by an old, cold craton with a thick lithosphere and high viscosities in the east and a failed rift system with thin lithosphere and low viscosities in the west divided by the Transantarctic Mountains. The response time and bedrock topography evolution in GIA models are sensitive to the Earth structure and glacial history. Therefore, the consideration of independent data for the determination of the 3D Earth structure helps to improve GIA models. Hereby, the lithospheric structure, which controls the bending stiffness, and the mantle viscosity, which determines the timing of the adjustment process, are parameters of great interest. But in detail, the Earth structure for Antarctica is not uniquely defined and differs depending on the employed method, e.g., seismic tomography models, geodynamic models or transfer functions.

We develop 3D Earth structures under consideration of independent constraints and apply them to GIA