The European Space Agency (ESA)’s wind mission, Aeolus, was launched on 22 August 2018. It is the fifth of the ESA Earth Explorer family of missions and its main objective is to demonstrate the potential of Doppler Wind Lidar (DWL) in space for improving weather forecasts and the understanding of the role of atmospheric dynamics in climate variability. Aeolus carries a single instrument called the Atmospheric LAser Doppler INstrument (ALADIN): a high spectral resolution DWL which operates at 355nm which is the first of its kind to be flown in space.
Aeolus provides profiles of single horizontal line-of-sight winds (primary product) in near-real-time (NRT), and profiles of atmospheric backscatter and extinction. The instrument samples the atmosphere from about 30 km down to the Earth’s surface, or down to optically thick clouds. The required precision of the wind observations is 1-2.5 m/s in the troposphere and 3-5 m/s in the stratosphere while the systematic error requirement is less than 0.7 m/s. The mission spin-off products include information about aerosol and cloud layers. The satellite flies in a polar dusk/dawn orbit (6 am/pm local time ascending node), providing ~16 orbits per 24 hours with an orbit repeat cycle of 7 days. Global scientific payload data acquisition is guaranteed with the combined usage of Svalbard and Troll X-band receiving stations.
After more than 3 years in orbit and despite some performance issues related to its instrument ALADIN, Aeolus has achieved its objectives and designed-life time. Positive impact on the weather forecast has been demonstrated by multiple NWP centres world-wide. Four of the main European meteorological centres are now assimilating Aeolus winds operationally thanks to the excellent data timeliness and continuous improvement of the ground based processors.
The status of the Aeolus mission will be presented, including overall performance, planned operations and exploitation. Scope of the paper is also to inform about the programmatic highlights and future challenges until the end of its operational life time.

The European Space Agency’s (ESA) Aeolus satellite was launched on 22 August 2018 from Centre Spatial Guyanais in Kourou, French Guyana. The first atmospheric returns and first wind measurements were retrieved within the first two weeks of operations after launch. Aeolus has continually provided wind measurements since this time with only limited outages in availability. Aeolus successfully met it’s nominal mission lifetime at the end of November 2021, and it has been agreed to extend the mission operations until the end of 2022.

It was discovered that the atmospheric return signals from the ALADIN instrument were lower than expected before launch by a factor of x2 to x3. Extensive investigations were performed which led to the conclusion that the most likely cause for the loss in signal was an over-illumination of the 88µm diameter instrument field stop. In addition to this initial loss, it was found that the output energy of the first flight laser transmitter (FM-A) was decreasing. An investigation showed that this decrease was due to a continual misalignment of the laser master oscillator. The decrease in the FM-A UV energy and the resulting degradation of the wind measurement random error led to the decision to change to the second flight laser (FM-B) in June 2019. The FM-B laser has performed well in the subsequent 2.5yrs of operations retaining UV output energies above 60mJ and has recently provided UV output energies around 90mJ, corresponding to the levels that were reached in the on-ground thermal vacuum performance tests.

Despite the good performance of the FM-B transmitter, losses in the atmospheric return signal were evident. These losses were eventually traced to the emit path of the instrument (i.e. those optics after the laser transmitter up to the telescope output), largely due to independent measurements of the ALADIN emit energy by the Pierre Auger Observatory in Argentina, which indicated some 50% reduction in the emit energy between 2019 and 2022. This has led to recent interest to revert back to the FM-A transmitter (correcting for the misalignment) or to lowering the Aeolus altitude from the current 320km to 255km, in order to improve the signal returns and extend the mission duration.

Several issues with systematic bias in the wind measurements were also discovered. Pixels with slightly increased dark current levels in the memory zone of the Accumulation Charge-Coupled Devices (ACCDs), so called “hot pixels”, were found, with a new hot pixel arising approximately every 2-3 weeks. A novel pseudo-dark current correction method was employed in order to correct for these hot pixels. An important sub-orbital bias caused by differing background illuminations of the ALADIN telescope due to albedo variations was also discovered. This was mitigated by employing a correction which utilised the ALADIN telescope temperatures.

The Aeolus data has been extensively analysed by a number of meteorological centres and found to have a positive impact on NWP forecasts, particularly in the tropics and polar regions. These very positive results, along with the successful in-orbit demonstration of the measurement concept and associated technologies utilised on Aeolus, resulted in a statement of interest from EUMETSAT in a future, operational DWL mission in the 2030 to mid-2040’s timeframe.

This paper will summarise the performance of Aeolus’ ALADIN instrument in its 3+ years of operations, detailing the issues that have been described briefly above, as well as the mitigation actions taken, and drawing lessons learned for a future operational Doppler wind lidar mission.

After more than three years of operations of ESA’s Wind mission, launched on 22 August 2018, the Aeolus Payload Data Ground Segment continues to ensure successfully global X-band data acquisition with the combined usage of the Svalbard and Troll X-band ground stations, seamless mission planning operations, uninterrupted systematic science data production in Near Real Time (within 3 hours from sensing) and easy data access and discovery.

The Payload Data Ground Segment is based on a distributed architecture that foresees the first level of processing up to the preliminary wind observations and scientific aerosol/cloud profile products combined with the telemetry acquisition service, hosted and operated by KSAT in Norway. The second level of processing including the scientific wind observations is performed by the European Centre for Medium-Range Weather Forecasts (ECMWF) based in England for further assimilation and usage by multiple Numerical Weather Prediction (NWP) centres world-wide.
The Aeolus data is made available to meteo and expert users via the ESA Earth Observation Gateway through a dedicated dissemination system and archived for data preservation. Data quality. monitoring calibration and scientific processors evolution is performed by an international expert consortium, the Aeolus DISC (Data, Innovation, and Science Cluster).
The PDGS team at the ESA/ESRIN centre in Frascati has been responsible of the coordination and execution of the Payload Data Ground Segment operations, including the deployment in operations of various processing baselines and the public dissemination to users of the Aeolus products once the relevant quality standards were met. In addition, the PDGS team is responsible for the challenging mission planning of the instruments operations and has supported the execution of three reprocessing campaigns allowing mission data to be made available to users in the latest processing baselines, ensuring the highest possible data quality standards.
Along the years efforts have been put also in providing the Aeolus data user community with a modern concept of extended access to Earth Observation (EO) data. The VirES for Aeolus system provides a highly interactive data manipulation and retrieval web interface for the official Aeolus data products. The VirES Service will be extended with a Virtual Research Environment (VRE) that will become operational at the beginning of 2022 in order to provide data manipulation capabilities to users.
The architecture, current status and performance of the Aeolus PDGS will be presented, with focus on the main activities performed in more than three years of operations of the ground segment of this extremely challenging mission.

Assuring and improving Aeolus' data quality is a collaborative effort of the ESA Aeolus team, the Aeolus DISC (Data Innovation and Science Cluster) as well as more than 30 individual Cal/Val teams. The Aeolus satellite has brought cutting edge UV DWL (Doppler Wind Lidar) technology to space for the first time. This required to be prepared and committed to support and maximise the performance from the ground, being ready for unknown biases and error product-sources. ESA, DISC, Cal/Val as well as the ASAG (Aeolus Science and Data Quality Advisory Group) have worked together closely during mission in order to react to findings from one of the performance monitoring tools, such as the monitoring of Aeolus winds against winds from Numerical Weather Predication centers (NWP monitoring), and the DISC has half yearly released new processing baselines, each of them improving the data quality and/or compensating for newly discovered anomalies and biases, but also reacting to performance decreases of the Aladin instrument over time. The presentation will introduce the many groups involved in maximizing Aeolus' data quality via on-ground activities, and their tasks, along with results, highlight and achieved milestones.

The Aeolus DISC (Data, Innovation, and Science Cluster) is a core element in ESA's data quality framework for the Aeolus mission, comprised of an international expert consortium to study and improve the data quality of Aeolus products. The tasks of the Aeolus DISC are various and include among others the instrument and data quality monitoring, the calibration and characterization of the instrument, and the refinement of the retrieval algorithms and the processor evolution. Additionally, the Aeolus DISC supports ESA with data reprocessing campaigns, provides support to data users and performs impact assessment studies.

The Aeolus DISC's mission experts and expert centres have long track records in supporting and contributing to Aeolus, covering instrumental aspects, laser-atmosphere interaction, calibration and validation (e.g. aircraft campaigns), wind product and processor development, as well as the development of optical, aerosol and cloud processors and products.

In this presentation, we will summarize the achievements of the Aeolus DISC for the data quality of the various Aeolus products. We will especially focus on how the constant instrument and data monitoring supported the evolution of the Aeolus processing chain. Past, present and future processor changes will be described and their impact on Aeolus NRT and reprocessed data quality will be illustrated.

Various processor improvements, developed by the Aeolus DISC after launch, lead to a drastic reduction of the systematic bias of the Aeolus wind products down to below 1 m/s on a global scale. Examples of such processor improvements are the hot pixel correction for enhanced dark current rates observed on the Aeolus detectors (Weiler et al., 2021a) and a correction for thermal changes of the instrument’s large telescope along the orbit (Weiler et al., 2021b).

Additionally, for aerosol and cloud retrievals, current processors were optimized and new processing routines were developed (Flament et al., 2021, and Ehlers et al., 2021). In particular, this includes a new feature mask and an optimal estimation retrieval based on EarthCare algorithms. Both products will be fully functional for the spring 2022 baseline release.




Weiler, F., Kanitz, T., Wernham, D., Rennie, M., Huber, D., Schillinger, M., Saint-Pe, O., Bell, R., Parrinello, T., and Reitebuch, O.: Characterization of dark current signal measurements of the ACCDs used on board the Aeolus satellite, Atmos. Meas. Tech., 14, 5153–5177, https://doi.org/10.5194/amt-14-5153-2021, 2021a.

Weiler, F., Rennie, M., Kanitz, T., Isaksen, L., Checa, E., de Kloe, J., Okunde, N., and Reitebuch, O.: Correction of wind bias for the lidar on board Aeolus using telescope temperatures, Atmos. Meas. Tech., 14, 7167–7185, https://doi.org/10.5194/amt-14-7167-2021, 2021b.

Flament, T., Trapon, D., Lacour, A., Dabas, A., Ehlers, F., and Huber, D.: Aeolus L2A Aerosol Optical Properties Product: Standard Correct Algorithm and Mie Correct Algorithm, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2021-181, in review, 2021.

Ehlers, F., Flament, T., Dabas, A., Trapon, D., Lacour, A., Baars, H., and Straume-Lindner, A. G.: Optimization of Aeolus Optical Properties Products by Maximum-Likelihood Estimation, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2021-212, in review, 2021.

Based on the experience gained during the pre-launch activities in the Aeolus processor development and validation campaign programmes, DLR has conducted an intensive performance monitoring for the mission since its launch in 2018.

Already in the decade bevor, various ground and airborne pre-launch validation campaigns had been performed in support of the Aeolus operations and processor development. An integral part of the early success of the mission was the deployment of the ALADIN Airborne Demonstrator (A2D), a prototype of the ALADIN Doppler Wind Lidar (DWL) instrument on-board Aeolus. With its high technological commonality, it allowed to study Aeolus-specific lidar topics under various atmospheric conditions already before launch. In addition, the scanning, coherent 2-µm DWL acted as a high-accuracy reference by providing wind vector profiles for the A2D and Aeolus validation. Airborne measurements were performed with the two instruments operated in parallel on the DLR Falcon research aircraft in a downward looking configuration. The comprehensive operational, technological and algorithm knowledge established at DLR during these pre-launch activities laid the basis for a twofold Aeolus performance monitoring, implemented to support the mission.

Firstly, performance-relevant instrument parameters of the laser and receiver optics as well as the detector signals evolution for the internal reference and atmospheric path were monitored and reported on a regular basis. Additionally, special operations for tests of the Aeolus lasers and instrument alignment were supported, both by applying the tools derived during the pre-launch period and with newly developed performance indicators. The second monitoring activity was covered by four post-launch airborne validation campaigns with a focus on the Aeolus wind product. Deploying the two DWLs onboard the Falcon, coordinated flights along a total distance of 26,000 km under the Aeolus track were performed in different geographical regions and during the main operational phases of the mission until September 2021. The collocated A2D and 2-µm DWL wind observations not only reflect the evolution of the error characteristics during each mission state, but also provide valuable recommendations for the optimization of the Aeolus wind retrieval and related quality control algorithms.

This contribution gives an overview of the Aeolus in-orbit instrument performance evolution and related results of the four airborne validation campaigns.

We highlight some of the scientific benefits of the Aeolus Doppler Wind Lidar mission since its launch in August 2018. Its scientific objectives are to improve weather forecasts and to advance the understanding of atmospheric dynamics and its interaction with the atmospheric energy and water cycle. A number of meteorological and science institutes across the world have demonstrated that the Aeolus mission objectives are being met, despite the measurements being noisier than expected. Its wind product is being operationally assimilated by five Numerical Weather Prediction (NWP) centres, thanks to demonstrated useful positive impact on NWP analyses and forecasts. Applications of its atmospheric optical properties product have been found, e.g., in the detection and tracking of smoke from the extreme Australian wildfires of 2020 and in atmospheric composition data assimilation. The winds are finding novel applications in atmospheric dynamics research, such as for tropical phenomena (Quasi-Biennial Oscillation disruption events), Sudden Stratospheric Warming events, detection of atmospheric gravity waves, and in the smoke generated vortex associated with the Australian wildfires. It has been applied in the assessment of other types of satellite derived wind information such as atmospheric motions vectors. The successes of Aeolus will hopefully lead to the approval of an operational follow-on mission.

Fronts – the boundaries between water masses, in coastal and oceanic regions - are hotspots for rich and diverse marine life, and are also where floating marine debris tends to accumulate.
The main goal of this research is to develop a prototype risk index for the accumulation of marine plastic debris at fronts. In addition to mapping the risk areas for debris accumulation, we investigate their connectivity to the pathways into the ocean, through numerical dispersion models with high spatial resolution. In doing so, we aim to provide a tool to local and regional policymakers to identify areas where intervention would be more effective. As a case study, we are working in collaboration with local stakeholders in Da Nang, Vietnam.
The study presented here shows the analysis of satellite imagery for 2018 around the Da Nang area. First, we have used very high-resolution imagery from Worldview 3 to validate Sentinel-2 detections of accumulation of marine debris. To do so, we have compiled a dataset from the literature, from specific imagery in the area and from reports on social media. We have then selected Worldview 3 imagery and used it to compare with Sentinel-2 detection of accumulations from different categories. This comparison has been done using different distance metrics. Then we have compared the location of verified accumulations with different detection methods for fronts, to identify the matching spatial resolution scales of observation. We present preliminary results on detection of fronts from altimetry, from AVHRR sea surface temperature and from Sentinel-3 and Sentinel-2 optical data. Dynamics of fronts have been identified aligned with the monsoon variations in the area. In addition, a very high spatial resolution hydrodynamic model has also been implemented and preliminary results will be shown on how the particle tracking matches sources/pathways to fronts. Results will be discussed in terms of challenges and opportunities ahead.

In recent years, there has been an increasing interest in exploring capabilities of remote sensing technologies to monitor and detect marine litter, and in particular, plastic litter floating in our water bodies. Remote sensing is now considered as the solely tool able to provide regular information at global scale to inform about this problem (Maximenko et al, 2019), which is essential to constrain the Lagrangian transport models put in place to understand its dynamics (Maximenko et al, 2012; van Sebille, 2015). In 2020, the European Space Agency (ESA) launched the Discovery Campaign on Remote Sensing of Plastic Marine Litter, funding a total of 26 initiatives across a wide spectrum of areas. The “Windrows As Proxies” project (WASP), included in the initiative, aimed to prototype an operational processor capable of identifying filaments of floating marine debris using Copernicus Sentinel-2/MSI (S-2) images, with a high probability of containing marine litter, based in the existing oceanographic knowledge of their role as accumulating agents of marine debris and plastic litter (Cozar et al, 2021; Ruiz et al, 2020).

The work from Hu (2021) stressed that a direct plastic detection using S-2 data could fall out of the possible or being specially challenging, due to spectral mixing, lack of specific bands, and challenges in both SNR and expected spatial coverage. Moreover, Dr. Hu (personal communication) also raises questions about the potential separability between marine litter and other substances, like sea snot, with similar spectral signature in the range 480-900 nm. Contrary to other initiatives (Biermann et al, 2020; Kikaki et al., 2020), where several attempts have been made to directly classify plastic within the images at pixel level, WASP embraced the problem from a different perspective. Assuming that litter rarely appears as a separate end member of the spectral reflectance measured at each pixel of S-2 data, and considering that S-2 lacks of plastic litter-specific bands (Hu, 2021), the objective is to detect filaments of floating debris, which often contain significantly lager quantities of plastic litter than their surroundings. This detection by proxy also enables the performance of both spectral and contextual (object-based) detection, which leads towards a less ambiguous classification process, with no need of external sources of data.

In a first step, WASP developed a specific spectral index that explores the use of NIR and SWIR bands as main source of information for the classification. This is also a different solution than published, where most of referred algorithms employ techniques closer to Ocean Colour using VIS bands as main source, following the lead of exiting spectral indices like NDVI and FAI (Biermann et al, 2020; Kikaki et al., 2020). The advantage of using NIR/SWIR is that, such spectral region, are containing information relevant for the detection of plastic (Garaba et al, 2020) and floating organic matter, reason why no external data is needed to support the detection.

In this work, we explore the use of such bands in Sentinel-2 for the detection of these filaments of floating marine debris, as well as create a better understanding of the general challenges that detection of plastic litter from space has. Part of the work performed includes the generation of an ad hoc procedure for cloud detection and filtering, and the use of a deterministic contextual classifier for the detection of filaments within the image, using a multiscale approach.

The results of WASP consist on snippets of S-2 analysed data containing the detected filaments. Such snippets are manually supervised by operators, in order to discard known false positives. Validation of the detection method has been carried out with the support of information over artificial targets deployed at sea in Lesbos Island (Topouzelis et al, 2020). The results of the validation show the capability the method has to detect both plastic and floating debris with high organic load.

The work presented here has helped also to understand the roadmap and needs for improvement of the techniques, as well as to advance in the detection principles of plastic litter with its challenges, a cornerstone information for the definition of a specific future EO mission devoted to plastic litter monitoring.

Biermann, L., Clewley, D., Martinez-Vicente, V., & Topouzelis, K. “Finding plastic patches in coastal waters using optical satellite data”. Scientific reports, 10(1), p1-10, 2020.

Cózar, A., Aliani, S., Basurko, O. C., Arias, M., Isobe, A., Topouzelis, K., ... & Morales-Caselles, C. (2021). Marine litter windrows: a strategic target to understand and manage the ocean plastic pollution. Frontiers in Marine Science, 8, 98.

Garaba, S. P., Arias, M., Corradi, P., Harmel, T., de Vries, R., & Lebreton, L. “Concentration, anisotropic and apparent colour effects on optical reflectance properties of virgin and ocean-harvested plastics”. Journal of Hazardous Materials, 124290, 2020.

Hu, C. (2021). Remote detection of marine debris using satellite observations in the visible and near infrared spectral range: Challenges and potentials. Remote Sensing of Environment, 259, 112414.

Kikaki, A., Karantzalos, K., Power, C. A., & Raitsos, D. E. “Remotely Sensing the Source and Transport of Marine Plastic Debris in Bay Islands of Honduras (Caribbean Sea)”. Remote Sensing, 12(11), 1727, 2020.

Topouzelis, K., Papageorgiou, D., Karagaitanakis, A., Papakonstantinou, A., & Arias, M. “Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)”. Remote Sensing, 12(12), p2013, 2020.
Maximenko, N., Hafner, J., & Niiler, P. “Pathways of marine debris derived from trajectories of Lagrangian drifters”. Marine pollution bulletin, 65(1-3), p51-62, 2012.

Maximenko, N., Corradi, P., Law, K. L., Van Sebille, E., Garaba, S. P., Lampitt, R. S., ... & Wilcox, C. “Toward the integrated marine debris observing system”. Frontiers in marine science, 6, 447, 2019.

Ruiz, I., Basurko, O. C., Rubio, A., Delpey, M., Granado, I., Declerck, A., ... & Cózar-Cabañas, A. “Litter windrows in the south-east coast of the Bay of Biscay: an ocean process enabling effective active fishing for litter”: Frontiers in marine science 2020.

Van Sebille, E., Wilcox, C., Lebreton, L., Maximenko, N., Hardesty, B. D., Van Franeker, J. A., ... & Law, K. L. “A global inventory of small floating plastic debris”. Environmental Research Letters, 10(12), 124006, 2015.

The remote sensing of marine litter is becoming increasingly important. Disasters such as floods cause large amounts of natural and man-made waste to enter coastal waters. Since such large-scale floating litter is an obstacle to safe operation of ships and recreation activities, it is necessary to issue an alert and remove it as quickly as possible. In addition, since man-made plastic litter adversely affects not only the aesthetics but also the marine ecosystem, interest in reducing plastic waste is higher than ever. Some of the plastic waste entered into the ocean and moves along the ocean current over a long period of time, threatening the life of marine animals. Numerous clean-up operations are being undertaken. Mapping areas of plastic litter accumulation in the ocean is certainly useful for efficient operations.
However, it is still challenging to reliably detect marine litter from space. One of the key technique to do is to detect changes in reflectance to identify small patches floating on the ocean surface. The spatial anomaly in reflectance was calculated by subtracting the spatially varying reflectance of the surrounding background water from the satellite-measured reflectance, and it was used to detect reflectance changes due to presence of drifting litter patches.
By applying this technique to high-resolution images of Worldview-3, we investigated the possibility of detecting plastic litter in Great Pacific Garbage Patch. Specifically, anomaly spectra were evaluated to detect plastic litter using the Ocean cleanup System 001 Wilson as a known target. While floating litter in the open ocean is found isolated, it is often observed accumulated along the water fronts. We applied the same technique to different-scale events in coastal waters using images from various satellite sensors including PlanetScope, MSI and GOCI. In addition to the change of reflectance due to the presence of litter, the reflectance spectrum of the target litter itself is very important information to identify the kind of litter, which will be discussed in this presentation.

Introduction

Ocean pollution is growing into a serious threat not only to the ecosystem and marine species but also to human life. In fact, significant quantities of anthropogenic trash, especially plastic, keeps getting discarded each year in the ocean mostly via rivers during extreme weather events. The pollutants could stay for long years in the ocean and cause long-term harm. For instance, plastic's degradation could take more than 400 years to happen, which is why it is very important to act rapidly to tackle the issue of marine pollution. Initiatives across the world such as the UN Sustainable Development Goal 14 and the EU Marine Strategy Framework Directive's descriptor 10, encourage improving the ocean's health.

A remarkable increase in methods to detect plastic and floating objects in general can be witnessed in the last few years and months (Topouzelis et al.,2019; Biermann et al., 2020; Mifdal et al., 2021). Spotting plastic can be very hard and expensive. In fact, most of the current monitoring methods rely on drones and/or coastal cameras which is not always affordable and convenient. Thus, an increasing amount of research is shifting the attention on the use of satellite data for floating targets monitoring. Currently, the most convenient satellite data is Sentinel-2 thanks to its global coverage and the public availability of its data, thus, it offers the possibility to monitor large-scale areas across the globe. Sentinel-2 provides images with thirteen or twelve bands, four bands have the spatial resolution of 10 m and the remaining ones have a 20 m and 60 m spatial-resolution. In this setting, only big patches of objects can be detected using Sentinel-2 data. Thanks to different natural processes such as wind, waves etc, floating debris are agglomerated and form patches that could be spotted on Sentinel-2 images. Thus, our goal is to focus on the detection of agglomerated floating targets in water bodies and provide a map of the detected objects which could be useful for other downstream tasks such as the detection of plastic, ocean clean-up operations, automating the monitoring of ocean pollutants etc. Recently, Sentinel-2 data along with deep learning methods proved effective for the detection of floating targets in water bodies (Mifdal et al., 2021). The authors hand-labeled floating patches on Sentinel-2 images based on the FDI (Biermann et al., 2020) and NDVI indices. Then, after running neural networks on the labeled datasets, the authors concluded from the results that the neural networks learnt efficiently the spatial patterns of the floating objects and were able to detect them with competitive accuracy. The labeled Sentinel-2 dataset along with the learnt weights are publicly available.

Contribution

Despite the results in (Mifdal et al., 2021), the detection of floating objects on water bodies remains a difficult problem and many issues could impact the network's performance. For example, on Sentinel-2 image of a coastal area, there are more pixels belonging to water than to the floating objects themselves, this creates an imbalanced learning problem. Also, the variability of regions across the world makes it hard sometimes for the network to spot the floating objects, and finally, the labels suffer from noise which could be misleading to the network during the learning phase. Thus, the goal of our contribution is to make the previous networks more robust to the domain adaptation problem and noisy labels by focusing on the utilization of self-supervised learning (SSL) while using attention mechanisms. More exactly, we use contrastive SSL as it is a simple and modular approach. The latter relies on a "pretext training task", defined by the user, that helps the network to learn invariances and latent patterns in the data without the need of any human annotation. To do so, the neural network forms positive pairs by considering various augmented views of a given sample and then maximizes their similarity through the noise contrastive estimation (NCE) loss. This emerging and yet promising paradigm increases model's robustness towards noise or frugality (Cio-carlan and Stoian, 2021), and representation learnt this way are known to have a better semantic and contextual dimension and, therefore, to better disentangle the main triggers of the network's decisions. Also, the conclusions of (Carmo et al., 2021) [1] emphasized the superiority of the MA-Net (U-Net with attention modules) with respect to U-Net, which encourages the use of attention mechanisms, especially transformers encoders. Indeed, despite the power of CNNs to extract meaningful features and textures, the spatial context and objects shape are less taken into account during the learning step when comparing with transformers encoders (Tuli et al., 2021). The lack of spatial information limits the global comprehension of the image and also the deep neural network's ability to identify and distinguish floating debris from lands or ships. Combining SSL with methods based on attention mechanisms altogether has shown a significant improvement in performances, especially in domain issues and generalization to challenging settings.

References

[1] https://210507-004.oceansvirtual.com/view/content/skdwP611e3583eba2b/ecf65c2aaf278557ad05c213247d67a54196c9376a0aed8f1875681f182daeed

L. Biermann, D. Clewley, V. Martinez-Vicente, andK. Topouzelis. Finding plastic patches in coastalwaters using optical satellite data.Scientific re-ports, 10(1):1–10, 2020.

A. Ciocarlan and A. Stoian. Ship Detection in Sen-tinel 2 Multi-Spectral Images with Self-SupervisedLearning.Remote Sensing, 13(21):4255, Oct. 2021.ISSN 2072-4292. doi: 10.3390/rs13214255. URLhttps://www.mdpi.com/2072-4292/13/21/4255.

J. Mifdal, N. Longepe, and M. Rußwurm. Towardsdetecting floating objects on a global scale withlearned spatial features using sentinel 2.ISPRSAnnals of the Photogrammetry, Remote Sensingand Spatial Information Sciences, 2021.

K. Topouzelis, A. Papakonstantinou, and S. P.Garaba. Detection of floating plastics from satelliteand unmanned aerial systems (plastic litter project2018).International Journal of Applied Earth Ob-servation and Geoinformation, 79:175–183, 2019.

S. Tuli, I. Dasgupta, E. Grant, and T. L. Griffiths.Are Convolutional Neural Networks or Transform-ers more like human vision?arXiv:2105.07197 [cs],July 2021. URLhttp://arxiv.org/abs/2105.07197. arXiv: 2105.07197.

Each year, several million metric tons of mismanaged plastic waste eventually enter the ocean from coastal environments. Once in the ocean, positively buoyant plastic debris are subjected to a wide range of physical transport processes such as coastal currents, large-scale and submesoscale open ocean processes, stokes drift, direct wind transport, vertical mixing, and beaching. These processes result in the dispersion of floating plastic objects over large distances globally, leading to particularly high concentrations in the surface ocean of remote subtropical oceanic gyres. With amounts of plastic exceeding one million pieces per km2 and hundreds of kilograms per km2, these waters are often referred to as ocean garbage patches. At present, the density and spatial variability of floating macroplastic (> 50 cm) in the subtropical gyres is still poorly understood, primarily due to limited observational tools. However, such information is crucial to more accurately quantify the global inventory of plastic debris in the world’s oceans and monitor its evolution, which in turn represents critical data for ecotoxicological risk assessments and for optimizing mitigation strategies.
This study uses fixed-wing Unmanned Aerial Vehicles (UAVs) to obtain photo grid surveys of the ocean surface in the North Pacific subtropical gyre during a 6-week offshore expedition in July-August 2021. We scanned almost 100 km2 of ocean surface in 22 flights. Plastic density maps, created by applying our previously developed object detection model (de Vries et al., 2021), reveal large daily fluctuations of background density, in addition to strongly clustered floating macroplastic (> 50 cm) within the area scanned by each flight. The UAV campaign results provide a dataset that sparks new insights into the plastic density in the North Pacific subtropical gyre, and that can aid in refining advection models specifically for floating macroplastic (> 50 cm).

Plastic litter and debris are now found all over the globe, from remote plains and mountains to estuarine systems and ocean waters. In the aquatic environment, plastic litter is fractionated into smaller sizes (nano or micro-plastics, diameter < 5 mm) and undergoes biogeochemical modifications through biofouling or incorporation into organic polymers. The diversity of plastic compounds and highly variable modifications occurring in the water column make it very difficult to have a systematic approach for documenting and classifying the optical properties of plastic litter from the field or laboratory. Plastic compounds are commonly birefringent and/or semi-transparent. As a result, knowledge of their polarization characteristics might be an asset for plastic detection and monitoring from spectro-polarimetric sensors.
In this study, full single scattering and radiative transfer computations were conducted to model the polarized water-leaving radiation of submerged plastic particles based on empirical size distributions and refractive indices. Simulations were also performed for coated plastic particles to mimic biofouling effects. The radiance and other Stokes vector terms (i.e., Q and U) were simulated at several levels in the atmosphere- water column system including top-of-atmosphere. The impacts of plastic compound, size and concentration were analyzed in terms of the linear degree of polarization and angle of polarization. This analysis was performed for several wavelengths in the visible-near-infrared part of the spectrum and for a comprehensive set of viewing geometries and Sun elevations. These results enabled the characterization of polarization signatures as measured from polarimetric sensors located directly in the water column, just above the water surface, or at the satellite level. Finally, the detectability of plastics from space with past, present, and future polarimetric satellite missions, including the PACE mission, was quantified and methods for field validation were delineated.

Carbon exchanges between the surface and the atmosphere mediated by plant photosynthesis are a key component of the terrestrial carbon balance, as photosynthesis is the mechanism for atmospheric carbon uptake by terrestrial vegetation and assimilation at the surface as accumulated biomass. Vegetation photosynthesis is highly variable according to environmental factors, particularly when plants are exposed to variable stress conditions, and such variability is enhanced under climate change and human pressure. A better quantitative estimation of the actual carbon assimilation by plants is needed to improve the accuracy of current terrestrial carbon models and to improve the predictability of such models towards future scenarios.

While Earth Observation (EO) techniques have been used extensively to model and quantify terrestrial vegetation dynamics, by determining the structure and functioning of plants, a direct estimate of actual photosynthetic activity by vegetation is only possible by quantifying not only the actual absorbed light by plants but also how the absorbed light is internally used by the plants. In fact, only a fraction of such absorbed light is used for photosynthesis, but such fraction is highly variable in space and time as a function of environmental conditions and regulation factors. Measuring chlorophyll fluorescence together with the total absorbed light, in addition to other key plant variables, provides a unique opportunity to quantify vegetation photosynthesis and its spatial and temporal dynamics by satellite observations.

The Fluorescence Explorer (FLEX) mission was selected in 2015, by the European Space Agency (ESA), as the 8th Earth Explorer within the Living Planet Programme, with the key scientific objective of a quantitative global mapping of actual photosynthetic activity of terrestrial ecosystems, with a spatial resolution adequate to resolve land surface processes associated to vegetation dynamics. FLEX will also provide quantitative physiological indicators to account for vegetation health status and environmental stress conditions, to better constraint global terrestrial carbon models.

Spatial coverage is driven by the need to globally observe all terrestrial vegetation, while optimal observation time around 10:00 is driven by the diurnal cycle of photosynthetic processes. The spatial resolution of 300 m is driven by the need to resolve land processes at appropriate scales relevant for the identification and tracking of stress effects on terrestrial vegetation, covering at least several annual cycles. The targeted uncertainty for photosynthesis derived from instantaneous measurements ranges from about 5% in unregulated conditions up to 30% in case of highly variable regulated heat dissipation, in line with model requirements and improving current capabilities provided by other techniques.

Given the four main pathways for light absorbed by plants (photochemistry, constitutive heat dissipation, chemically regulated heat dissipation and fluorescence) FLEX measurements include not only fluorescence emission, but also vegetation temperature and estimates of regulated heat dissipation, which is highly variable and drives the relation between fluorescence and photosynthesis. In addition, FLEX has also to acquire all the necessary information to determine vegetation conditions to properly interpret the photosynthesis variability, and all the necessary information needed for appropriate cloud screening, compensation for atmospheric effects and proper analysis of the measured signals.

For the retrieval of vegetation fluorescence, very high spectral resolution (better than 0.3 nm) is needed, with also very high signal-to-noise ratio given the weakness of the fluorescence signal as compared to the background reflected radiance. To be able to accomplish such objective, the FLEX mission carries the FLORIS spectrometer, with a spectral sampling in the order of 0.1 nm, specially optimized to derive, spectrally resolved, the overall vegetation fluorescence spectral emission in the full range 650-780 nm, and also measuring the spectral variability in surface reflectance in the range 500-650 nm indicative of chemical adaptations in regulated heat dissipation.

FLEX is designed to fly in tandem with Copernicus Sentinel-3. Together with FLORIS, the OLCI and SLSTR instruments on Sentinel-3 provide all the necessary information to retrieve the emitted fluorescence, and to allow proper derivation of the spatial and temporal dynamics of vegetation photosynthesis from such global measurements. OLCI and SLSTR data also help in the compensation for atmospheric effects and the derivation of the additional biophysical information needed to interpret the variability in fluorescence measurements.

The science products to be provided by the FLEX mission are not restricted to the basic chlorophyll fluorescence measurements, but include also the estimates of regulated and non-regulated heat dissipation, needed to quantify actual photosynthesis. Together with canopy temperature and other variables characterizing vegetation status, such as chlorophyll content and fraction of light absorbed by photosynthetic pigments, Level-2 products include instantaneous photosynthesis rates and estimates of vegetation stress based on ratios between actual versus potential photosynthesis and variable PSI/PSII contributions tracking photosynthesis dynamics. Level 3 products are derived by means of spatial mosaics and temporal composites, giving also as a temporal product the activation / deactivation of photosynthesis, growing season length and related vegetation phenology indicators. Finally, by means of data assimilation into advanced dynamical models of FLEX time series and ancillary information, Level-4 products are also provided, including time series of Gross Primary Productivity (GPP) and more advanced dynamical vegetation stress indicators.

Such science products can be directly used by vegetation dynamical models, climate models and different applications. In particular, with the increase population and food demand, usage of agriculture will need optimization of crop photosynthesis in such variable conditions, and FLEX is expected to contribute not only to the carbon science but also in associated applications. Efforts are put in place to guarantee proper cal/val activities and dedicated validation network for FLEX products. Particular efforts are in place to provide each product with realistic and properly estimated uncertainties, and also to propagate the derived uncertainties from the original satellite data until the final high-level products, accounting in each step for the covariance matrices associated to each set of intermediate variables, and using a combination of Montecarlo and analytical error propagation tools along the whole FLEX data processing chain.

The availability of validated ready-to-use high level science products will allow an extensive scientific usage of FLEX data, with also a high potential for derived applications. The open availability of FLEX products, and the accessibility through open exploitation platforms where users can themselves validate the products or even make changes in the algorithms to optimize the products towards their specific needs, is intended to offer a large versatility in FLEX exploitation approaches. FLEX is also expected to be used in conjunction with many other sources of EO data, including high spatial resolution time series from Sentinel-2. The FLEX Level-2 products are already provided in the same geographical grid as Sentinel-2 products to facilitate such exploitation developments. Usage of common global multi-resolution spatial grids for high-level products, and compatibility of data formats, are also taken into account to maximize the inter-operability of FLEX products with other EO products in global data assimilation approaches and multiple applications.

FLEX (FLuorescence EXplorer) is the 8th Earth Explorer mission currently being developed by ESA with the objective to perform quantitative measurements of the solar induce vegetation fluorescence. It will advance our understanding of the functioning of the photosynthetic machinery and the actual health of terrestrial vegetation. The fluorescence signal measured from space is so faint that additional information is required for an accurate retrieval and interpretation of the vegetation Fluorescence emissions. Hence the FLEX satellite will fly in convoy with a Sentinel-3 satellite for close temporal coregistration of its OLCI and SLSTR measurements.
The FLEX project development started in 2016 with the technologically challenging FLORIS Instrument by the instrument contractor Leonardo (Italy) with OHB (Germany) in its core team. The platform development was then kicked-off in 2019 with ThalesAleniaSpace (France) as the overall satellite prime contractor. A major milestone has been achieved by completing the instrument and satellite Critical Design Reviews beginning of 2022, allowing now to move into the phase of flight hardware manufacturing and integration.
An overview of the development progress will be provided including an outlook of the future project activities to get ready for launch in 2025.

The FLEX instrument Performance Simulator (FIPS) is a software allowing the simulation of synthetic raw data representative of the FLEX instrument radiometric, spectral and geometric performances. The FIPS simulates the optical performance of the instrument telescope and the two spectrometers. Particular emphasis was put in simulating the straylight performance of the instrument. The full acquisition chain from detectors to onboard data generation is also simulated allowing the generation of instrument source packets.
The Ground Prototype Processor (GPP) processes both synthetic and real instrument Earth Observation data, from the instrument source packet up to the Level 1B user product. The processing includes dark signal removal, smearing correction, non-linearity correction, straylight correction, absolute radiometric calibration and flat field equalization. The resulting Level 1B product includes geolocated top-of atmosphere radiances, associated data quality information, meteorological data and instrument characteristics required for further processing of the data to the Level 2.
In addition, the GPP is also designed to process data from the instrument whilst operating in various calibration modes. This functionality will enable in-flight characterization and calibration of the instrument.
The instrument radiometric calibration will be performed in-flight using a sun diffuser. It will be further monitored and validated using regular observations of the moon and deep convective clouds. The non-linearity of the instrument detector chain will be characterized on ground and then verified in-flight using natural targets at various level of signal and associated instrument integration times.
The in-flight spectral characterization will be based on the measurements of atmospheric absorption features as well as solar absorption lines observed on the onboard sun diffuser.
The absolute geometric performance will be monitored and corrected for through spatial feature matching of nominal EO data with a database of georeferenced high spatial resolution (30 m) Landsat images. The spatial coregistration between the high resolution and low spectral resolution spectrometers will be ensured by spatial feature matching between the two spectrometers data.

ESA’s 8th Earth Explorer mission FLEX aims at mapping Sun-induced fluorescence (SIF) as a proxy to quantify photosynthetic activity of terrestrial vegetation. The mission consists of a single platform (FLEX) carrying a hyperspectral imaging spectrometer (FLORIS). Flying in tandem with Copernicus’ Sentinel-3 satellite, FLEX will exploit the synergies with OLCI and SLSTR instruments. The complexity of the FLEX mission concept, its stringent mission requirements, and the large data volume impose important challenges to the Level-2 processing algorithms.

Within ESA/ESTEC FLEX Level-2 Study, a consortium made of remote sensing scientists and industry is joining forces to tackle these challenges, developing a Level-2 data processing prototype that can accurately retrieve SIF emission from space. The Level-2 data processing chain consists in four modules:
Level-1C: creating a synergistic product that includes co-registered FLEX and Sentinel-3 instrument data, quality flags and pixel classification, as well as a refined FLORIS spectral/radiometric calibration.
Level-2A: characterizing the atmospheric conditions (water vapor and aerosol optical properties) with a state-of-the-art synergistic approach using Sentinel-3 and FLEX data. This module provides an accurate inversion of the surface (apparent) reflectance within 1% error.
Level-2B: deriving the full spectrum of SIF emission from terrestrial vegetation by an efficient spectral fitting method within 0.2 mW/m2/sr/nm error.
Level-2C: providing vegetation biophysical parameters and a measurement of the non-photochemical quenching in order to interpret the SIF signal and its link to vegetation photosynthesis.

The FLEX Level-2 Study just finished its 3rd year of activities with a fully implemented software that it is functionally and scientifically validated. The current validation results show high accuracy of Level-1C calibration, atmospheric correction and subsequent SIF retrieval. In this presentation we will give an overview of the project activities and the current status of the developed algorithms, validation results and on-going challenges. The goal is to promote discussion with the scientific community and industry to consolidate the Level-2 mission products and data processing algorithms. In this presentation, we will describe the entire Level-2 data processing chain, and demonstrate the current accuracy of the implemented algorithms through the latest performance assessment results.

FLEX-E is the FLEX Level-2 end-to-end mission performance Simulator for ESA’s FLEX Earth Explorer-8. It is a key tool to demonstrate the feasibility of the whole FLEX mission concept and the baseline for the mission Ground Segment. It is also a versatile scientific tool, allowing to simulate the variability of ground, atmospheric and observation conditions that the FLEX – Sentinel-3 tandem mission will face, as well as to test the impact, on the L2 scientific products, of the constraints imposed by the technical solutions adopted for the FLEX instrument/platform.
FLEX-E has a modular architecture, based on the concept defined in ESA’s ARCHEO-E2E project. All FLEX-E modules are integrated within the OpenSF generic simulation framework. The “core” is the Scene Generation Module (SGM), designed to provide reference scenes in ground coordinates, with subpixel resolution, in a consistent manner for all the FLEX / Sentinel-3 tandem mission instruments. The SGM allows the different instruments to “fly” over the same scene (with different viewing geometries and spectral configurations), as it will be the case in real FLEX operations over a given geographical area. The interaction of the incoming solar radiation with vegetation and soils, and its propagation up to the Top of the Atmosphere (TOA), is performed with two coupled radiative transfer codes: SCOPE and MODTRAN. Through this, the SGM is able to generate the TOA radiance hypercube that is the input of the FLORIS and Sentinel-3 instrument modules. The orbit, attitude and observation geometry, needed for the scene generation, is provided by two Geometry Modules (GM and S3G), for the FLORIS and Sentinel-3 spectrometers, respectively.
The FLORIS instrument module, developed in a parallel project, comprises two chained submodules. The FLEX Instrument Performance Simulator (FIPS) models first the conversion of TOA radiances to digital numbers, including all the instrument spatial, spectral and radiometric effects and errors. Then, the Ground Processor Prototype (GPP) generates the L1b data products from the raw data, by implementing the calibration and correction of systematic errors. For the Sentinel-3 data flow, the Instrument+L1 Processing Module (S3M) models a simplified S3 sensors’ behaviour (OLCI and SLSTR), both in the spatial and spectral domain, and the L1 Ground Processing for the generation of Level-1 products.
At the end of the chain, the Level-2 Retrieval Module (L2RM), also developed in a parallel project, ingests the L1b inputs from GPP and S3M and implements all the retrieval algorithms for the FLEX L2 products, including Top-of-canopy reflectance, Sun-Induced Chlorophyll Fluorescence (SICF) and high-level photosynthesis products.
The Mission Performance Assessment is finally done by comparing the L2 products with the L1b data and the reference data produced by the SGM assessing the performance with respect to the Mission requirements provided in MRD document. This is achieved through two dedicated Performance Assessment Modules (PAM) for L1 and L2 mission requirements’ evaluation, and a parallel generic PAM configured for FLEX’s MRD. FLEX-E allows the independent evaluation of the impact of different instrumental configuration and effects, as well as “real world” scenarios (ground, atmosphere, observational) in the L2 retrieval.
The overall design and architecture of FLEX-E is presented, together with the status of its implementation and the latest results of the FLEX mission performance assessment.

In 2025 the European Space Agency (ESA) will launch the FLuorescence EXplorer (FLEX) mission, which will provide global maps of vegetation fluorescence that can reflect photosynthetic activity and plant health and stress. In turn, this is not only important for a better understanding of the global carbon cycle, but also for agricultural management and food security. FLEX will fly in tandem with the Copernicus Sentinel-3 mission, in particular working in combination with the OLCI and SLSTR instruments Sentinel-3 carries.
FLEX being in its implementation phase, the preparations for the later product validation started by means of mainly ground and airborne instrumentation. The gathered data provide fundamental information about the underlying processes and even more important confidence of data products and their required uncertainties. One challenge in this context is a comprehensive understanding and characterization of measurement uncertainty of the proposed validation datasets and the spatial and temporal support or representativity of these.
These data also form the basis for defining future validation activities and identifying potential key Cal/Val issues.
We will provide an overview of the future validation strategies for FLEX and how these integrate into a broader validation strategy for the Sentinel-3 and FLEX tandem and earth observation science strategy for the carbon cycle. In addition, we will highlight recent activities and outline planned activities for the coming years.

Studies of land-cover and land-use change (LCLUC) on a global scale became possible when the first satellite of Landsat series was launched 50 years ago. Since then, land-change science has been rapidly developing to answer the questions on where changes are occurring, what is their extent and over what time scale, what are their causes, their consequences for ecosystems and human societies, their feedbacks with climate change, and what changes are expected in the future. LCLUC studies use a combination of space observations, in situ measurements, process studies and numerical modeling. To get the most out of current remote sensing capabilities researchers strive to utilize data sources, different in space and time resolution and in electromagnetic range. Fusing observations from optical sensors with radar data helps filling the cloud-induced gaps in optical data. The goal is to develop multi-sensor, multi-spectral methods to increase the spatiotemporal coverage and to advance the virtual constellation paradigm for moderate spatial resolution (10-60m) land imaging systems with continental to global scale coverage. Also, the use of commercial satellite very high (meter) resolution is accelerating with more data becoming available and accessible. On the other hand, socioeconomic research plays an important role in land-change science and includes analyses of the impacts of changes in human behavior at various levels on land use. Studies of the resultant impacts of land-use change on society, or how the social and economic aspects of land-use systems adapt to climate change are becoming more and more important as the climate crisis issues draw increasing attention.

The NASA LCLUC Program is developing interdisciplinary approaches combining aspects of physical, social, and economic sciences, with a high level of societal relevance, using remote sensing tools, methods, and data. The Program aims at developing the capability for annual satellite-based inventories of land cover and land use to characterize and monitor changes at the Earth’s surface to improve our understanding of LCLUC as an essential component of the Earth System. The Program currently focuses on detecting and quantifying rapid LCLUC in hotspot areas and examining their impact on the environment and interactions with climate and society. This talk will summarize the Program’s achievements during the 25 years since its inception with an emphasis on the most recent findings. It will describe the synergistic use of multi-source land imaging data including those from the instruments on the International Space Station. The examples will cover various land-cover and land-use sectors: forests, grasslands, agriculture, urban and wetlands.

The existing CCI Medium Resolution land cover (MRLC) product delineates 22 primary and 15 secondary land cover classes at 300-meter resolution with global coverage and an annual time step extending from 1992 to the present. Previously, translation of the land cover classes into the plant functional types (PFTs) used by Earth system and land surface models required the use of the CCI global cross-walking table that defines, for each land cover class, an invariant PFT fractional composition for every pixel of the class regardless of geographic location.
Here, we present a new time series data product that circumvents the need for a cross-walking table. We use a quantitative, globally consistent method that fuses the 300-meter MRLC product with a suite of existing high-resolution datasets to develop spatially explicit annual maps of PFT fractional composition at 300 meters. The new PFT product exhibits intraclass spatial variability in PFT fractional cover at the 300-meter pixel level and is complementary to the MRLC maps since the derived PFT fractions maintain consistency with the original land cover class legend. This was only possible by ingesting several key 30m resolution global binary maps like the urban, the open water, the tree cover, the tree height while controlling their compatibility thanks to the MRLC maps.
This dataset is a significant step forward towards ready-to-use PFT descriptions for climate modeling at the pixel level. For each of the 29 years, 14 new maps are produced (one for each of 14 PFTs: bare soil, surface water, permanent snow and ice, built, managed grasses, natural grasses, and trees and shrubs each split into broadleaved evergreen, broadleaved deciduous, needleleaved evergreen, and needleleaved deciduous), with data values at 300-meter resolution indicating the percentage cover (0–100%) of the PFT in the given year.
Based on land surface model simulations (ORCHIDEE and JULES models), we find significant differences in simulated carbon, water, and energy fluxes in some regions using the new PFT data product relative to the global cross-walking table applied to the MRLC maps. We additionally provide an updated user tool to assist in creating model-ready products to meet individual user needs (e.g., re-mapping, re-projection, PFT conversion, and spatial sub-setting).

Driven by advancements in capabilities of satellite data acquisition and processing and continued interests in monitoring the earth’s surface for a variety of needs, global land cover (GLC) mapping efforts have seen accelerated progress. As such, several GLC maps have been produced with increased temporal resolution containing annual updates and with increased spatial resolution e.g., at 10m resolution. However, the validation of GLC maps has not kept up the same pace as the map generations. Most GLC maps are validated using statistically rigorous accuracy assessment methods following internationally promoted guidelines (CEOS Stage-3). Still, updates (e.g. annual or per epoch) on GLC maps often lack rigorous accuracy assessments. Considering that validation datasets are collected using human interpretation, which is costly and time-consuming, validation datasets should be designed to be easily adjustable for timely validation of new releases of land cover products and also be suitable for assessing multiple maps.
Aiming towards operational land cover validation, this study presents a framework for operational validation of annual global land cover maps using efficient means for updating validation datasets that allow timely map validation according to recommendations in the CEOS Stage-4 validation guidelines (Figure 1)(Tsendbazar et al. 2021). The framework includes a regular update of a validation dataset and continuous map validation. For the regular update of a validation dataset, a partial revision of the validation dataset based on random and targeted rechecking (areas with a high probability of change) is proposed followed by additional validation data collection. For continuous map validation, an accuracy assessment of each map release is proposed including an assessment of stability in map accuracy targeting users that require multi-temporal maps.

This validation framework was applied to the validation of the Copernicus Global Land Service GLC product which includes annual GLC maps from 2015 to 2019. We developed a multi-purpose global validation dataset that is suitable for validating maps with 10-100m resolution for the reference year 2015 (Tsendbazar et al. 2018). As part of the operational validation, this dataset was updated to 2019 based on partial revision consisting of random and targeted revisions. The BFAST time series algorithm was used to target sample locations that are possibly changed during the update period. Additional sample sites were also collected to increase the sampling intensity in the land cover change areas.

Through this updating mechanism, we validated the annual GLC maps of the CGLS-LC100 product for 2015–2019. We further assessed the stability in class accuracy over this period. Implementation of this operational validation framework in the context of the Copernicus Global Land Service GLC product will be presented.
Since the validation dataset is a multi-purpose validation dataset that allows validating maps with 10-100m resolution, this dataset was further updated to the year 2020 to validate ESA’s WorldCover 2020 GLC map which is based on Sentinel 1 and Sentinel 2 data at 10m resolution. The approach and results of validating the WorldCover 2020 GLC map will also be included in this presentation.
As more operational land cover monitoring efforts are upcoming, we emphasize the importance of updated map validation and recommend improving the current validation practices towards operational map validation so that long-term land cover maps and their uncertainty information are well understood and properly used.
Index Terms— land cover validation, operational monitoring, dataset update, global land cover


Related literature
Tsendbazar, N., Herold, M., Li, L., Tarko, A., de Bruin, S., Masiliunas, D., Lesiv, M., Fritz, S., Buchhorn, M., Smets, B., Van De Kerchove, R., & Duerauer, M. (2021). Towards operational validation of annual global land cover maps. Remote Sensing of Environment, 266, 112686
Tsendbazar, N.E., Herold, M., de Bruin, S., Lesiv, M., Fritz, S., Van De Kerchove, R., Buchhorn, M., Duerauer, M., Szantoi, Z., & Pekel, J.F. (2018). Developing and applying a multi-purpose land cover validation dataset for Africa. Remote Sensing of Environment, 219, 298-309

1. Introduction
Land cover is one of the main environmental climate variables (ECVs) as it is highly correlated with climate change. In this context, in the framework of the Climate Change Initiative (CCI) of ESA [1], the High Resolution Land Cover (HRLC) project is aimed to study the role of the spatial resolution in the mapping of land cover and land-cover changes to support climate modelling research [2]. Land cover and related changes are indeed both cause and consequence of human-induced or natural climate changes. This has been demonstrated by the previous phase of the CCI program, focused on the generation of Medium Resolution (MR) Land Cover maps at global scale. Differently from the MR land cover CCI, which provided annual land cover maps at 300m resolution in the period 1992-2020 [3], the HRLC project produces regional maps characterized by a spatial resolution of 10m/30m. Moving from 300m to 30m requires the definition of new data analysis methods, reframing the perspective with respect to the MR project both from the theoretical and the operational viewpoints. Although HR potentially increases the capability of a detailed analysis of spatial patterns in the land cover, many challenges are introduced with respect to the MR case and limitations in the available data make the development of products at very large scale very challenging.
This contribution presents the architecture and the methodologies developed for implementing the full processing chains that have been developed to process Earth Observation (EO) data and generate the HRLC products. The primary products of the project consist of: (i) HR land-cover maps at subcontinental scale at 10m as reference static input (generated for 2019 only) to the climate models, (ii) a long-term record of regional HR land cover maps at 30m in the regions identified for the historical analysis every 5 years (generated in the period 1990-2015), and (iii) change information at 30m at yearly scale consistent with historical HR land-cover maps.

2. Methodology
The development of the proposed architecture was based on the observation that temporal availability of HR data in the past/current archives is much lower than that of the MR ones and strongly varies across the years. Differently from the MR case (e.g., SPOT-Vegetation archive), no daily acquisitions are available and only in the very recent years it was possible to get a quite dense temporal sampling due to Sentinel and Landsat-8 missions. Prior to them, the number of yearly-based images available in archives dramatically reduces (being Landsat Thematic Mapper, ASAR and ERS-1 and 2 the most relevant data sources), resulting in a much more challenging problem for the development of HRLC products. This scenario led to a complex process to produce historical time series of products. Moreover, it required a shift in the processing paradigm that moves from the analysis of many images per year acquired at MR to a few images (for some areas and years single or no images are available) characterized by high spatial resolution.

To produce the land-cover maps, two multisensor (optical and SAR) processing chains have been designed and implemented: one is based on the exploitation of Sentinel 1 (S1) and Sentinel 2 (S2) images for the generation of maps at 10 m resolution (used in the project for generating products in 2019) (Figure 1) and the other one generates historical maps every 5 years going back to 1990 by exploiting Landsat (Enhanced) Thematic Mapper images and ASAR and ERS-1/2 data (Figure 2). Both architectures share two pre-processing branches (one for optical and the other for SAR data) and a fusion module for the final map production. The main difference between the two processing chains is related to the pre-processing techniques (which consider the large differences in data quality and availability between Sentinel and previous missions) and in the paradigm exploited for the classification mechanism. The S1/S2 architecture classifies independently the time series of images acquired in the target year (in the project 2019) and generates the land-cover products by fusing the classification results obtained independently on the two branches (optical and SAR) by using consensus theory and Markov Random Field approaches [4]. The historical processing chain assumes as baseline the classification results generated with S1/S2 data and exploits the cascade classification paradigm [5] to properly model the temporal correlation between images when producing the historical land-cover maps. This is done to mitigate the well-known problem of error propagation in multitemporal classification, which is extremely critical when independent classification of multitemporal data is performed. The cascade classification paradigm is robust and theoretically well founded as it is based on the Bayesian decision theory. The classification techniques included in the optical and SAR branches include “shallow” machine learning techniques (Support Vector Machines, Random Forest) and specific SAR detectors focused on built -up and water related classes [6]. Both architectures provide in output uncertainty measures for the classification of each pixel in the map and also indications on the second alternative class for a better representation of the real complex conditions on the ground. These are crucial information to be given as input to the climate modelling task when using the generated products. Specific methodologies have also been devised to support the definition of the training sets to be used for the 2019 and the historical image classifications [7].

To produce land-cover change maps every year, a third architecture has been defined (figure 3) that is driven by the cascade classification output and is aimed at identifying the location in time of the changes on a yearly base. This allows to localize in time the changes occurred between 5 years maps. The change detection products have been generated by using optical data acquired by Landsat (Enhanced) Thematic Mapper. The main challenge is related to the very uneven distribution of data available in different areas and in different years. This was addressed by defining an architecture based on a feature extraction module, a time series regularization module (based on a “shallow” neural network) and an abrupt change detection module [8]. The change detection products have associated reliability information for each pixel in terms of the probability of change.

3. Product generation and conclusion
The processing chain has been developed according to the use of dockers and with the requirement to be able to process big data volumes of optical and SAR images. Processors have been fully integrated in Python-based pipelines that automatically retrieve the needed products for the specific task and perform the processing. The production has been run on Amazon Web Services (AWS) cloud computing, even if the processing chain is flexible and can be run on DIAS and other cloud infrastructures.
The Climate User Group involved in this project defined three large regions of particular interest to study the climate/LC feedbacks in three continents involving climate (tropical, semi-arid, boreal) and complex surface atmosphere interactions that have significant impact not only on the regional climate but also on large-scale climate structures. The three regions are in Amazon basin, the Sahel band in Africa and in the in the northern high latitudes od Siberia.
The products generated on the three areas have accuracies that given the complexity of the task and of the legend of land covers classes (which includes also seasonal classes) are satisfactory (see the “ESA CCI High Resolution Land Cover Products” presentation).

References
[1] ESA – European Space Agency: ESA Climate Change Initiative description, EOP-SEP/TN/0030-09/SP, Technical Note – 30 September 2009, 15 pp., 2009.
[2] L. Bruzzone et al, "CCI Essential Climate Variables: High Resolution Land Cover,” ESA Living Planet Symposium, Milan, Italy, 2019.
[3] P. Defourny et al (2017). Land Cover CCI Product User Guide Version 2.0. [online] Available at: http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf
[4] D. Tuia, M. Volpi, G. Moser, “Decision fusion with multiple spatial supports by conditional random fields,” IEEE Trans. Geosci. Remote Sens., 56, 2018
[5] L. Bruzzone, R. Cossu, A multiple cascade-classifier system for a robust a partially unsupervised updating of land-cover maps, IEEE Trans. on Geosci. Remote Sens., Vol. 40, 2002.
[6] T. Sorriso, D. Marzi, P. Gamba, ”A General Land Cover Classification Framework for Sentinel-1 SAR Data.” in Proc. of the 2021 IEEE 6th Int. Forum on Research and Tech. for Society and Industry (RTSI), Napoli,. 2021.
[7] C. Paris, L. Orlandi, L. Bruzzone, “A Strategy for an Interactive Training Set Definition based on Active Self-Paced Learning,” IEEE Geosci. Remote Sens. Letters, Vol. 17, 2021.
[8] Y.T. Solano-Correa, K. Meshkini, F. Bovolo, L. Bruzzone, “A land cover-driven approach for fitting satellite image time series in a change detection context,” SPIE Conf. on Image and Signal Processing for Remote Sensing XXVI, 2020.


List of the other HRLC team members: C. Domingo (CREAF), L. Pesquer (CREAF), C. Lamarche (UCLouvain), P. Defourny (UCLouvain), L. Agrimano (Planetek), A. Amodio (Planetek), M. A. Brovelli (PoliMI), G. Bratic (PoliMI), M. Corsi (eGeos), C. Ottlé (LSCE), P. Peylin (LSCE), R. San Martin (LSCE), V. Bastrikov (LSCE), P. Pistillo (EGeos), M. Riffler (GeoVille), F. Ronci (eGeos), D. Kolitzus (GeoVille), Th. Castin (UCLouvain), R. San Martin (LSCE-IPSL), C. Ottlé, V. Bastrikov (LSCE-IPSL), Ph. Peylin (LSCE-IPSL).

With the rise of distributed constellations (Landsat, Sentinel, VIIRS, MODIS, Planet, Airbus, Maxar) there has been a general push to make earth observation data interoperable. This has led to the notion of harmonized data products. Moreover, there is a strong incentive for these data sources to be combined into virtual constellations to achieve high revisit rates for resulting sensor fusion products. Distributed constellations of small commercial satellites can deliver data that is higher in information density and radiometrically accurate when paired with traditional missions. Today, the need for high cadence time series to quantitatively leverage bio-optical models of vegetation and measure the impact human activity is driven by the urgency to measure the environmental dimensions of “sustainable development.”
Under the sponsorship of the European Union’s Horizon 2020 programme, we are exploiting the notion of a virtual constellation for the purpose of updating and maintaining the CORINE (CLC) land cover product, which is the flagship of the Copernicus Land Monitoring Service (CLMS). In our approach, we fuse global daily imagery from the PlanetScope contributing mission with Landsat 8, Sentinel-2, VIIRS, MODIS to create cloud free, harmonized, 3 m resolution, near-daily time series covering three full years starting from the latest CLC release: 2018, 2019, 2020. We sample half a million data cubes across the entire territory of the EU. We sample from each country relative to country surface area and perform stratified sampling with respect to the 44 CLC land cover classes. We label the data cubes based on the land cover classes present at each location in the 2018 reference year and we use this corpus to train machine learning models that can learn how to recognize the “pulse” of land cover types and detect changes on a short time scale in subsequent years.
The challenge is to develop novel AI architectures that can properly exploit the unprecedentedly high spatiotemporal resolution of these data streams and provide new insights into land cover dynamics. Exploratory data analyses show that 3 m daily time series of spectral indices such as NDVI are powerful indicators of biodiversity and excellent discriminators of land cover types. For instance, simple clustering of these fine temporal signatures can lead to segmentation of tree species at the crown level based on intraspecific and interspecific variations in leaf phenology measured in early spring and throughout the fall season, leading to a better assessments of forest composition. The same can be said about the phenometrics of agricultural crops and wild vegetation in general when captured at this scale. The high temporal cadence helps us improve our understanding of land use and challenging habitats such as wetlands, grasslands and pastures.
Our baseline models are supervised classification models that employ Convolutional Neural Networks (CNN) for spatial encoding of class distributions and Recurrent Neural Network (RRN) for modelling their temporal evolution. Of particular interest is the development of weakly or fully unsupervised spatiotemporal deep learning models that can learn to disentangle structural change from phenology based on multi-year observations and in the absence of labels. These constitute our more advanced models and rest on methodologies for self-supervised image representation learning. We compare and evaluate the potential impact of all these architectures for more continuous updates of the CLC product by showing results drawn from large regions of interest in Europe.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356, Copernicus evolution: Research activities in support of the evolution of the Copernicus services.

The European Commission has been working to maximize the adoption of Copernicus data and information in Europe, to increase its socio-economic benefits for European citizens and businesses and to support the competitiveness European Earth Observation industry. To achieve these objectives, it is essential to continue and optimize the Copernicus User Uptake activities.
Copernicus User uptake initiatives include training and skills development, such as the Copernicus Skills Programme, EO4GEO and MOOCs (Massive Open Online Courses), thematic trainings and workshops. Copernicus Academies and Relays have been in place since 2017 as local information and coordination points to facilitate activities around Copernicus, its benefits, and opportunities for local residents and businesses.
In addition, each of the six core services develops and implements user uptake activities regularly within their domains – Land, Marine, Climate, Atmosphere, Emergency response and Security. Copernicus Member States also initiate numerous user uptake activities, both through the Copernicus Framework Partnership Agreement (FPA) and in national capacities.
Since 2021, the European Union Agency for Space programme is in charge of developing of downstream market and fostering of innovation for Copernicus commercial users. The Commission is currently starting the work to formulate a new user uptake strategy for the entire EU Space Program, based on earlier assessments and recommendations.
Copernicus data and products are widely used for monitoring and reporting purposes, and are useful in the implementation of several EU policies and directives, such as the Green Deal, the EU Zero Pollution Action Plan, the Methane Strategy and the Global Methane Pledge, the new EU Arctic Policy and more. The Knowledge Centre on Earth Observation (KCEO), launched in April 2021, serves as a focal point to make use of EO data for EU policymaking decisions and implementation.
This presentation will cover the main pillars of the Copernicus User Uptake activities, future plans and strategies.

With the creation of the first-ever integrated Space Programme, the European Union is reinforcing its strategy to harness the power of space to re-ignite its post-COVID economy, address climate change, transit to digitalization, and secure its autonomy and sovereignty.
In 2021 the European Union Agency for the Space Programme (EUSPA) was created, bringing all EU space activities under one roof and enabling space to contribute effectively to the priorities of the European Union agenda.
EUSPA is also responsible for the development of downstream markets and fostering of innovation based on Galileo, EGNOS, and now also for the commercial users of Copernicus, leveraging funding mechanisms such as Fundamental Elements and Horizon Europe.
In particular, bringing management of downstream and combined applications based on Galileo, EGNOS and Copernicus under the umbrella of one agency will make it possible increasingly to leverage synergies. On their own, these technologies can play a key role in supporting a digital and green transformation, but leveraging their synergetic and combined use will facilitate the generation of innovative solutions that bring a higher societal impact.
EUSPA, as an EU user-oriented agency, makes sure that these challenges are addressed through the design and development of new EU space-based services which meet the needs of the users, while ensuring their market uptake.
EUSPA creates opportunities for EU companies to explore new markets, through research and development initiatives, grants and prizes to enable new business opportunities and connect them with private investors and venture capitalists for the necessary financing capability to jump-start their business cases.
In collaboration with the European Commission, ESA, the Copernicus Entrusted Entities and all other relevant actors of the earth observation ecosystem, EUSPA will launch a series of activities to foster the adoption of Copernicus data and services across different market segments. These activities will complement and leverage existing market development efforts and promote the creation of innovative solutions making use of EU Space services towards a sustainable green and digital transition.

Earth Observation (EO) is the measurement of physical, chemical and biological systems of our planet earth. It delivers proxies of measurable parameters which can be detected via a sensor (optical, radar, laser) influenced by a variety of parameters (e.g. clouds, atmosphere, solar winds). Earth observation includes airborne and satellite based measurements as well as in situ sensor data (e.g. air or water temperature) for the purpose of calibration.
Using EO data for statistical purposes can contribute to the survey methodology, the analytical possibilities, timeliness, spatial coverage and semantic harmonisation. Several statistical domains like tourism, transport (port activities, lorry traffic) and production estimates (e.g. oil storage, car, agriculture) do have the potential to use earth observation data for statistical data production.
The presentation will outline a limited set of usage of EO at Eurostat and the European Statistical Systems (ESS) - the national statistical data providers - at large.
Inside Eurostat provides the LUCAS survey micro-data on land cover and land use, as well as environmental information, serving as in-situ observation for EO, which has been used by Member States to produce classified Copernicus maps based on a subset and to validate the results (EEA, 2015). Additionally, for the reporting of the Sustainable Development Goals (SDG) EO derived information is used for a restricted set of parameters. Lastly, A variety of EO information from space and airborne sensors is provided centrally by GISCO to facilitate spatial analysis and visual interpretation.
Eurostat facilitates the usage of Earth Observation at ESS level in the National Statistical Systems with the ESSNET Big Data program, and with individual Member States through the GEOS grants. Examples presented include classical approaches like land use/land-cover mappings, determining urban sprawl, crop recognition and yield prediction systems up to advanced systems using Artificial Intelligence and Machine learning to detect solar cells on roofs to support knowledge on transformation of energy generation/SDG. In addition, some NSIs have managed relevant projects on their own budget to early test the opportunities and implication of using earth observation data on similar topics. To increase the internal knowledge specific sessions are integrated in the European Statistical Training Program (ESTP) course program.

Sentinels data are increasingly being used to support decision making at different levels. Mandated to contribute to the European Green Deal amid growingly complex challenges, European public authorities can leverage Copernicus as an effective tool to make informed decisions, enforce environmental policies and build more sustainable and resilient lifestyles for European citizens. For instance, the use of the data helps them to e.g. improve efficiency, save costs, improve coordination and communication to the public, deter unlawful behaviours, assess potential risks. Often, the synoptic and regular views provided by the Sentinels allow to improve environmental monitoring capabilities of the agencies in charge beyond what is currently possible.
Successful use cases are increasingly gathered and published for Copernicus, collectively contributing to support the evidence about the benefits brought by the use of EO/Copernicus data. Although each case presents specificities on its own, the analysis of common processes and challenges can greatly contribute to deepen the understanding of the nature of value of the EO-derived information and to improve the design of services and user uptake strategies.
In this presentation, we will provide an overview about two relevant initiatives procured by the ESA and the EC.
First, the “Copernicus4regions” initiative will be presented. This is an initiative undertaken in cooperation with the Network of European Regions using Space Technologies (NEREUS) and provides an outstanding collection of 99 examples gathered from European regional and local authorities (see http://www.nereus-regions.eu/copernicus4regions/). Each story is characterised by a usage maturity level defined by the primary user.
After this, an overview of the latest findings from the Sentinel Benefits Study will be presented. This study is managed by the European Association of Remote Sensing Companies and performs detailed impacts assessments for selected use cases along complete value chains (i.e. from data distribution to data exploitation to society at large), with particular focus on cases of benefits for public administrations (https://earsc.org/sebs ).

The Twin ANthropogenic Greenhouse Gas Observers (TANGO) mission is a pioneering satellite mission comprising two satellites, TANGO-Carbon and TNAGOTANGO-Nitro flying in loose formation. The mission concept is developed through ESA’s SCOUT program and currently consolidated to be ready for a timely launch. TANGO envisages a unique European contribution to monitor globally and independently the emission of the anthropogenic greenhouse gases over the period 2025-2028. To this end, breakthrough technology will be used to quantify emissions of the greenhouse gases methane (CH4) and carbon dioxide (CO2) at the level of individual industrial facilities and power plants. The mission will demonstrate a distributed monitoring system that can pave the way for future larger constellations of small-satellites allowing for enhanced coverage and temporal resolution. The TANGO mission consists of two agile small-satellites, each carrying one spectrometer. The first satellite measures spectral radiances in the shortwave infrared part of the solar spectrum (1.6 µm) to determine moderate to strong emissions of CH4 (≥ 10 kt/yr) and CO2 (≥ 5 Mt/yr). The instrument has a field of view of 30 x 30 km2 at spatial resolutions small enough to monitor individual large industrial facilities (≤300 x 300 m2), with an accuracy to determine emissions on the basis of a single observation. Using the same strategy, the second satellite yields collocated NO2 observations from radiance measurements in the visible spectral range, supporting plume detection and exploiting the use of CO2/NO2 ratio. In essence, TANGO will provide surface fluxes of specific emission types based on the combination of CH4, CO2 and NO2 observations at a high spatial resolution following strictly open data policy. Mission operation will be based on scientific input on target selection. In doing so, TANGO aims to uniquely complement the large current and planned Copernicus monitoring missions like Sentinel-5(P) and the CO2M mission by providing unrivalled high-resolution monitoring of the major anthropogenic greenhouse gas emissions at on a regular basis.

Monitoring and understanding the Earth’s magnetic field and the ionospheric environment is key for both fundamental science and multiple applications. The Earth’s magnetic field protects our planet from incoming energetic charged particles and organizes the way the near outer space (the magnetosphere) and the ionized upper layers of the atmosphere (the ionosphere) respond to solar activity. This response can produce strong magnetic signals that can affect ground technology such as power transmission networks, radiation hazards that can affect satellites in the near outer space, and multiple ionospheric perturbations that can severely affect radio transmissions, radars and GNSS systems (hazards collectively known as space weather hazards). Monitoring Earth’s magnetic field and ionospheric environment is crucial for investigating all these phenomena. Identifying and understanding Earth’s magnetic field multiple sources is also crucial to aid precise navigation, reveal properties of the shallow and deep Earth, and provide key information for geophysical surveying for minerals.

The very successful on-going ESA Earth Explorer Swarm constellation revealed the considerable science value of using a well-conceived satellite constellation for such investigations. Building on Swarm’s achievements, NanoMagSat has been designed to demonstrate the ability of New Space technology to bring such studies to the next level of success.

The constellation will consist of an innovative low-Earth orbit (LEO) constellation of three 16 U nanosatellites, with a current baseline of two 60° inclined and one polar orbits, allowing much faster local time coverage of all geographic locations up to 60° North and South latitudes than is currently possible with Swarm. This constellation would also allow even better coverage, should NanoMagSat be launched while Swarm is still in operation. Each satellite will carry an identical payload consisting of an advanced Miniaturized Absolute scalar and self-calibrated vector Magnetometer (MAM) combined with a set of precise star trackers (STR), a compact High-frequency Field Magnetometer (HFM), a multi-needle Langmuir Probe (m-NLP) and dual frequency GNSS receivers. This payload will allow the production of absolute vector magnetic data at 1Hz sampling, scalar and vector magnetic data at 2 kHz sampling, electron density data at 2 kHz sampling, electron temperature data at 1 Hz sampling, as well as TEC and ionospheric radio-occultation data.

As already demonstrated in the context of the NanoMagSat Scout consolidation phase carried out in 2020, this combination of data, particularly those acquired at higher rates than on Swarm, and the proposed new constellation configuration, will allow improvement of the type of monitoring and investigations Swarm (and previous missions such as Oersted and CHAMP) achieved, also bringing entirely new science opportunities.

Science primary objectives begin with the precise recovery of the field produced by the geodynamo within the Earth’s core. This field, also known as the Earth’s main field, is critical to characterize and understand the way the Earth reacts to the incoming flux of energetic charged particles (the solar wind). Its precise knowledge is also used for many practical applications, both ground-based and space-borne. Recovering its fast dynamics is a top priority, as our still-limited knowledge of this dynamics severely hampers present efforts, relying on data assimilation and advanced numerical dynamo models, to predict main field evolution at the level requested by users. Twenty years of space-born observations by Oersted, CHAMP and Swarm, combined with ground observation data, has allowed great progress, making it possible to study inter-annual changes as well as abrupt changes known as geomagnetic jerks. Thanks to recent progress in numerical dynamo simulations, we now know that further observations are needed to fully characterize and understand these phenomena, since much faster changes can occur. It has not been possible to study such rapid variations with the existing satellite constellations. NanoMagSat will have the ability to considerably improve the situation by capturing core field signals with periods of as short as three months.

A second set of primary objectives will be to also improve our ability to recover fast changing planetary scale ionospheric and magnetospheric fields. These also need to be better monitored and understood. The mid and low latitude ionospheric field typically varies on a daily and seasonal basis, but significant day-to-day variability occurs in response to solar activity. In contrast to Swarm, NanoMagSat will have the ability to recover such variability. The magnetospheric field shows even stronger and faster dynamics. The ability of the NanoMagSat constellation to cover all local time scales at mid-latitudes over its orbital period will also make its recovery much easier. Characterizing both these fields, and the companion fields produced by the electrical currents they induce in the solid Earth, will not only help understand the way the Earth responds to solar activity, but also help reconstruct the still poorly known conductivity structure of the solid Earth.

A third set of primary objectives will take advantage of the innovative payload combination of NanoMagSat to investigate the ionospheric environment. As demonstrated by the experimental “burst mode” of the absolute magnetometers (ASM) on board the Swarm satellites (scalar data acquired at 250 Hz), whistlers produced by lightning strikes in the neutral atmosphere can be detected at LEO altitude and used to sound the state of the ionosphere below the satellites. NanoMagSat will have an extensive ability to even better do so, thanks to the 2 kHz sampling rate of its vector magnetometers. Such information, together with the TEC data, ionospheric occultation data (which Swarm lacks), and local electron density data will provide a powerful means to monitor the state of the ionosphere, to both investigate its dynamics and improve ionospheric models, such as the International Reference Ionosphere (IRI) model. Investigation of the local smaller scale dynamics of the ionosphere will also be made possible thanks to the joint use of 2kHz sampling vector magnetic and electron density data. This will provide access to ionospheric meter-scale plasma density structures and allow monitoring of the electrical currents testifying for the energy input that feeds them from the magnetosphere.

Additional secondary, but equally important, science objectives have also been identified. Some are already addressed by Swarm, but would considerably benefit from both longer (ideally permanent) satellite observations and could also greatly benefit from the joint use of NanoMagSat and Swarm data, should NanoMagsat be launched while Swarm is still in operation. This is the case for the magnetic signals produced by oceanic lunar tides. NanoMagSat would allow these minute signals to be recovered faster and more accurately. As already demonstrated, these signals can be used to sense the electrical conductivity of the uppermost regions of the solid Earth. On the long term, they could also potentially be used to assess the evolution of the temperature of the oceans (the magnitude of the tidal signals being sensitive to ocean temperature), thus contributing to monitoring one key parameter of climate global change. Additional signals produced by the global ocean circulation and its variability could also potentially be investigated, at time (month to interannual) and length scales expected to be accessible with NanoMagSat.

Another important secondary science objective that could benefit from NanoMagSat, especially if operated jointly with the Swarm constellation, is the recovery of the magnetic field signals produced by magnetized rocks within the lithosphere. Maps of these provide invaluable information about the nature and thermal state of the lithosphere and its deep- seated rocks, as well as about their tectonic history. This objective requires making the best of all missions, as it benefits from the accumulation of data over long periods. The Swarm constellation (with two satellites side-by-side) was optimally designed for that purpose. However, the much better local time coverage provided by NanoMagSat could be taken advantage of in order to better remove signals produced by all other sources and assist in better isolating this lithospheric signal.

Many additional possible secondary objectives are also now under study, thanks to the support of the science community, following a dedicated NanoMagSat brainstorming session organized in Athens in October 2021, as a follow-up of the 11th Swarm Data Quality Workshop. In particular, exciting ideas have emerged that could take advantage of the ability of the NanoMagSat payload to study ionospheric-magnetospheric dynamics, as a stand-alone mission or in conjunction with other missions.

In this invited talk, we will strive to illustrate all the numerous science objectives NanoMagSat could achieve, also reporting on E2E simulations that will have been run by the time of the meeting. Since we just learned that following successful negotiations with ESA, the NanoMagSat project will soon (January 10, 2022) kick off a new technical phase of Risk Retirement Activities for a period of 18 months, we will also report on how we have started backing up this phase with appropriate science preparation activities. All scientists willing to contribute to this effort to further enhance the science return of the NanoMagSat mission and demonstrate the potential of New Space for such science are warmly welcome to join. Beyond this initial mission, NanoMagSat could indeed be used as a stepping-stone for permanent low-cost monitoring of the Earth’s magnetic field and ionospheric environment.

Scout missions are a new Element in ESA’s FutureEO Programme, demonstrating science from small satellites. The aim is to tap into the New Space approach, targeting three years from kick off to launch, and within a budget of €30m, including launch and commissioning of space and ground segments. The Scout missions are non-commercial and scientific in nature; data will be made available freely using a data service delivery approach. HydroGNSS has been selected as the second ESA Scout Earth Observation mission, primed by Surrey Satellite Technology Ltd, with support from a team of scientific institutions - Sapienza University of Rome, Institute for Space Studies of Catalonia, Finnish Meteorological Institute, Tor Vergata University of Rome, “Nello Carrara” Institute of Applied Physics, National Oceanography Centre and University of Nottingham. The microsatellite uses established and new GNSS-Reflectometry techniques to take four land-based hydrological climate variables; soil moisture, freeze/thaw, inundation and biomass. The initial project is for a single satellite in a near-polar sun synchronous orbit at 550 km altitude that will approach global coverage monthly, but an option to add a second satellite has been proposed that would halve the time to cover the globe, and eventually a future constellation could be affordably deployed to achieve daily revisits.

GNSS Reflectometry has been developing rapidly as an L-Band remote sensing technology suitable for small satellites, a form of passive bi-static radar that uses GNSS satellites as radar sources, and it continues to find applications over ocean, ice and land. The UK TechDemoSat-1 and NASA CYGNSS missions were primarily flown to target wind speed over the ocean but enabled demonstration of the potential for cryospheric and hydrological applications. Over rough surfaces, for example oceans, a resolution of approximately 25 km is expected, but over flat surfaces such as sheltered rivers, the signal becomes coherent and the resolution achieved can be better than 1 km, while the resolution achievable over land varies between 1 km and 25 km depending on the characteristics of the terrain. The ESA Scout-2 HydroGNSS mission will use established and new measurements, including Galileo signals, coherent reflection sensing, dual polarisation and dual frequency reflectometry exploration to improve the resolution and separation of hydrological measurements. Although targeting land, measurements will also be collected over ocean and ice, with the prospect of a number of secondary applications.

The instrument is a development based on technology used on TechDemoSat-1 and CYGNSS missions, upgraded to allow for additional measurements. The nadir antenna uses 2 x 2 array of metal patches to implement an efficient dual frequency dual polarisation antenna. Low noise amplifiers incorporate cavity filters and calibration switches and the receiver is able to collect multiple GPS and Galileo reflections simultaneously at dual polarisation and at dual frequencies, L1/E1 and L5/E5. Signal processing uses open loop predictions to target reflections at each specular point and collect measurements in the form of Delay Doppler Maps (DDMs). The platform is a 55kg implementation of the SSTL-Micro, incorporating many features advantageous to a GNSS reflectometry mission. The accurate and agile attitude capability is enabled by an attitude control system using star trackers and wheels. Propulsion allows for collision avoidance and phasing and maintenance of a future constellation. A large capacity data storage and high rate X-band downlink allows acquisition of targeted raw sampled captures in addition to the routine on-board processed DDMs.

The ground segment makes use KSAT station in Svalbard for both telemetry, telecommand and control link as well as payload downlink, where the high latitude frequent passes could be an enabler for fast data availability for future weather applications. SSTL’s ground station in Guildford is available as backup. The data is processed by the Payload Data Ground Segment (PDGS), which prepares different levels of products. The Level 1 data comprises of GNSS DDMs and coherent measurements, and are made available with sufficient metadata for calibration and recovery of surface reflection coefficients at the specular reflection points. Level 2 operational processors are supplied to the PDGS by scientific partners, and these will allow the operational recovery of the climate variables, soil moisture, inundation, freeze/thaw and biomass, plus secondary products over the ocean of ocean wind speed and sea ice extent. Level 1 and Level 2 products will be shared publically with registered users over the web using a similar platform to “MERRByS” that shared the TechDemoSat-1 data.

The project is now underway – the HydroGNSS mission contract was signed between ESA and SSTL on 11th October 2021, and a ride-share launch in 2024 is currently being negotiated. Upon launch, there will be a concerted effort to commission the satellite, payload and PDGS and undertake preliminary validation of all the Level 2 products within 6 months. HydroGNSS will continue to explore the technique of GNSS-Reflectometry while laying the foundations for a future constellation offering high spatial-temporal resolution observations of the Earth’s weather and climate.

HydroGNSS is a mission concept selected by ESA on 2021 as the second Scout small satellite in the frame of the FutureEO Earth Observation programme. The satellite is planned to be launched in 2024 after a 3-year development phase and is being developed by Surrey Technology supported by a scientific team composed by Sapienza University of Rome, IEEC in Barcelona Spain, NOC in Southampton UK, Finland Meteorology Institute, CNR/IFAc in Sesto Fiorentino Italy, Tor Vergata University of Rome.
The mission targets the hydrological cycle and is based on the GCOS requirements for monitoring Essential Climate Variables (ECV). Water is a natural resource vital to climate, weather, and life on Earth. It manifests itself in or on the land in different ways, for example, moisture in the ground, wetlands and rivers, snow and ice, and vegetation density. Global knowledge of land water content and state is important in its different forms for many reasons:
• Soil moisture knowledge is needed for weather forecast, hydrology, agriculture analysis, and wide scale flood prediction
• Freeze / thaw state is an important variable that helps understand permafrost behaviour in high latitudes, a key issue in climate change as a methane source.
• Above Ground Biomass feeds into understanding of carbon stock in forests and a sink in the carbon dioxide cycle, and it also has a coupling to biodiversity
• Wetlands are fragile ecosystems that also can be sources of methane, and often hidden under forest canopies
Increasingly complex and accurate models are used to characterise and forecast hydrological processes. Earth System Models (ESMs) are used for climate, and Numerical Weather Prediction (NWP) models for weather forecasting. A better knowledge of these processes requires hydrological observational data to be assimilated into models to ensure correspondence with the complexity of the real world.
The HydrGNSS mission is based on the GNSS Reflectometry (GNSS-R) technique that collects the signal transmitted by the navigation satellites and scattered by the Earth surface in the near specular direction. GNSS-R has consolidated applications for ocean monitoring, which have been demonstrated by past and current missions (UK TEchDemosat-1, NASA CyGNSS) but land applications have been also proved. Specifically, the target Level 2 products for HydroGNSS are surface soil moisture, inundation/wetland, freeze/thaw state and forest above ground biomass, which are partially coincident with ECV’s (moisture and biomass) or strictly related to them (freeze / thaw and wetland).
This presentation illustrates the products that will be delivered by HydroGNSS, the requirements dictated by GCOS and the performances expected from HydroGNSS based on the state of art of the GNSS-R technique. The development of an End to End simulator is a first step to demonstrate the mission is capable to achieve the Scientific Readiness Level 5 and the achievements in this field by the research group will be presented.

Earth is changing at an unprecedented pace. Awareness of the socio-economic impact of climate change has also been growing and so has the international consensus on the urgency of understanding and limiting the impacts. To do this successfully, we need to understand and quantify the processes driving the change and its future, and particularly the role played by the atmosphere.
The composition of the Upper Troposphere and Stratosphere (UTS) plays a significant role in controlling the Earth’s climate, but there are still poorly explored feedbacks within the Earth System. This region is coupled to the surface and the free troposphere both dynamically and radiatively. Its composition is strongly affected by anthropogenic emissions of greenhouse gases (GHG) and pollution precursors, and is subject to changes via radiative, dynamical, and chemical processes. As the Earth’s atmosphere is changing, so is the UTS. The rapid vertical and horizontal variations in the abundances of trace gases and aerosol around the tropopause, as well as strong and differing trends either side of the tropopause, have made trend detection challenging in this region with a knock-on effect on estimates of their climate impact.
The overall goal of CubeMAP is to study, understand, and quantify processes in the UTS, study its variability, and contribute to analysis of trends in its composition and the resulting effects on climate and vice-versa. The mission focuses on tropical regions, which are most critical for UTS processes, as the strong vertical transport in these latitudes means this is effectively the “gateway to the stratosphere”.
The space sector is also changing at a fast pace, which ought to benefit innovation for science missions. CubeMAP embraces a high level of innovation and studies the UTS processes with a unique combination of high accuracy, high vertical resolution, and enhanced geographical and temporal coverage, owing to a novel sounding approach that leverages small satellite benefits. These measurement characteristics are achieved by measuring high spectral resolution atmospheric transmission spectra at the limb, exploiting the high signal to noise ratio delivered by solar occultation. The limited coverage usually associated with solar occultation limb sounding is obviated through the deployment of a nanosatellite constellation of identical sounders, equally distributed in one orbital plane.
The mission makes uses of GOMSpace 12U cubesat platforms, with electric propulsion and enhanced pointing control. The CubeMAP fleet is made of three spacecrafts. Each spacecraft embarks three thermal infrared laser heterodyne spectrometers for high spectral resolution atmospheric transmittance spectroscopy, miniaturized using hollow-waveguide integration technology. The scientific payload also includes a solar disc imager required for determining the pointing knowledge of each spacecraft, which also provides hyperspectral atmospheric transmittance data over 16 channels in the visible and infrared by using a multi spectral focal plane mosaic array.
In addition to its immediate scientific objectives, CubeMAP contributes to developing a novel resilient approach to atmospheric observation to be scaled up: it offers a high level of deployment flexibility and system modularity, as well as economy of scale, through the use of identical payloads and platforms but targeted towards specific Earth observation goals. CubeMAP is highly complementary to the existing nadir sounding infrastructure and will add value by enhancing its outputs and exploitation.

The chemical composition of the upper troposphere and lower stratosphere – the UTLS – is spatially and temporally highly variable and determined by a range of factors such as transport, chemistry, and tropopause dynamics. The UTLS is also thought of being very sensitive to climate change. The tropopause in particular has been dubbed the canary of the climate system due to its sensitivity to radiative forcing. Because of long radiative timescales, a small forcing leads to a large response, with knock-on effects on transport and composition. However, given a rather sparse sampling (in space and/or time) and limited precision of available observations, accurately quantifying UTLS composition variability and trends and identifying their drivers poses a considerable challenge.

CubeMAP – CubeSats for Monitoring of Atmospheric Processes – focusses on exactly addressing this challenge. To this end, CubeMAP is putting forward a flexible, fast to implement, and highly innovative modular measurement system which will make observations at tropical and sub-tropical latitudes. It will observe chemical trace gases such as water vapour, carbon dioxide, methane, ozone, and nitrous oxide and their isotopologues, as well as aerosols – all of which play a key role in radiative forcing and climate change and which will shed light and help quantify diverse climate processes.

In this contribution, we will review open science questions CubeMAP will help to address, including how natural and anthropogenic emissions (e.g. from wildfires, deep convection, long-range transport) will affect UTS composition, how water vapour in the UTS will change in response to climate change, how these changes will feed back on climate, how climate change will alter transport in the stratosphere and impact the ozone layer and its recovery, and how knowledge of the vertical distribution of greenhouse gases will help improve the quantification of estimates of their surface emissions.

Weather-related hazards are ubiquitous around the world including: 1) hurricane storm surges, 2) rapid snowmelt and heavy rainfall, 3) severe weather leading to flash floods, and 4) seasonal freeze and thaw of rivers that may lead to ice jams. Each of these hazards affects human settlements and has the potential to impact agricultural productivity. In each setting, end-users in disaster management need access to data processing tools helpful in mapping past and current disasters. Analysis of past events supports risk mitigation by understanding what has already occurred and how to alleviate those impacts in the future. Having capabilities to generate the same products in a response setting means that lessons learned from risk analysis will carry forward to event response. Synthetic aperture radar (SAR) data from the ESA’s Sentinel-1 mission are particularly useful for these activities due to their all-weather 24/7 monitoring capabilities, global and regular sampling strategy, and free-and-open availability.

Here we present HydroSAR, a scalable cloud-based SAR data processing service for the rapid generation of hazard information for hydrological disasters such as flooding, storm surge, hail storms, and other severe weather events. The HydroSAR project is led by the University of Alaska Fairbanks in collaboration with the NASA Alaska Satellite Facility (ASF) DAAC and the NASA Marshall and Goddard Space Flight Centers. As part of this project we developed a series of SAR-based value-added products that extract information about surface hydrology (geocoded image time series, change detection maps, flood extent information, flood depth maps) and flood impacts on agriculture (crop extent maps, inundated agriculture information) from Sentinel-1 SAR data.

To enable automatic, low-latency generation and distribution of these products, HydroSAR utilizes a number of innovative software development, cloud computing, and data access technologies. HydroSAR is fully implemented in the Amazon Web Service (AWS) cloud, resulting in a high-performance and scalable production environment capable of continental-scale data production. The service is co-located with ASF’s AWS-based Sentinel-1 data holdings to accelerate data access, reduce data migration, and avoid egress costs. The HydroSAR implementation heavily leverages ASF’s cloud-support services such as OpenSARLab (cloud-based algorithm development) and the ASF Hybrid Pluggable Processing Pipeline (HyP3; cloud acceleration and elastic scaling). HydroSAR is fully open source. All HydroSAR technology is Python based and accessible via GitHub, and HydroSAR products are stored in publicly accessible AWS S3 cloud storage. To ease integration into GIS environments and other web services, HydroSAR products are served out using REST endpoints and ArcGIS Image Services.

In our presentation we will introduce the SAR-based products that were developed for this effort. We will describe our approach for product calibration/validation and provide metrics for product performance. We will outline the cloud-based production pipeline that was built to automatically generate our data products across large spatial scales. We will demonstrate the benefit of ArcGIS image services for data sharing and data integration. Finally, we will showcase the integration of HydroSAR products into disaster response activities for a number of recent disasters including the 2021 U.S. hurricane season, the 2021 Alaska spring breakup flooding, and the 2021 flood season in Eastern India, Bangladesh, and Nepal.

The latest developments in big cloud computing environments, supporting ever increasing volumes of Earth Observation (EO) data, present exciting new possibilities in the field of EO. Nevertheless, the paradigm shift of deploying algorithms close to the data sources also comes with new challenges in terms of data handling and analysis methods. The Euro Data Cube (EDC) was developed in this regard, to facilitate large-scale computing of EO data by providing a platform for flexible processing, from the local to the continental scale. Not only does the EDC offer a workspace with customisable levels of computational resources, it also provides on-demand cloud-optimised fast access to tens of petabytes of satellite archives from open and commercial sources with proven scalability. The platform also offers a growing ecosystem of inter-compatible modules supporting users in all steps of data analysis, processing and visualisation.

To showcase the potential of the EDC, we will present how the platform supported the development of algorithms for a spatio-temporal drought impact monitoring system in Europe. Using new Copernicus Land Monitoring Service (CLMS) High Resolution Vegetation Phenology and Productivity data (HR-VPP) we produced indices on vegetation dynamics as a response to soil moisture deficit. The exercise was run at a 10-meter resolution for the period 2017-2020 over Europe. Owing to the large volume of the vegetation and soil moisture anomaly time series (0.6 PB), the datasets could not be processed using local computing resources. Simply adding more computing resources or even using a High Performance Computing (HPC) facility would not solve the problem either, given the large volumes of regularly-updated data needed to be transferred between clouds. The use of the EDC deployed on CreoDIAS, which also host the HR-VPP data as part of the WEkEO infrastructure, made it possible to produce efficient, transparent, repeatable and ad-hoc updates of drought impact indicators, which are an essential input to the European Environment Agency supporting European Policy making.

The use-case presented is just one example of how the EDC can be used to monitor continental phenomena at high spatial and temporal resolutions. Stemming from this example, we will present the EDC technology stack and services at the disposal of users to develop and deploy large-scale algorithms that in turn, can be triggered as on-demand algorithm executions in a Software-as-a-Service style. We will also discuss the data scientist’s workflow and adjustments that were needed in this regard, to transfer a typical remote sensing exercise into a scalable process.

Many global and continental scale mapping projects today have to solve the same problems: finding an infrastructure with the required datasets, setting up a scalable processing system to produce results, configuring preprocessing workflows to generate Analysis Ready Data (ARD), and then scaling from test runs to the final continental or global map. After that appropriate metadata needs to be generated followed by a dissemination phase where products have to be made ready for viewing. OpenEO platform solves most of these problems, but how does that work out in a real-world scenario?

To demonstrate the efficiency and useability of OpenEO platform (https://openeo.cloud) we have generated a European crop type map at 10m resolution. The map is based on both Sentinel-1 and Sentinel-2 data, which makes it a suitable blueprint for many other mapping projects. The feature engineering capabilities of the platform transform this data into a set of training features, which are then sampled across Europe to build a training and validation dataset. The built-in classification processes are finally used to train a model, which is used subsequently to generate the final map, which is delivered as a set of cloud optimized geotiffs with SpatioTemporal Asset Catalog (STAC) metadata. The final product will be validated using a harmonized LPIS dataset based on the availability at country level.

In this presentation we will give an overview of the full product R&D and production lifecycle. We report on efficiency gains and potential remaining bottlenecks for users. This gives a good overview of platform capabilities, but also of the overall maturity with respect to being production-ready for continental scale mapping efforts. For the large scale production of the map, processing capacity available at VITO, CreoDIAS and EODC will be used containing large and local, data archives extended with data on Sentinel Hub accessed via Euro Data Cube. This federation of data and processing capacity is made transparent by openEO platform. A built-in large scale data production component is responsible for distributing the workload across the available infrastructure. This aims to show that OpenEO platform has reached a maturity level that allows users to engineer an entire ML pipeline from data to final classification on a large scale.

The WorldCereal project aims at developing an efficient, agile and robust Earth Observation based system for timely global crop monitoring at field scale. The resulting platform is a self-contained, cloud agnostic, open-source system.

The global agricultural monitoring requires a solution to an increasingly complex computational problem. Therefore, high performance computing became a critical resource and that is why the need of a processing cluster arose. The project delivers an open-source EWOC system which automatically ingests and processes Sentinel-1, Sentinel-2 and Landsat 8 time series in a seamless way to develop an agro-ecological zoning system and disseminate high added value products to the users through the visualization portal using the geoTIFF COG format.

The system can be installed over a cluster which is driven by Kubernetes. The cluster itself may be hosted by a provider of choice. (CreoDIAS, AWS, etc... )

A set of open-source tools have been used for creating the process framework. Python has been mostly used for connecting the different processors and making them run. To achieve the results, the cluster has first to be scaled up to several desired nodes which are interconnected computers working as a single system. The operating system on these nodes is Linux. Furthermore, whenever a processing is needed (due to major growing season ending or by a user’s wish), a workflow started by Argo tool is scheduling the pods over the previously spawned nodes. These pods are in fact a collection of self-contained (i.e., shipped as Docker containers) applications or systems that produce high value added EO products such as crop masks, crop type maps and irrigation maps. The pre-processing chains for Sentinel 1, Sentinel 2 and Landsat 8 are first run in a parallel processing to provide enough dataset for the classification processor which in turn is providing the WorldCereal products.

At the center of the system is a Postgresql database which is used to organize the pending tasks and the upcoming productions. This precise tracking allows resilience in case a process failure, a re-processing is then automatically scheduled by Argo.

The information regarding the processing of the results as well as the S3 bucket paths to these results are stored in the database which grows in time. The progress of a running workflow may be monitored over a map which contains an overview of the tiles which cover the area for which the processing is run.

The goal of the cluster itself is to simplify the parallel pre-processing and the classification processing with the use of multiple data-sources as input. In case of a node failure (because of different causes like spot instance node termination in case of AWS provider, or a hardware failure in case of CreoDIAS provider) the system may recover whenever a new node is available.

Current system demonstration instance, hosted on a DIAS, benefits from the extensive amount of already available Earth Observation Data. This synergy offered by the cloud provider and the associated available data offers the perfect operational context for our cloud-based system. For additional data resources, the EWoC system seamlessly interfaces with external providers using its own dedicated data retrieval abstraction layer. This last component, overcoming some limitation of DIAS data offer could benefit, as an open-source piece of software, to other projects.

The EWoC system is a demonstration of an operational, open-source, cloud-based processing platform from which other future projects can benefit through its many reusable components.

Big data always presents new challenges, which can lead to innovation and potential improvements. At EOX we were recently at a breaking point and have reworked our EOxCloudless (https://cloudless.eox.at) data processing infrastructure almost from scratch. For those unfamiliar with the label this data is the one to generate our satellite map layer known as “Sentinel-2 cloudless” (https://s2maps.eu).

# Splitting the responsibilities - Kubernetes

Autoscaling is a tough problem and we know that managing large compute clusters is a responsibility we should delegate to our engineering team. Using Kubernetes
(https://kubernetes.io/de/docs/concepts/overview/what-is-kubernetes/) as the core abstraction to interface between our teams was an undisputed decision based on our experience in other operational deployments of EOxHub like “Euro Data Cube” (https://eurodatacube.com/), “Earth Observing Dashboard” (https://eodashboard.org/), and “Rapid Action on coronavirus and EO” (https://race.esa.int/). Our EOxHub technology, able to mediate on top of Kubernetes with platform resources on different clouds, enables us to both handle the workloads and to automatically scale the infrastructure based on processing needs.

# Splitting the workload - dask

With only a handful of options for handling concurrent/parallel workflows in python, we have opted for the meanwhile well established “dask” (https://dask.org/). For local processing we have adapted the “dask - Futures”, which is an extension for natice python‘s “concurrent futures'' (https://docs.python.org/3/library/concurrent.futures.html), to our use case. This with the help of “dask.distributed” (https://distributed.dask.org/en/latest/) runs concurrently a huge number of tasks locally and on a cluster as well.

# Geographical splitting/tiling of the workload - tilematrix

What is a “task” in this scenario? In “dask futures” itself it can be any arbitrary python function which runs itself and returns a result, the future is collected after completion of any single task.

For splitting geospatial data we have written our custom tiling package for regular matrices called “TileMatrix” (https://github.com/ungarj/tilematrix). Apart from the default EPSG:4326 and EPSG:3857 grids, it allows defining custom grids in any usual coordinate reference system. These grids provide a tile structure following a classical tiling scheme {zoom}/{row}/{column} ({z}/{y}{x}.{extension}). This is similar to the “OGC Two Dimensional Tile Matrix Set” (https://docs.opengeospatial.org/is/17-083r2/17-083r2.html).

# Mapchete meets dask

How to run a mosaicking script handling thousands of Sentinel-2 scenes distributed over the globe?

By doing exactly this even before this update for several years with different tools, for us this only took adding the dask know-how into the mix. Yet another python package originating from EOX called “Mapchete” (https://github.com/ungarj/mapchete) was extended by the “dask.distributed” package opening it to the dask environment, which gave it a direct way to be connected to a couple of cluster connection options offered by the dask community.

# Sentinel-2 data access optimization

Apart from extended concurrency options, getting a hand on a huge amount of Sentinel-2 data can be somewhat costly, difficult, and time-consuming. To shed some light on this, let’s look over some of the options that people interested in doing similar processing might consider doing.

Sentinel-2 data access:
1. https://scihub.copernicus.eu/
2. https://www.copernicus.eu/en/access-data/dias
Sentinel-2 data at Amazon s3 (AWS s3):
1. https://registry.opendata.aws/sentinel-2/
2. https://registry.opendata.aws/sentinel-2-l2a-cogs/

Let’s start with some of the basics, apart from the regular access to Sentinel-2 data, AWS offers under its “opendata” policy the datasets we need to produce our annual global mosaic, therefore we opt for AWS as the cloud and data provider. From AWS (1.) we can get all the metadata we need to merge the granule products into datastripes (acquisition stripes) and the angles and geometries for the angular correction (BRDF) to compensate for the Sentinel-2 L2A sen2cor imbalanced values in the west-east direction. The disadvantage of the AWS number one (1.) is that most of the data requests are currently under “requester-pays” settings, which for a couple of billion requests induces a not negligible cost.
Thankfully the AWS number two (2.) has currently no such restriction and it has one more advantage. The Sentinel-2 data there are stored as Cloud Optimized Geotiffs (COGs), which improves GDAL reading speed significantly as well as being free to make requests from the AWS s3 storage. Both of the AWS s3 endpoints provide “Spatio Temporal Asset Catalog” (STAC) accessibility, which makes search and browse extremely convenient once the STAC structure and tools have been understood (https://stacspec.org/).

The data is being read and BRDF corrected by custom mapchete extensions, which take care of all the metadata transformation, reading, reprojection, and corrections. After that they are submitted to the mosaiking process, which gets the masks and finally evaluates the timeseries based on provided settings to deliver a homogenous, cloudless mosaic output.

# Scale up with Dask-Kubernetes

Finally it comes down to scaling up the processing from the local environment to something much bigger. Each task needs to be processed not on one server or Virtual Machine (VM), but on a random dask-worker within the kubernetes cluster.
The advantage of this is that dask cluster supports adaptive dask-worker scaling, which directly controls the kubernetes resources scaling as well, this requests and launches cloud resources only when needed and shutting them down when idle.
The “dask-kubernetes”(https://kubernetes.dask.org/en/latest/) is paired with a so called “dask-gateway” (https://gateway.dask.org/) to handle hardware specifications and optionally to have different tiers of users to not allow everyone to use all the resources.
For the overall management and config of the kubernetes a Flux (https://fluxcd.io/) configuration has been set up to handle the top control of the kubernetes environment.

# Use the output data

Lastly, let's see how we can use the generated data. The output data organized as GeoTIFF Tile Directories can be read by the GDAL STACTA (Spatio Temporal Asset Catalog - Tiles Assets) driver (https://gdal.org/drivers/raster/stacta.html), which allows users to get, convert, or subset the data with GDAL directly from object storage.

Furthermore with some of the recent updates to OpenLayers (https://openlayers.org/) GeoTIFFs organized as STACTA can be loaded in the browser directly as shown in the exampleat https://openlayers.org/en/latest/examples/cog-pyramid.html. This provides the option to support all sorts of interactive rendering and analysis of the original 16bit data values directly in the browser.

# Conclusion

By the time of the Living Planet Symposium (LPS 2022) in May 2022 the next update of the EOxCloudless satellite map for the year 2021 will be available at https://s2maps.eu/. With that at hand we will have the results of an actual global benchmark for this updated big data processing workflow to present.

The presentation of this work will include the presentation of the workflow as described here and a live demonstration, partly as a tutorial.

We present here the recent progress of the DACCS (Data Analytics for Canadian Climate Services) project [1], funded by the Canadian Foundation for Innovation (FCI) and various provincial partners in Canada, in response to a national cyberinfrastructure challenge. The project’s development phase is set to finish in 2023, while funding for maintenance is planned over ten years. DACCS leverages previous development efforts of PAVICS [7], a virtual laboratory facilitating the analysis of climate data, without the need to download it. A key aspect of DACCS is to develop platforms and applications that combine both climate science and Earth observation, in order to stimulate the creation of architectures and cyber infrastructures shared by both.

In climate sciences, it is now common to manipulate very large and highly dimensional volumes of data derived from climate models such as the CIMIP5 and soon CIMIP6 datasets. In Earth Observation (EO), the concepts of time series and ARD data also lead us to manipulate multidimensional data cubes. Consequently, the two domains share a common set of requirements, especially in terms of visualization services and deployment of applications close to the data. The overall philosophy of the platform is to provide a robust backend with interoperable services with the ability to deploy processing applications close to the data (A2D).

The proposed platform architecture adopts some of the characteristics of the EO Exploitation Platform Common Architecture (EOEPCA) [12], including user access, data discovery, user workspaces, process execution, data ingestion and interactive development. This architecture describes A2D use cases where a user can select, deploy and run applications on distant infrastructures, usually where the data resides. An Execution Management Service (EMS) transmits such a request to an Application Deployment and Execution Service (ADES) [8].

Python notebooks are offered as the main user interaction tool. This way, advanced users can keep the agility of Python objects, all while offering the option to call demanding A2D tasks via widgets. Beyond the technical contribution, the platform offers an opportunity to explore synergies between the two scientific domains, for example to create hybrid machine learning models [6].

The platform ingests Sentinel-1 & 2 and Radarsat Constellation Mission (RCM) images in order to create multi-temporal and multi-sensor datacubes. The EO ingestion is performed by Sentinel Application Platform (SNAP) [13] workflows packaged as application inside Docker containers [11]. The goal is to provide researchers the ability to create data cubes on the fly via python scripts, with an emphasis on replicability and traceability. Resulting data cubes will be stored in NetCDF format along with the appropriate metadata to be able to document data provenance and guarantee replicability of the processing. Workflows and derived products will be indexed in a STAC catalog. The platform is aiming at facilitating the deployment of typical processing tasks including standard ingest processing (w.g. calibration, mosaicking, etc.), but also advanced Machine Learning tasks.

Aside from EO use cases, DACCS project should advance PAVICS climate analytics capabilities. The platform readily allows access to several data collections ranging from observations, climate projections and reanalyses. PAVICS relies on the Birdhouse Framework [9], a key component of the Copernicus Climate Data Store [10]. PAVICS is the backbone of the analytical capabilities of ClimateData.ca, a portal created to support and enable Canada’s climate change adaptation planning, and improve access to relevant climate data.

Climatedata.ca provides access to a number of climate variables and indices, either computed from CMIP5 and CMIP6 projections downscaled to Canada [3], or from historical gridded datasets [4]. Variables can be visualized on maps or on graphs, either on the basis of their grid cell element or by spatial aggregates, such as administrative regions, watersheds or health regions. Climatedata.ca also provides up-to-date meteorological station data, standardized precipitation evapotranspiration index (SPEI) [5], as well as sea-level rise projections.

The portal’s Analyze page [14] allows users to select their grid cells or regions, and to set the thresholds, climate models, RCPs and percentiles they would like to use for analysis. User queries are then sent to the PAVICS node through OGC API - Processes. Computations are realized by xclim[2], a library of functions to compute climate indices from observations or model simulations. The library is built using xarray and benefits from the parallelization handling provided by dask.

References

[1] Landry, T. (2018). "Bridging Climate and Earth Observation in AI-Enabled Scientific Workflows on Next Generation Federated Cyberinfrastructures", AI4EO session, the ESA Earth Observation Φ-week, November 2018, ESA-ESRIN, Frascati, Italy.
[2] Logan, T. et al. (2021): xclim - a library to compute climate indices from observations or model simulations. Online. https://xclim.readthedocs.io/en/stable/
[3] Cannon, A.J., S.R. Sobie, and T.Q. Murdock (2015): “Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?”, Journal of Climate, 28(17), 6938-6959, doi:10.1175/JCLI-D-14-00754.1.
[4] McKenney, Daniel W., et al. (2011): "Customized spatial climate models for North America." Bulletin of the American Meteorological Society, 92.12: 1611-1622.
[5] Tam BY, Szeto K, Bonsal B, Flato G, Cannon AJ, Rong R (2018): “CMIP5 drought projections in Canada based on the Standardised Precipitation Evapotranspiration Index”, Canadian Water Resources Journal 44: 90-107.
[6] Requena-Mesa, Christian, et al.(2020) "EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts." arXiv preprint arXiv:2012.06246, https://www.climatechange.ai/papers/neurips2020/48
[7] The PAVICS platform. Online. https://pavics.ouranos.ca/
[8] Landry et al. (2020): “OGC Earth Observation Applications Pilot: CRIM Engineering Report”, OGC 20-045, http://www.opengis.net/doc/PER/EOAppsPilot-CRIM
[9] C. Ehbrecht, T. Landry, N. Hempelmann, D. Huard, and S. Kindermann (2018): “Projects Based in the Web Processing Service Framework Birdhouse”, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W8, 43–47
[10] Kershaw, Philip, et al. (2019): "Delivering resilient access to global climate projections data for the Copernicus Climate Data Store using a distributed data infrastructure and hybrid cloud model." Geophysical Research Abstracts. Vol. 21.
[11] Gonçalves et al. (2021): “OGC Best Practice for Earth Observation Application Package”, OGC OGC 20-089, Candidate Technical Committee Vote Draft, Unpublished
[12] Beavis P., Conway, R. (2020), A Common Architecture Supporting Interoperability in EO Exploitation, ESA EO Phi-Week 2020, Oct. 1st 2020. https://eoepca.org/articles/esa-phi-week-2020/
[13] Zuhlke et al. (2015), “SNAP (Sentinel Application Platform) and the ESA Sentinel 3 Toolbox”, Sentinel-3 for Science Workshop, Proceedings of a workshop held 2-5 June, 2015 in Venice, Italy.
[14] ClimateData.ca Analyze page: https://climatedata.ca/analyze/

Harmony was one of the three missions that was selected as ESA Earth Explorer 10 mission candidates. After a down-selection process at the end of the Phase-0 studies, Harmony, proceeded as the only remaining candidate to a Phase-A, which will close in the summer 2022. This presentation gives an overview of the mission, reflecting its current status.

The Earth is a highly dynamic system where transport and exchanges of energy and matter are regulated by a multitude of processes. The non-linear nature of the governing physics results in couplings between processes happening at a wide range of spatial and temporal scales, with cascades of energy flowing from the larger to the smaller scales and vice-versa.

The Earth System cannot be understood or modeled without adequately accounting for small-scale processes. Indeed, the parameterisation of the unresolved, sub-grid physical processes in global or regional models remains one of the main sources of uncertainty in climate projections, in particular with respect to air-sea coupling, cryosphere and clouds

Harmony is dedicated to the observation and quantification of small-scale motion and deformation fields, primarily, at the air-sea interface (winds, waves, and surface currents), of the solid Earth (tectonic strain), and in the cryosphere (glacier flows and surface height changes).

The retrieval of kilometre and sub-kilometre scale motion vectors requires concurrent observations of its components, which will be achieved by flying two relatively light-weight satellites as companions to a Sentinel-1D, with a receive-only radar as main payload. The resulting line-of-sight diversity will be exploited in combination with repeat-pass SAR interferometry to estimate tiny deformation rates in the solid Earth, and for land ice processes. It will also be used in combination with Doppler estimation techniques for the retrieval of instantaneous ocean and sea ice surface velocities. Over oceans, geometry-diverse measurements of the radar backscatter will further allow the retrieval of surface (wind) stress and wave-spectra. The Harmony spacecraft will also carry a multi-beam thermal-infrared payload, which in the presence of clouds will allow the retrieval of height-resolved motion vectors. The combination of surface currents, surface wind-stress, along with TIR derived cloud-top height and cloud-top motion vectors will provide an unprecedented view of the MABL. In absence of clouds, the TIR payload will provide simultaneous observations of the sea surface thermal differences,
providing a unique window to look at upper-ocean processes and air-sea interactions on the small ocean scales.

The formation flying architecture of Harmony enables the unique capability to reconfigure its flight formation so that instead of being optimised for the measurement of motion vectors, it is optimised for the measurement of time-series of surface topography. This will, among other outcomes, result in a globally consistent and highly resolved view of multi-annual glacier volume changes between well defined epochs, needed to better quantify the climatic response of glaciers. At the same time, Harmony will allow studying the seasonal and sub-seasonal processes from space that play a role in such responses, for instance by measuring variations in lateral ice flow and associated elevation changes simultaneously over large areas for the first time.

In order to study upper ocean processes and air-sea interactions, Harmony will provide kilometer-scale surface roughness, root mean squared slopes, and surface kinematics, in different viewing perspectives, reflecting the imprint of Marine Atmospheric Boundary Layer (MABL) eddies on the ocean surface. This provides information about both the surface wind vector, as well as surface current vectors and swell, and, importantly, the thermal disequilibrium between air and ocean.

This will lead to a more precise understanding of small-scale (submesoscale) impacts on air–sea fluxes, especially CO$_2$ fluxes, momentum, ocean heat uptake and overall energy pathways, to reduce uncertainties for lateral dispersion of pollutants and tracers, vertical transport and nutrient pumping.

The scientific goal of Harmony for the cryosphere is to bridge existing observational gaps in order to improve our understanding of the physical processes causing the widespread shrinkage of the cryosphere. These conceptually new observations will push back the existing limits by refining the reconstructions of past and ongoing glacier changes, by improving the representation of the driving mechanisms in regional and global models of ice flow dynamics and mass balance, by describing the unresolved complex processes allowing calibration and validation of sea ice models with more realistic rheology or by improving our understanding of the permafrost dynamics.

Harmony aims at providing, for the first time, worldwide integrated measurements of elevation changes and ice flow on glaciers and ice-sheet coastal areas, as well as localised elevation measurements of icebergs and ice shelves.

A particular strength of the interferometric capabilities of Harmony that the mission is able to measure large topographic changes and lateral displacements (scale of metres and tens of metres) through repeat XTI-mode elevation models and SAR offset tracking, and, at the same time, small changes (cm-scale) thanks to the diversity of SAR lines-of-sight.

Harmony aims to provide an integrated view of the dynamic processes that shape the Earth’s surface. For the Solid Earth, the scientific goals are to improve our understanding of tectonic and magmatic processes by bridging existing observational gaps with regard to strain rates, which are currently hindered by the lack of sensitivity to the North-South deformation component, and with regard to rapid elevation changes.

Coupled atmosphere and ocean boundary layer dynamics represents a crucial component of the Earth system. Air-sea interactions span several different time scales, from shorter scale weather pattern evolution and extreme meteorological events, to inter-annual/decadal climate variability and global change. Air-sea fluxes depend on many correlated properties. For instance, the exchange of momentum is caused by the shear resulting from the atmospheric wind and many other physical quantities, e.g. the ocean velocity, the sea state and the density stratification in the lower atmosphere and in the upper ocean. Similarly, heat fluxes depend on surface stress, roughness, temperature, and vertical transport in the turbulent atmospheric and oceanic boundary layers. Those physical characteristics and processes are often correlated, through the action of complex interactions. An increased coupling between surface winds and the stronger winds aloft emerges when warm sea surface temperature destabilizes the air column, influencing entrainment at the top of the boundary layer, and surface ocean currents affect roughness as demonstrated by the covariance of mesoscale features and surface wave-energy. Furthermore, secondary flows in the ocean and atmosphere boundary layers impact vertical transport and mixing, even producing non-gradient fluxes.

Despite the importance of the small scale processes in setting the conditions at the interface between air and water, the limited capabilities of present satellite and in situ instruments do not allow to properly characterize them. Indeed, most of our understanding of the dynamics at these scales comes from high resolution numerical modeling and theoretical studies, and only sparse observational analyses have effectively been carried out. Ocean fronts and eddies identified with aerial guidance, further seeded with drifters, occasionally provided some reference ranges and target requirements for future scientific missions. Reported analyses indicate divergence and vorticity values in the upper ocean that can largely, at time and very locally, exceed 5 and even 30 times the Coriolis frequency, which represent very extreme departures from balanced dynamics, and suggest intense vertical motion induced by sub-mesoscale features. Strong sub-kilometer scale variations of winds, associated with dynamical features such as atmospheric boundary layer rolls and convective plumes, have been reported, indicating that transport in the MABL depends on processes that are not represented in climate models.

To properly characterize the ocean and atmosphere boundary layer dynamics, it is thus crucial to identify - through the imprint they leave - small scale processes at the air-sea interface. Harmony will allow the retrieval of relative surface velocities, winds, and waves at kilometer to sub-kilometer resolution in target areas of high dynamical interest, providing information on divergence and vorticity both in the ocean and in the atmosphere. Simultaneous measurements of sea surface temperature under clear sky conditions and of cloud top heights & motion otherwise will either allow to link dynamical patterns to thermodynamic features or to estimate the thickness of the atmospheric layer directly affected by air-sea processes.

The identification of the presence of large variations in winds at sub-kilometer scale during intense events, such as in tropical cyclones, will also allow to identify the presence of local anomalies in air-sea fluxes and to link them to convective updrafts through the effect they have on relative vorticity near the surface, providing crucial information for the prediction of rapid intensification events in tropical cyclone development.

Even if the relatively long revisit clearly precludes the possibility to follow individual feature evolution outside the high latitude bands, these composite data will still represent fundamental new information that could be used to validate existing and future high-resolution numerical models and also to eventually downscale the observations from lower resolution products as those obtainable through satellite altimetry and scatterometers. This can be considered a realistic target considering the growing efforts towards the application and development of artificial intelligence tools combining techniques originally thought for computer vision tasks as super-resolution.

Spatially-detailed maps of three-dimensional surface displacements and topography change, and their temporal evolution, are essential for understanding and modelling geophysical processes that trigger earthquakes, landslides and volcanic events, and for the assessment of hazards arising from these phenomena. Current SAR missions are sensitive to vertical and east-west motions, but are extremely limited in their sensitivity to north-south motion. In its bistatic configuration, the Harmony mission will deliver 3D vectors of surface motion by means of differential repeat-pass InSAR methods. In areas where displacement is predominantly north-south, the ability to systematically measure the third dimension of displacement will reveal motions that have been invisible up until now, and in other areas will enable the resolution of ambiguities in the underlying physical processes that lead to earthquakes, landslides and volcanism. Resolving strain rates in tectonic regions down to 10 nanostrain/year will be a key target for the mission, which will encompass the majority of continental regions that lead to deadly earthquakes. In its cross-track configuration, as well as still providing 3D deformation maps, the Harmony mission will deliver time series of topographic change, providing high-resolution views of the active processes that reshape the Earth's surface. A key focus will be constraining changes that occur during volcanic eruptions, such as lava flows, dome growth and pyroclastic density flow deposits.

The unique bistatic configuration of the Harmony mission will allow the retrieval of 3D deformation maps with unprecedented accuracy. Current performance and end-to-end simulation results indicate that a deformation measurement performance close to 1 mm/yr @100 km can be achieved for all three dimensions. Similarly, the single-pass digital elevation models acquired in the cross-track configuration will have a point-to-point vertical accuracy of about 1-2 m at a resolution of 30 m x 30 m with the interferometric baselines being currently considered.

Besides presenting the main goals and motivations of the Harmony mission for solid Earth, this contribution will also show dedicated simulation results obtained with the performance simulator currently being developed in the frame of the science studies of the Harmony mission.

Land ice applications are one of the primary scientific goals of the ESA EarthExplorer 10 candidate mission Harmony. Specifically, the mission aims to
(i) provide a consistent, temporally and spatially highly resolved global glacier mass balance, filling major spatial gaps in the current observation of mountain glaciers and outlet glaciers of the ice sheets;
(ii) give new insights into the physical processes associated with the coupling between glacier mass change and ice dynamics and through that substantially improve understanding and prediction of rapid or even abrupt glacier changes, and the balance between vertical ice flow and mass accumulation/ablation;
(iii) provide crucial large-area information on the spatial distribution, extent and magnitude of subsidence and erosion in permafrost areas in order to estimate permafrost degradation and its local and global impact.
To fulfil these goals, the Harmony mission design consists of two passive (receive-only) SAR satellites that fly in constellation with Sentinel-1D. The two Harmony spacecraft will perform measurements of the signals transmitted by Sentinel-1D in either single pass cross-track interferometry (XTI) or stereo formation. From two time series (each one year long, data points every 12 days) of single pass XTI acquisitions, the first series at the beginning (year 1) and the second at the end of the mission (year 4 or 5), world-wide glacier elevation changes will be computed, responding to goal (i). The same approach will be used to detect major erosional processes in ice-rich low-land permafrost, such as thaw slumps, over a period of 4-5 years (goal iii). At the higher temporal resolution of 12-days for the individual series, the interferometric XTI digital elevation models (DEMs) will serve to extract seasonal and sub-seasonal glacier elevation changes. Remarkably, these fast repeat DEMs will be simultaneous with lateral ice displacements derived from repeat Sentinel-1D data using offset tracking, enabling a major step forward for linking variations in ice flow with vertical surface elevation changes (goal ii) and in addition improving the precision of the displacement measurements through the concurrent elevation maps and multi-view redundancy. When interferometric phase coherence persists across the 12-day repeat interval of subsequent acquisitions with XTI but in particular the Harmony stereo formation, this will lead to interferometric repeat pass measurement of three-dimensional Earth surface motion and deformation. Being able to separate out the vertical ice flow component on glaciers will allow comparison with accumulation or ablation to detect mass imbalance (goal ii). Over permafrost areas, Harmony’s interferometric 3D surface motion will improve measurements of seasonal and multi-year frost heave and thaw subsidence, and help detect additional lateral components of these two processes, which so far are assumed to act mostly vertically.

The upper ocean is of intense interest. Almost all human interactions with the ocean occur in its first hundred meters in depth. Most ocean life is concentrated in the upper ocean, and is currently largely altered by modified exchanges between the atmosphere and the ocean. Air-sea interaction processes in the upper ocean are also essential factors in determining future weather and climate.
This makes the upper ocean an important arena for science which transcends the boundaries of physics, chemistry, biology, meteorology and climatology. Digital twins of the Earth systems (DTEs) shall then be key tools to provide integrated capabilities to improve local and global predictions. DTEs will then use any sort of models, data-driven and/or model-driven ones, that provide sufficiently accurate representations that are being twinned/replicated.

Ideally, where accuracy of numerical simulations would be perfect, DTEs would use model-driven simulations derived directly from physics principles. The barrier of computational costs to reach this high accuracy shall certainly not preclude these physics-based approaches. But air-sea interactions are characterized by a too large number of scales interacting over a wide range of time and spatial scales. No computer can encompass the interacting dynamics of all hydrodynamic scales involved in ocean-atmosphere interactions – ranging from the scale of the Sun’s heating (∼10,000 km) down to the turbulence dissipation scale (∼1 mm). Computers can only simulate some of the scales. The others, at the unresolved scales of motion and exchanges, must be parameterized for each type of phenomenon (wave, eddy, current, rain/slicks, ...), in terms of its effects on the resolved scales. These neglected sub-grid processes must then be properly 'calibrated' and further taken into account in order to achieve accurate energy transfers at the ocean-atmosphere interface, as well as to ensue stable numerical simulations. Some very high resolution physics-based model can likely reach the necessary high accuracy, and will certainly be used to generate sets of reliable results to refine surrogate models or metamodels to enter DTEs. Yet, we must admit that computational simulations may also reach some limiting “irreducible imprecision” compared to measured quantities at the ocean-atmosphere interface for different turbulent regimes at the ocean-atmosphere interface, e.g. very extreme events. This limiting feature explains the observed irreproducibility among different model schemes which are supposed to be solving the same problem.

In that context, especially marine-atmosphere extreme events benefit from ultra-high resolution satellite observations and have long been known to be essential to determine whether the results seen in high-resolution ocean-atmosphere coupled models are realistic. In this endeavor, the Harmony mission seeks to provide more direct observations to help quantify and calibrate fine-scale air-sea interaction processes. The Harmony mission will thus pave this new regime of high resolution observations of upper ocean and lower atmosphere dynamics to inform and drive the developments of the next generation of simulation schemes to enter DTEs. In particular, Harmony directional and combined-observations down to very high spatial resolution will deliver evidences to improve the representions of the rapidly fluctuating ocean-atmosphere components. The Harmony mission will thus further produce a new systematic capability for dealing with the changing regimes of uncertainty at the ocean-atmosphere interface.

DataCAP: Sentinel datacubes, crowdsourced street-level images and annotated benchmark datasets for the monitoring of the CAP

Recently, the Common Agricultural Policy (CAP) has undergone radical changes with respect to both the direct payments pillar (Pillar I) and the rural development pillar (Pillar II), particularly in the way they are monitored. This fast-paced transitioning will continue over the next years, shifting towards the CAP 2020+ reform, where the operating model will get progressively simplified and improved. In order to do that, big space-borne Earth Observations (EO), advanced ICT technologies and Artificial Intelligence (AI) have been and will continue to be key enablers.

The Sentinel satellite missions provide frequent optical and Synthetic Aperture Radar (SAR) images of high spatial resolution and have been extensively used for the monitoring of the CAP. Usually, in a Sentinel based CAP monitoring system, the parcel geometries from the Land Parcel Identification System (LPIS), which has connected the declared crop type label to each parcel, are integrated with the Satellite Image Time-series (SITS). Then the Sentinel SITS, the LPIS objects and labels are used to feed AI models for downstream CAP monitoring services, such as crop classification and grassland mowing detection. EO based CAP monitoring systems need to be able to process and visualize very large amounts of satellite data and, for this reason, big Earth data management technologies, such as datacubes, are immensely useful. In more detail, datacubes are arrays of four dimensions, namely the longitude, latitude, time and EO product dimensions, which enable the efficient management and the simple processing and exploitation of data.

In order to harness the massive satellite data utilizing AI and thus enable timely and effective agriculture monitoring, we are still missing a crucial component, which is the ground truth. While satellite data is widely available, associated labels and in-situ data are not, and they are now the most expensive data component to obtain. This is true not just in terms of monetary cost, but also in terms of time, manpower and the availability of the expert knowledge for annotation. As a result, in-situ data and labels are frequently the missing ingredient for i) converting raw data into training datasets for Machine Learning (ML) models, ii) evaluating their performance and iii) make manual decisions when ML is not enough. Thus far, the CAP’s Paying Agencies (PAs) are mandated to check a small percentage of the total number of farmers' declarations, either through field visits or visual inspection of very high resolution images. These procedures are non-exhaustive, time-consuming, sophisticated, and reliant on the inspector's abilities. In this regard, it is the aim of the new CAP to shift from the costly sampled-based inspections towards the wall-to-wall monitoring of the CAP, predominantly using space-borne EO. Nevertheless, EO data and EO-driven information need to be accompanied by timely in-situ observations. Typical in-situ data collection methods are expensive, time-consuming and therefore cannot provide continuous data streams. However, crowdsourced street-level images or images at the edge constitute an excellent alternative source.

In this work, we demonstrate DataCAP, a multi-level data solution for the monitoring of the CAP. DataCAP comprises Sentinel datacubes, street-level images of crops, crop type labels and annotated benchmark datasets for ML. DataCAP addresses both the AI4EO community and the CAP stakeholders, and particularly PAs. It offers easy and efficient searching, storing, pre-processing and analyzing of big EO data, but also visualization tools that combine satellite and street-level imagery for validating the AI models. Using the datacubes, one can generate Sentinel analysis ready data in any form, i.e., pixel, patch, parcel datasets, to then feed their AI models for CAP monitoring (e.g., crop classification, grassland mowing detection). Using the DataCAP’s visualization component, displaying parcel-focused Sentinel time-series and any available street-level images, data scientists and PA inspectors can verify the decisions of the crop classification and/or grassland mowing algorithms through visual inspection. Additionally, street-level images, Sentinel-1 and Sentinel-2 patches are annotated using the LPIS declarations and are offered in the form of benchmark datasets for crop classification. Using both benchmark datasets, we have developed and applied a number of Deep Learning models to classify crop types.

Currently, DataCAP is being populated with Sentinel data and street-level images coming from the Netherlands and Cyprus. The street-level images are harvested through the Mapillary API, and for the pilot in Cyprus we perform our own collection campaigns that we in turn contribute to the Mapillary database. Inspectors from the Cypriot PA have smart-phones mounted on their service cars and capture thousands of street-level images as they do their daily inspections. This way, we secure a continuous data stream of meaningful photos without any additional cost, simply by taking advantage of the existing operations of the PA.

Acknowledgement: This work has been supported by the ENVISION (No. 869366) and the CALLISTO (No. 101004152) projects, which have been funded by EU Horizon 2020 programs.

Fires are among the most significant disturbances decreasing forest biomass (Frolking et al. 2009, Pugh et al. 2019). They may cause severe ecosystem degradation and result in loss of human life, economic devastation, social disruption, and environmental deterioration (Stephenson et al. 2012). Occurrence of forest fires is also expected to increase due to the changing climate (Szpakowski & Jensen 2019, Venäläinen et al. 2020), which can, in turn, accelerate global warming through increased release of CO2 and decreased vegetation binding CO2 (Flannigan et al. 2000, Oswald 2007).

A variety of remote sensing technologies have been used for mapping and monitoring spatial, temporal, and radiometric dimensions of forest fires (Banskota et al. 2014). Optical satellite observations (e.g. Landsat, MODIS) have widely been applied for estimating fire impacts (i.e., burned area) (Lentile et al. 2006, Chuvieco et al. 2019, 2020). However, there is a need for approaches quantifying burned biomass in a comprehensive manner (Bolton et al. 2017).

Laser scanning offers an additional dimension for optical satellite missions as it generates 3D information on trees and forests. Terrestrial laser scanning (TLS) especially provides details of tree stems (Liang et al. 2014), crowns (Seidel et al. 2011, Metz. et al. 2013), branches (Pyörälä et al. 2018), as well as biomass (Calders et al. 2015). As multitemporal TLS datasets are becoming more available, it becomes possible to monitor changes in trees (Luoma et al. 2019, 2021) and forests (Yrttimaa et al. 2020).

The aim of the study is to quantify burnt forest biomass of a controlled burning carried out in boreal forests. In other words, we will develop a methodology to quantify spectral response of burned biomass from optical satellite imagery of Sentinel-2 with terrestrial laser scanning.

We investigated a study site from the Nuuksio national park in southern Finland. The size of the study site was 1.7 ha and controlled burning was carried out on June 8th, 2021. In controlled bunnings carried out in Finland, only the vegetation on the forest floor is burned. In other words, the fire is not allowed to spread to trees, but it burns grasses, twigs, and possible fuel load on the forest floor (e.g. cleared/cut suppressed trees).

A plot of 1 ha was established within the study site and TLS data were acquired twice in the summer of 2021, between June 4 and 6 as well as between June 28 and 30 (i.e. before and after the burn). The scan locations were placed in every 10 meters including altogether 100 scans. We used RIEGL VZ400i scanner that uses time of flight measurement principal and records multiple returns from each sent laser pulse. We used scan resolution of 40 mdeg (i.e. beam divergence 0.7 mrad) resulting with scan resolution of 14 mm at 20-m distance, and laser pulse repetition rate of 1.2 MHz. The scans were filtered and registered as one harmonized point cloud with RiSCAN PRO software.

The Sentinel-2 Level-2A product was used here as it includes scene classification and atmospheric corrections. Sentinel-2 Level-2A imagery from May 24 to July 3, 2021 were utilized and gradient of spectral response (i.e., normalized burn ratio, NBR) was generated for each date. NBR uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths, and it was designed to take advantage of the different responses that disturbed and undisturbed areas will have in the NIR and SWIR spectral regions (Cohen and Goward, 2004). The NBR has showed to be related to structural component of vegetation (Epting & Verbyla 2005, Pickell et al. 2016) thus, it was utilized here as a measure for burned forest biomass of the controlled burning site.

The points of the before and after fire TLS data sets were classified into ground points and non-ground points (i.e. vegetation points) by utilizing the lasground-tool in LAStools (rapidlasso GmbH) software, and before and after digital terrain models (DTMs) were generated from a triangulation irregular network. Parameters for normalization in lasground-tool were tuned according to Ritter et al. (2017).

The difference between the before and after DTMs were studied to quantify the burned biomass on the forest floor. That was linked to the change in NBR values before and after the controlled burning. The NBR value before controlled fire was ~0.44 and it declined to 0.23 after the burn. And as we know the in the controlled burning only vegetation on the forest floor, the preliminary results indicate that also this kind of lower-level burn severity can be identified from a within-year optical satellite time series. This already brings new knowledge as previously mainly stand-replacing fires have been identified from yearly Landsat time series (White et al. 2017).

References

Banskota, A., et al. 2014. Forest monitoring using Landsat time series data: A review. Canadian Journal of Remote Sensing 40: 362-384.

Bolton, D.K., et al. 2017. Assessing variability in post-fire forest structure along gradients of productivity in the Canadian boreal using multi-source remote sensing. Journal of Biogeography 44: 1294-1305.

Calders, K., et al. 2015. Nondestructive estimates of above-grund biomass using terrestrial laser scanning. Methods in Ecology and Evolution 6:198-208.

Chuvieco, E., et al. 2019. Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment 225: 45-64.

Chuvieco, E., et al. 2020. Satellite remote sensing contribution to wildland fire science and management. Current Forestry Reports 6: 81-96.

Cohen, W.B. and Goward, S.N., 2004. Landsat's role in ecological applications of remote sensing. Bioscience 54: 535-545.

Epting, J. & Verbyla, D. L. 2005. Landscape Level Interactions of Pre-Fire Vegetation, Burn Severity, and Post-Fire Vegetation over a 16-Year Period in Interior Alaska. Canadian Journal of Forest Research 35: 1367–1377.

Flannigan, M.D., et al. 2000. Climate change and forest fires. Science of The Toal Environment 262: 221-229.

Frolking, S., et al. 2009. Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impact on aboveground biomass and canopy structure. JGR Biogeosciences 114: G00E02.

Lentile, L.B., et al. 2006. Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire 15: 319-345.

Liang, X., et al.. 2014. Automated stem curve measurement using terrestrial laser scanning. IEEE Transactions on Geoscience and Remote Sensing 52: 1739-1748.

Luoma, V., et al. 2019. Examining changes in stem taper and volume growth with two-date 3D point clouds. Forests 10(5): 382.

Luoma, V., et al.. 2021. Revealing changes in the stem form and volume allocation in diverse boreal forests using two-date terrestrial laser scanning. Forests 12: 835.

Metz, J., et al. 2013. Crown modeling by terrestrial laser scanning as an approach to assess the effect of aboveground intra- and interspecific competition on tree growth. Forest Ecology and Management 213: 275-288.

Oswald, B.P. 2007. San Diego Declaration on Climate Change and Fire Management: Ramifications for fuels management. In: Butler, B.W., Cook, W. (comps) The fire environment, management, and policy. Conference proceedings. 26-30 March 2007, Destin, FL, USA.

Pickell, P.D., et al. 2016. Forest recovery trends derived from Landsat time series for North American boreal forests. International Journal of Remote Sensing 37: 138-149.

Pugh, T.A.M., et al. 2019. Important role of forest disturbances in the global biomass turnover and carbon sinks. Nature Geoscience 12: 730-735.

Pyörälä, J., et al. 2018. Quantitative assessment of Scots pine (Pinus sylvestris L.) Whorl structure in a forest environment using terrestrial laser scanning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11: 3598-3607.

Ritter, T., et al 2017. Automatic mapping of forest stands based in three-dimensional point clouds derived from terrestrial laser-scanning. Forests 8: 265.

Seidel, D., et al. 2011. Crown plasticity in mixed forests-Quantifying asymmetry as a measure of competition using terrestrial laser scanning. Forest Ecology and Management 261: 2123-2132.

Stephenson, C., et al. 2012. Estimating the economic, social and environmental impacts of wildfire in Australia. Environmental Hazards 12: 93-111.

Szpakowski, D.M. & Jensen, J.L.R. 2019. A review of the applications of remote sensing in fire ecology. Remote Sensing 11: 2638.

Venäläinen, A., et al. 2020. Climate change induces multiple risks to boreal forests and forestry in Finland: A literature review. Global Change Biology 26: 4178-4196.

White, J.C., et al. 2017. A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment 194: 303-321.

Yrttimaa, T., et al. 2020. Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning. Remote Sensing 12: 2672.

With the launch of the Sentinel 1 and 2 missions, using EO-data for the monitoring of agricultural production at the parcel level has really opened up. In parallel, evolutions in computing technology as well as the rise of artificial intelligence (AI) in the EO-domain have enabled researchers as well as the industry to exploit the wealth of information in these EO-data. One of the main bottlenecks that remain is the availability of reliable and abundant in-situ data, which is needed to convert the EO-data into meaningful information. Concrete examples are information on crop type, management practices, biomass production and yield.

There is a plethora of data available, for example through open-access publications, or initiatives from international organizations such as the Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) Joint Experiment for Crop Assessment and Monitoring (JECAM) sites, the International Institute for Applied Systems Analysis (IIASA) citizen science platforms (LACO-WIKI GEO-WIKI), the Radiant MLHub, the Future Harvest (CGIAR) centers, the National Aeronautics and Space Administration Food Security and Agriculture Program (NASA Harvest) etc. However, these data are scattered over many different sources, lack standardization and/or have incomplete metadata. This hampers the re-use of these data by others, causing an inefficient use of resources, while also impacting the resulting product quality.

To address this problem, the added value of the centralized in-situ data is exploited within the e-shape project. The system under study is a combination of three components: (i) the CropObserve app, specifically designed to facilitate the easy collection of information at the parcel level; (ii) AGROSTAC for the curation and public dissemination of the data, and (iii) EO-based monitoring services that are calibrated/validated with these in-situ data. The different components are discussed in more detail below.


CROPOBSERVE

This app was initially developed by IIASA to enable the collection of crowd-sourcing information on agricultural fields by non-experts. The first component is the CropObserve app, which enables the collection of parcel-based information in the field. The information that can be provided is grouped into four categories, i.e. crop types, phenological stage, visible damages, and management activities. Each category is optional, and can be left open.

- crop type: a cascading selection menu is foreseen that allows you to provide the crop type at different levels of detail. This option was foreseen to keep the overview of crop types without cluttering the screen, as well as allow the user to provide the information with which the user is confident, as not all users are agricultural experts (e.g. for Winter Wheat, the cascading options are Cereals – Wheat – Winter Wheat).

- Phenological stage: this refers to the condition of the crops and/or field at the moment the field is visited. Only a few generic crop development stages are included, as these may vary between crop types.

- Visible damage: a number of damages are foreseen to be logged, such as drought, frost or flood.

- Management activity: this enables tracking specific activities that occur on the field that occur on a specific day. Examples are Ploughing, Planting, or Harvest. It is important to make the distinction between logging the current state (e.g. the field is harvested, but the user does not know when this happened) which should be logged as a phenological stage, and the specific action on the field (Harvest, which occurs at the specific moment/day of providing the input in the app).

All data collected through the CropObserve app is automatically made publicly available through AGROSTAC (see below)


AGROSTAC

For a centralized hosting and curation of in-situ data, WENR and VITO initiated AGROSTAC (Janssen et al., 2012). AGROSTAC collects and harmonizes georeferenced open data around key agronomy observations such as crop type, phenology, biomass, yield and leaf area. Published, open data sets are screened for key observations and selected data goes through a dedicated data curation procedure. This is a crucial step to ensure re-use of data beyond its original purpose of collection. In this procedure, meta data are checked and completed using all information available in the data files, supporting documents and associated publications. Data are converted into standard units, and phenology events are mapped to the BBCH scale. As a result, data can be offered in a FAIR manner (Findable, Accessible, Interoperable, Reproducible), and ready for use. To date AGROSTAC includes data sets from ODJAR (odjar.org), generic repositories like DataVerse (https://dataverse.harvard.edu), specific initiatives and phenology networks. All the data collected through CropObserve is ingested in AGROSTAC, where it will undergo quality checks before being published and made accessible to the public.



EO-BASED SERVICE DEVELOPMENT

The data made available in AGROSTAC, coming from CropObserve and other sources, can be an invaluable source of information to calibrate and validate EO-based monitoring services. Here the example is provided for the development of tools to monitor crop calendars, and specifically two events: Crop Emergence and Harvest.

The initial development of the methodologies was done with a limited data set, which was already available through another project. This data was used to make decisions on the methodological set-up (e.g. which AI-methods, how to go from model-outputs to exact harvest date). A detailed description of the method itself is described in Bonte et al. (2021). However, through this exercise on the integrated in-situ data flows, the transfer from a locally-trained model to a performant monitoring method could be done, by including training data with more variability in crop types, growing conditions, soil types, etc. One the one hand the robustness of the harvest detection method could be evaluated at a European scale, and on the other hand the models could be re-trained to be more performant at this scale.




Through this workflow with its integrated components, the importance of high-quality in-situ data became very clear. For many monitoring services, the limited availability of proper in-situ data over larger regions is the main bottleneck that hampers the scaling-up, and thus the operational rollout. This is to a large extent mitigated through the centralized and curated data dissemination via Agrostac, while the CropObserve app enabled the collection of the needed data over Europe. As such this work was also in support of the GEOGLAM in-situ data working group, whose goal it is to build a community of practice to openly share agricultural in situ data to promote research in operational activities for global agricultural monitoring.



REFERENCES

Bonte, K., Moshtaghi, M., Van Tricht, K., & Tits, L. (2021, July). Automated Crop Harvest Detection Algorithm Based on Synergistic Use of Optical and Radar Satellite Imagery. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 5981-5984). IEEE.

Janssen, S., van Kraalingen, D., Boogaard, H., de Wit, A., Franke, J., Porter, C. and Athanasiadis, I.N., 2012. A generic data schema for crop experiment data in food security research. In Proceedings of the sixth biannial meeting of the International Environmental Modelling and Software Society (pp. 2447-2454).

Terrestrial ecosystems provide a very wide range of essential ecosystem services, but these services are under increased levels of stress due to climate change. Ecosystem structure and climate are closely-linked: changes in climate lead directly to physical changes in ecosystem structure and vice versa. To improve our understanding of this relation and ecosystem resilience, we require information of the fine-scale structural heterogeneity between individual trees. This will be key for effective forest management and to support climate mitigation actions appropriately. Drought experiments, which simulate drought conditions by excluding rainfall from a given zone, bring highly valuable information on the mechanisms involved in the response of ecosystems to drought (Bonal et al., 2016; Tng et al., 2018; Meir et al., 2018). Novel techniques using 3D laser scanning (LiDAR – Light Detection and Ranging) can provide us with a new way to estimate the structure of individual trees. Terrestrial laser scanning (TLS) can measure the canopy structure in 3D with high detail, and several algorithms have recently been developed to produce full 3D models of trees down to fine (cm) scale. Terryn et al. (2020) calculated 17 different structural tree metrics in the context of tree species identification. Within this study we explore similar structural metrics to determine the long -and short term affect of water availability on tropical tree structure. The long term effect is reflected through three wet tropical rainforest sites on a rainfall gradient (2000-4000 mm/y). The short term effect will be investigated through an induced drought experiment which has been scanned after a time-span of three years with TLS. For this purpose we will extract about 130 individual trees from the TLS data of the three tropical forest plots in Australia. Quantitative Structural Models (QSMs) will be built with TreeQSM to obtain the individual tree structures. Tree structure from the three sites will be compared to assess the long-term adaptation. The structure of the control trees will also be compared with the tree structure of the drought induced trees using structural metrics obtained from the QSMs.

Introduction

We present an overview of the backgrounds and recent developments of the EuroCrops dataset [3] and its crop taxonomy, denoted as the Hierarchical Crop and Agriculture Taxonomy version 2 (HCATv2), alongside an exploration of its potential use-cases and possibilities.

Background
Within recent years, an increasing number of member states of the European Union has decided to release data they initially collected in the scope of the agricultural subsidy control. This data, containing the crop type together with the corresponding geo-referenced field parcel polygon, can be used as reference data for various applications, such as vegetation analysis and crop classification with satellite imagery, biodiversity monitoring and the impact of climate change on crop harvest. So far, most publications focus on only a small fraction of the available data due to the exponentially increasing effort to homogenise the data at the transnational level [2, 5].

General Problems
Firstly, the collection of the data itself proves to be more laborious than widely assumed. Even though many countries decide to release their data to the public, the means of distribution platforms differ widely, ranging from the commonly known INSPIRE platform (inspire.ec.europa.eu) to websites that are only available in the national language. Some countries do not even offer an option to download the data, but are willing to send them to those who request it. Therefore, we connected with official GIS and agricultural authorities from European Union member states to ensure an accurate collection of available crop datasets.
Secondly, the majority of subsidy control data we obtained comes with country-specific crop identifiers and class names in the respective national language. An automatic translation into English does not suffice to grasp the complexity of these classes and the former lack of a common ground that captures all country-specific taxonomies made it challenging to make use of the data.

Main Results
With EuroCrops and the updated corresponding taxonomy HCATv2, we propose an approach to make data from a variety of countries available and comparable.
EuroCrops
As already introduced earlier [3, 4], we are compiling a dataset that includes all publicly available crop reference data from the European Union. By November 2021, we have gathered data from sixteen different countries, i.e., Austria, Belgium, Germany, Denmark, Estonia, Spain, France, Croatia, Lithuania, Latvia, the Netherlands, Portugal, Romania, Sweden, Slovenia and Slovakia. The years for which we have received data from the aforementioned countries, alongside the data itself, can be found on our maintained website www.eurocrops.tum.de. As explained before, collecting the data is not sufficient and, therefore, we developed a new taxonomy into which we fit the data gathered within the past year.
HCATv2
The Hierarchical Crop and Agriculture Taxonomy (HCAT), as presented earlier [3], is derived from the Copernicus EAGLE matrix [1]. Efforts to develop the taxonomy are still ongoing, as each newly obtained dataset must be manually fit, with the complete HCATv2 expected to be released within 2022. Compared to its predecessor, HCATv2 includes five times more classes, organised across six levels and will eventually aim to capture most crops types cultivated within the European Union.

Context
With EuroCrops and HCATv2, we hope to give researchers the opportunity to develop models that generalise better to unseen data, while being able to take into account as many classes as desired. In addition, by keeping most information of the original datasets and publishing the mappings of the national crop classes to HCATv2, we want to encourage authorities, industry, and academia to make use of and contribute to a harmonised European crop taxonomy.

Perspective
We anticipate that the development and publication of our dataset will persuade more countries and districts to publish their crop data, leading to a European transnational dataset.

References
[1] S. Arnold, B. Kosztra, G. Banko, G. Smith, G. Hazeu, M. Bock, and N. V. Sanz, “The EAGLE concept – a vision of a future European land monitoring framework,” in EARSeL Symposium proceedings, "Towards Horizon 2020", 2013, pp. 551–568.
[2] M. Rußwurm, C. Pelletier, M. Zollner, S. Lefèvre, and M. Körner, “BreizhCrops: A Time Series Dataset for Crop Type Mapping,” in ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLIII- B2-2020, 2020, pp. 1545–1551. DOI : 10.5194/isprs-archives-XLIII-B2-2020-1545-2020.
[3] M. Schneider, A. Broszeit, and M. Körner, “EuroCrops: A pan-European dataset for time series crop type classification,” in Proceedings of the Conference on Big Data from Space (BiDS), P. Soille, S. Loekken, and S. Albani, Eds., Publications Office of the European Union, 2021. DOI : 10.2760/125905.
[4] M. Schneider and M. Körner, TinyEuroCrops, Dataset, Technical University of Munich (TUM), 2021. DOI: 10.14459/2021MP1615987.
[5] M. O. Turkoglu, S. D’Aronco, G. Perich, F. Liebisch, C. Streit, K. Schindler, and J. D. Wegner, “Crop mapping from image time series: Deep learning with multi-scale label hierarchies,” Remote Sensing of Environment, vol. 264, p. 112 603, 2021. DOI: 10.1016/j.rse.2021.112603.

Background and aim: Seasonal carbon fluxes over Amazonia are poorly resolved both spatially and temporally. This limits our ability to predict how climate change affects this globally important carbon sink. One reason for this is the lack of quantitative data on patterns of leaf phenology (leaf flushing, leaf shedding, leaf lifespan etc). Seasonal patterns of vegetation indices derived from spectral imagery suffer from a suite of confounding factors such as seasonally varying aerosol contamination, water vapor content, cloud cover and, sun-sensor geometry effects. As a result, studies on Amazon forest seasonality based on satellite imagery disagree on whether there is more greenness in the dry season than in the wet season. Furthermore, the relative contribution of leaf age and leaf area to the canopy greenness is difficult to assess from passive remote sensing alone. Both components may play a role in determining the seasonality of the water and carbon exchanges with the atmosphere.
There is therefore a need for characterizing temporal patterns of LAI and greenness separately. This has to be done at a scale large enough to capture the inter-species variability and also to cover an area commensurate with the pixel size of low spatial resolution/high temporal frequency satellite imagery such as MODIS (Moderate Resolution Imaging Spectroradiometer) or geostationary ABI (Advanced Baseline Imager). Such data may help resolve discrepancies between observed and modeled gas exchange at flux tower sites from Eddy covariance measurements when temporal variation in LAI and potential carboxylation rate per unit leaf area are neglected.

Material and methods: Year-round UAV acquisitions were conducted from October 2020 to November 2021 with RGB, multispectral and LiDAR sensors within the Guyaflux tower footprint at Paracou Field Station, French Guiana. LiDAR scanning data were acquired using a Riegl Minivux UAV1 sensor encased within the Yellowscan VX20 system, along with an Applanix 20 IMU, with post-processing kinematics differential GNSS. The miniature LIDAR was calibrated against a more sensitive full waveform LIDAR (Riegl LSQ780) and transmittance estimates were validated against independent light sensor measurements.
Two overlapping regions of interest (ROI) were defined and were scanned every two to three weeks (21 sampling dates). A 7ha-core area contributing an estimated 25% to the gas exchanges measured at the flux tower was covered at high density (>200 points.m-2, median density 340 points.m-2). A larger area (32ha) contributing an estimated 60% was covered at lower density (> 50 points.m-2, median 70 points.m-2).
We used the open-source AMAPvox software (http://www.amapvox.org) to analyse laser signal extinction in the canopy. The scene was voxelized at 1m resolution. In each voxel, local extinction rate was computed from incoming and outgoing laser pulses. Plant Area Density (PAD) was then derived from the extinction rate using the Beer-Lambert turbid medium approximation.
Time series of 3D maps of PAD were produced for both ROIs (7 and 32 ha).
50 light sensors (two above the canopy and 48 at ground level) were also deployed to measure light transmittance over the same one-year period every half hour.
Results
The first year of data collection has just been completed and data analysis is ongoing. We will show results that give insight on the following topics:
Seasonal variation in LAI: We are generating 1m2-Plant Area Index (PAI) maps by summing PAD vertically at each date over both ROIs. The timing and amplitude of the seasonal variation in PAI will be evaluated at various resolutions: entire ROIs, per ha, per 50x50m quadrat and per individual tree crown. Seasonal patterns of LAI at ground sensor locations will be compared to light measurements for checking the consistency between data sets.
Spatial variability in LAI dynamics: PAI dynamics at fine spatial resolution will be correlated with maps of water availability derived from the Topographic Wetness Index.
Asynchrony between upper and lower canopy: We shall use the dense data set (7 ha ROI) which provides a better sampling rate of the lower canopy, to compute PAI per stratum (above and below 20m height above ground level). We shall test if variation in PAI is negatively correlated across upper and lower strata (as previously noted to be the case in some areas of the Amazon) and, if so, at what spatial resolution.

The Infra-Red Sounder (IRS) instrument is the primary payload of the Meteosat Third Generation Sounder satellite (MTG-S). The main objective of the MTG sounding mission is to enhance Numerical Weather Prediction (NWP) capabilities at regional and global scales, through the provision of Atmospheric Motion Vectors (AMV) with higher vertical resolution and frequent information on temperature and water vapour profiles. Additionally, layer-by-layer analysis of the atmosphere will offer greater insight into its complex chemical composition and support atmospheric gas tracing applications, such as air quality and pollution monitoring. The MTG-S satellite and the IRS instrument are being developed under the responsibility of OHB System (Germany), with TAS (France) as MTG’s mission prime contractor and ESA/EUMETSAT as end customers.

The IRS will be the first European hyperspectral sounding instrument in geostationary orbit and will be capable of scanning a full Earth’s disc (over Europe and Africa) every hour with a spatial resolution of 4 x 4 km² at nadir, covering roughly 640 x 640 km² per stare every 10 seconds. The design of the instrument is based on an imaging Fourier Transform Infrared Spectrometer (FTIR) and will deliver hyperspectral sounding information in two infrared bands: LWIR (700 - 1210 cm-1) and MWIR (1600 - 2175 cm-1), with a spectral channel interval of 0.625 cm-1. The instrument incorporates two IR detectors of 160 x 160 pixels each, which leads to a total of 51,200 delivered interferograms per stare. The recorded interferograms will undergo pre-correction and data compression before being sent to ground. Once the data is received on-ground, the Level 1 ground processing algorithms will convert the measured interferograms into radiometrically and spectrally corrected spectra.

Applications benefiting from the Level 1 science data provided by the IRS instrument are manifold and await to be fully explored. For example, by delivering frequent four-dimensional information on humidity, temperature, and wind profiles, the IRS will significantly enhance regional and global NWP, thus improving early detection (and warning) of rapidly developing atmospheric instability like severe convective storms. The spectral range of the IRS will also allow to estimate and monitor the concentration of atmospheric trace gases like ozone and carbon monoxide, leading to enhanced information for air pollution forecasting. Moreover, through information on the composition and density of volcanic ash clouds, ash fallout prediction models will be refined.

The first flight model of the instrument (IRS PFM) is currently in its AIT phase that will culminate with an optical performance test in vacuum starting in Q2 2022. The instrument verification follows a proto-flight approach, including tests successfully carried out on several development models (i.e. STM, Flat-EM and Core Spectrometer) and the achievement of the final qualification on the PFM. The first MTG-S satellite launch is planned by end 2023/early 2024.

This paper will provide a detailed overview of:
• the objectives and capabilities of the MTG sounding mission
• the design and development status of the IRS instrument
• the Level 1 science data provided by the IRS instrument and its expected performance

The Copernicus missions Sentinel-4 (S4) and Sentinel-5 (S5) will carry out atmospheric composition observations on an operational long-term basis to serve the needs of the Copernicus Atmosphere Monitoring Service (CAMS) and the Copernicus Climate Change Service (C3S).

Building on the heritage from instruments such as GOME, SCIAMACHY, GOME-2, and OMI, S4 and S5 are imaging spectrometer instruments covering wide spectral bands in the UV, visible, near infrared, and (S5 only) the short wave infrared domain. S4 will monitor key air quality parameters with a pronounced temporal variability by observing NO2, O3, SO2, HCHO, CHOCHO, and aerosols over Europe with an hourly revisit time. In addition to the S4 target species, S5 will observe CO, CH4, and stratospheric O3. S5 will provide composition data with global coverage on a daily basis serving climate, air quality, and ozone/surface UV applications.

A series of two S4 instruments will be embarked on the geostationary Meteosat Third Generation-Sounder (MTG-S) satellites. S4 establishes the European component of a constellation of geostationary instruments with a strong air quality focus, together with the NASA mission TEMPO and the Korean mission GEMS.

The presentation will provide an overview of the S4 and S5 missions and the related science and applications.

EUMETSAT has provided the user community with more than three decades worth of satellite data, starting with the geostationary missions of the Meteosat First Generation, and since 2002 with the Meteosat Second Generation (MSG) series satellites.

EUMETSAT is currently developing the future geostationary program, the Meteosat Third Generation (MTG). The MTG system will host a more advanced 16-channel VIS/IR Flexible Combined Imager (FCI) as well as a Lightning Imager (LI) on its geostationary imaging platform (MTG-I), whereas the sounding platform (MTG-S) will host the MTG InfraRed Sounder (IRS) and the Copernicus Sentinel-4 ultraviolet/near-infrared (UVN) sounding missions. The launch of the first two satellites MTG-I1 and MTG-S1 hosting the imaging and sounding instruments is foreseen in late 2022 and in early 2024, respectively.

The presentation will give an overview of the MTG system design, the system architecture, its observation missions, and some of the main improvements over Meteosat Second Generation (MSG) in terms of new missions and expected product performance.

More specifically, a brief status update will be provided on the Telemetry, Tracking & Commanding Facility (TTCF) and the Mission Operations Facility (MOF). Regarding data processing, the presentation will discuss the status up to launch and commissioning of the Instrument Data Processing Facility for both MTG-I and MTG-S (IDPF-I and IDPF-S, respectively) which process the mission data from Level 0 to Level 1, as well as the Level-2 Processing Facility (L2PF). The L2PF is also split in MTG-I and MTG-S parts in functionalities and deliveries.

Regarding the MTG system overall activities, the operations preparations are well underway and the so called system freeze is expected in June 2022. The stability of the LEOP Ground Segment has already been demonstrated, with solid operational knowledge of the LEOP contractor.

System Integration Verification Validation (IVV) are well underway, with System Version V1.0 completed. Operational Scenario Validation has started in December 2021. As a highlight of the system validation activities, it can be mentioned the successful full system end-to-end test (4 runs of 41 hours) involving Satellite Application Facilities (SAF) which was completed already in July 2021.

In this study, the assessment of the combined wind and wave energy potential is presented for locations in the North Atlantic, that is characterized by high energy swells generated by remote westerly wind systems as a consequence of extratropical cyclones (Ponce de León and Bettencourt 2021), and in the Mediterranean Sea where extreme waves are common. The objective is investigating the feasibility of satellite altimetry-based assessments of combined wind/wave renewable energy potential in the European shelf, taking advantage of the increased time and spatial coverage of the satellite altimetry constellation, composed of 10 past and present altimetry missions compiled in the ESA Sea State Climate Change Initiative data base (Dodet et al., 2020).

The method consists of using the homogenized multi-mission altimeter data, to estimate site wind and wave power densities. We use the empirical model of Gommenginger et al. (2003) to estimate the energy period, required for the computation of the wave power density from the altimeter Ku band significant wave height and radar backscatter coefficient. Waves buoys were used to validate the method.

Using Atlantic and Mediterranean sites as comparators for wind/wave correlation (Fusco et al., 2010) we show that wind/wave energy are relatively correlated in the Mediterranean, but not in the North Atlantic, which has implications for the efficient combination of renewable energy sources to make renewable energy supply more resilient. In particular, the co-location of wind and wave farms only has a strong rationale in locations where wind and wave resources are relatively uncorrelated. To date, while significant effort has gone into individually mapping wind and wave resources, little attention has focused on their temporal correlation. With a drive to 100% renewable energy, it is important that complementarity between individual renewable (including marine) energy resources, so that the most resilient forms of renewable energy, and combinations of renewable sources, are developed.

References

Dodet, G., Piolle, J.-F., Quilfen, Y., Abdalla, S., Accensi, M., Ardhuin, F., Ash, E., Bidlot, J.-R., Gommenginger, C., Marechal, G., Passaro, M., Quartly, G., Stopa, J., Timmermans, B., Young, I., Cipollini, P., Donlon, C., 2020. The Sea State CCI dataset v1: towards a sea state climate data record based on satellite observations. Earth System Science Data 12, 1929–1951, https://doi.org/10.5194/essd-12-1929-2020
Fusco F., Nolan G., Ringwood J., 2010. Variability reduction through optimal combination of wind/waves resources – An Irish case study. Energy 35, 310-325, https://doi.org/10.1016/j.energy.2009.09.023
Gommenginger, C.P., Srokosz, M.A., Challenor, P.G., Cotton P.D., 2003. Measuring ocean wave period with satellite altimeters: a simple empirical model. Geophysical Research Letters VOL. 30, NO. 22, 2150, https://doi.org/10.1029/2003GL017743
Ponce de León S., Bettencourt J.H., 2021. Composite analysis of North Atlantic extra-tropical cyclone waves from satellite altimetry observations, Advances in Space Research 68 762-772, https://doi.org/10.1016/j.asr.2019.07.021

Abdalmenem Owda, Merete Badger

1-0 Introduction

Synthetic Aperture Radar (SAR) has been increasingly used in wide range applications ranging from climatic changes, monitoring natural phenomena and hazards, change detection, pollution up to maritime applications. Huge number of daily observations become available for users around the world on a free, full, and open basis thanks to Copernicus services. SAR systems are unique remote sensing instruments; they provide high spatial resolution data, operate day-and-night regardless of cloud coverage and weather conditions.
With the advent of SAR data from the current SAR constellations, a paradigm shift occurred in many maritime applications and especially in offshore wind energy applications. SAR data have been used studying wind wakes at far regions of the offshore wind farms (OWFs), wind energy and resource assessment, development and planning of new OWFs. This study aims to characterize the physical structure of the far-wind wake based on the OWF’ capacity.

2-0 Wind speed retrieval from SAR data and wind wake analysis

SAR sensors overpass and illuminate the ocean surfaces with their own illumination system. The recorded echoes of the backscattered signals, called the normalized radar cross section (NRCS), can be related to the wind speed using a Geophysical model function (GMF). The function relates the sea surface roughness, radar incidence angle, relative wind direction to wind speed measurements by inversion of the model itself. In this study, CMOD5.n function is used to retrieve the values of wind. The function works well for the neutral stable condition.


where is the NRCS and is the angle between wind direction and scatterometer azimuth look angle (both measured from the North). The other coefficients shape the terms , is the sea surface wind (SSW) at 10 m.s.l and is the incidence angle [1].
The far-wind wake refers to the velocity deficit at downstream side of the OWFs due to wind turbine operation. The velocity deficit is computed by subtracting the mean wind velocity at upstream from the mean velocity at downstream side.



3-0 Study Area and selection criteria

The northern European sea has been enriched with the global offshore wind farms. It has about 5566 wind turbines extracting about 26 MW for 5 European countries (UK, Germany, Netherlands, Denmark, Belgium), according to the mid-year report 2021 [2]. This study will take OWFs with different capacities ranging between 200 and 1000 MW. Table 1 shows the information of the 4 selected OWFs.

Table 1: the selected OWFs for the study with their characteristics.
* We take the commissioning date of the last installed OWF in the cluster which refers to Meerwind Süd/Ost .

We have setup the following criteria before processing of the selected OWFs:
1)Only the winds blow from the land are considered for this study with width sector angle 90.
2)The wind direction sector angle is 90.
3) Only SAR wind speed scenes between cut-in and cut-out speed were taken in the study, since the wind wakes appeared well within this range of speed.
4)All scenes after the commissioned date of the selected OWFs were considered in the processing.
4-0 Preliminary and Expected results

Iinvestigate the ability of SAR to monitor the impact of the OWFs’ capacities on the wind wakes’ characteristics (area, length, maximum wind deficit, wind recovery length, e.g.), the loss on power production at far wake regions, and provide useful information for OWF developer. It is expected that the physical characteristics of wind wakes will be proportional with the capacity of OWFs. Figure 1a, shows the deficit areas at the far-wake region of the Nordsee cluster, it is obvious that the close background area of the cluster experienced the highest wind deficit (more than 5%) along a distance about 20 km. Additionally, about 2 % deficit were observed in the adjacent areas of the main deficit, afterward, the wind deficit percentile decreased which means the wind speed started to recover after 20 km away from the outer edge of the cluster. For the Butendiek OWF (figure 1b), the smallest capacity in the study, the situation was completely different in terms of the magnitudes of the physical wind wakes’ characteristic, smaller deficit areas and shorter wind wakes (less than 10km) were measured. Lastly, East Angila ONE‘s capacity is little bit than Nordesee cluster, it shows considerable amount of deficit ,but in term of magnitudes, the deficit area is smaller than what we observed in Nordsee cluster.

Figure 1: mean velocity deficit for all Sentinel scenes after the assigned commissioning date of the cluster in table 1. a, b, and c refers to Nordsee cluster, Butendiek and East Angila ONE, respectively. The percentiles refer to the average velocity deficit inside the polygon. The black arrows refer to the wind direction sector angle.

Any new installation for any future OWFs close to these processed OWFs and clusters will face the severe consequences of wind wakes of the neighbor OWFs. In this study, we are going to process the OWFs, the relationships and the magnitude of power loss for any future OWF close to these selected OWFs.


5-0 References

[1] H. Hersbach, A. Stoffelen, and S. De Haan, “An improved C-band scatterometer ocean geophysical model function: CMOD5,” J. Geophys. Res. Ocean., vol. 112, no. 3, pp. 1–18, 2007, doi: 10.1029/2006JC003743.
[2] Offshore wind energy 2021 mid-year statstics, accessed online at 10/12/2021 14:20 am, https://windeurope.org/intelligence-platform/product/offshore-wind-energy-2021-mid-year-statistics/

Atmospheric aerosols have the highest impact on surface solar irradiance under cloudless conditions. Aerosols scatter and absorb solar irradiance, resulting in an attenuation of the direct normal irradiance (DNI) and an increase in the diffused horizontal irradiance (DHI). Ultimately this causes the total global horizontal irradiance (GHI) at the ground surface to reduce.
The pre-monsoon period in the arid and semi-arid regions of the Indian subcontinent is conducive to dust storm events due to the intense surface heating and steep atmospheric pressure gradients in summer. This study analyzes the degradation in accuracy of satellite-retrieved GHI under situations of high atmospheric dust aerosol content.
The Heliosat method [1] is used to derive cloud index (CI) maps from Meteosat-8 visible channel images for a period in June 2018 during which heavy dust storms were reported in Northern India. Two sources of clear sky data are utilized for transforming CI to GHI. They are the Dumortier model [2] with climatological turbidity values and the McClear method [3] within CAMS with measured or modelled aerosol optical depth (AOD) values. The satellite estimates are validated against ground measured GHI from a BSRN station. Measurement of particulate matter from a nearby air quality monitoring station is used to analyze the dust AOD modelled by CAMS. The results show that there is a large under-estimation of satellite retrieved GHI derived using the McClear model, while the GHI derived with the Dumortier model shows an over-estimation.

References:
1. Rigollier, C., Lefèvre, M., Wald, L., 2004. The method Heliosat-2 for deriving shortwave solar radiation from satellite images. Solar Energy 77(2) 159-169.
2. Dumortier, D., 1995. Modelling global and diffuse horizontal irradiances under cloudless skies with different turbidities. Daylight II, jou2-ct92-0144, final report vol, 2.
3. Lefèvre, M., Oumbe, A., Blanc, P., Espinar, B., Gschwind, B., Qu, Z., Wald, L., Schroedter-Homscheidt, M., Hoyer-Klick, C., Arola, A., Benedetti, A., Kaiser, J.W., Morcrette, J.-J., 2013. McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions. Atmospheric Measurement Techniques 6 2403-2418

The modern mining industry is of considerable importance to the global economy as it delivers a great range of mineral products for industry and house-hold consumers. The consequence of the considerable significance of the mining and mineral processing industry is not only a great diversity of provided mineral resources but also a massive amount of wastes generated. In fact, the mining sector is one of the largest, if not the largest waste producer on Earth (~25-50 Gt per year). The wastes of the mineral industry are generally useless at the time of production, yet they can still be rich in resource ingredients. Unfavourable economics, inefficient processing, technological limitations or mineralogical factors may not have permitted the entire extraction of resource ingredients at the time of mining and mineral processing. In the past, inefficient mineral processing techniques and poor metal recoveries produced wastes with relatively high metal concentrations. Thus, old tailings and waste rock piles that were considered worthless years ago are now “re-mined”, feeding modern mining operations (e.g. gold tailing piles in South Africa). Feasibility studies on the possible re-processing of such discarded materials require detailed site information. To date such feasibility studies commonly rely on time-consuming and costly ground surveys.

The AuBeSa project ("Automated identification, measurement and mineralogic classification of mining heaps and tailings ponds via satellite remote sensing") aims to create a database containing the location, material volume and mineralogical composition of mining heaps and tailings ponds using satellite remote sensing data and artificial intelligence (AI) computing systems. During the project, AI algorithms will be programmed and trained based on satellite images to identify mining locations and collect aforementioned wastes properties. To achieve this, the following activities are pursued:
1. Training of a machine learning (ML) model that extracts mining tailings and heaps from Sentinel-2 satellite images and classifies them automatically;
2. Volume estimation of the identified objects via topographic satellite data;
3. Mineralogical and chemical analysis of the waste material based on hyperspectral PRISMA-satellite data.

The methodology has been initially developed for mine sites in arid and semi-arid areas of Chile and Peru, because these areas allow an easier analysis of the land surface area and have existing ground-truth data sets. In future, other mines sites in areas of different climate and vegetation zones as well as unknown waste characteristics will be investigated. The acquired database is to provide information on waste dumps and tailings heaps to the mining sector so that well-informed decisions can be made on the possible extraction of remaining resources from these materials. It is expected that automated detection, surveying and classification of mine wastes using satellite remote sensing and AI systems will (a) allow faster decisions on the likely resource potential of individual waste repositories, and (b) reduce the dependence on ground surveys.

Acknowledgements:
This work was supported by the German Federal Ministry of Education and Research and is part of the AuBeSa project (grant number 01|S20083B).

Net-zero commitments require a transition to cleaner transport and renewable energy storage, but this poses many challenges for energy and mineral supply chains. Low-carbon technology is intensive from a mineral perspective. If our planet is to remain within the COP21 Paris Agreement commitment of a global average temperature increase of well below 2°C, the World Bank estimates that more than three billion tons of minerals and metals will be needed to deploy wind, solar and geothermal power, as well as energy storage [1]. Against this background, the demand for battery metals, such a lithium and cobalt, is set to reach 500% of current production levels by 2050 [1]. Mineral supply will be critical in determining the speed and scale at which green energy technologies can lower greenhouse gas emissions and enable climate-resilient development. At the forefront of green technologies are electric vehicles, where Li-ion rechargeable batteries are a fundamental component. Lithium is also used for the production of other batteries, such as cell phones and laptops. The battery market currently takes the 71% of global lithium production [2]. Worldwide lithium production rose more than 300% from 25300 tons in 2010 to 82000 tons in 2020 [2,3].

Within this context, the search and extraction of lithium is becoming an important revenue source for many world economies. In particular, the U.S. Geological Survey [2] estimates that the first three producers lie within the South American ‘Lithium Triangle’, i.e. Bolivia, with 21 million tons resources, Argentina, with 19.3 Mt, and Chile, with 9.6 Mt. Security of supply of lithium to the global markets and increasing expectations by consumers for responsibly sourced raw materials result in a growing global interest in lithium resources and their extraction.

This study uses the largest salt flat in the world, the Salar de Uyuni in Bolivia, as a test site to develop a repeatable and seamless workflow to track lithium from its source in the watershed to the salar nucleus at its highest concentration. It provides a systems-based understanding of aspects of lithium-brine deposit genesis that can contribute to broader considerations on the reporting of Li resources, such as assigning uncertainty bounds to resource estimates. For this study, open source Earth observation (EO) data is analysed to support geological and hydrological research. We explore the potentials of EO data for several research aspects, such as (1) Jointing: it may influence fracture-flow of groundwater and also be significant in terms of surface-area for water-rock interaction, i.e. potentially increasing the "leaching" rates of Li from the bedrock into the water; (2) Weathering: the degree and style of weathering may influence the liberation of Li from rocks into the water; (3) Distribution of clays: the distribution of clays that may restrict the liberation of Li from weathered rock, or may scavenge Li from passing water; (4) Water and moisture: the distribution of water-bodies and sources, including active streams, springs etc. We are building a groundwater recharge model having as input soil moisture content; (5) Geological structure: the presence of neotectonic faults that may disrupt the salar, as well as structures that may provide pathways for the flow of fluids; (6) Lithological mapping and classification: possible refinement of existing geological maps.

In conclusion, we found that by constructing a flexible and repeatable workflow the question how does lithium reach the salar de Uyuni can be addressed. This workflow will support the sustainable management of lithium in the region. Moreover, the provision of "fit for purpose" systems of tracking Li helps in filling gaps in existing methods to enable Li brines resources to be correctly reported.

[1] Kirsten Hund, Daniele La Porta, Thao P. Fabregas, Tim Laing, John Drexhage. Minerals for Climate Action: The Mineral Intensity of the Clean Energy Transition. World Bank Group Report; 2020.
[2] USGS. Lithium: Mineral Commodity Summary. 2021.
[3] USGS. Lithium: Mineral Commodity Summary. 2011.

The modern world is built upon a network of complex, globe-spanning supply chains, where critical natural resources are extracted, processed, and transported with a bare minimum of oversight. Mining of metal ores, mineral and fuel sources is fundamental to both current and future green economies but requires vast earthworks as well as intensive processing and refinement operations. A major agreement from the COP26 summit, the Glasgow Climate Pact, stipulates a “phasing down” of coal rather than a complete phase out. The question follows: how does this translate to real action to mitigate the worst polluting hydrocarbon energy resource? How many countries will continue to use coal as part of their energy mix without the technology or monitoring infrastructure to verify its continued environmental impact? Terrabotics provides easy to understand metrics, enhanced by AI, to collect, organise and make sense of the gamut of geospatial data on coal mine operation, allowing decision makers, regulators and operators to assess the environmental impact of coal mine sites objectively and reliably. Observing vast coal mining operations is highly suited to satellite earth observation owing to its wide spatial coverage, weekly-to-daily revisit times and much lower relative cost compared to ground, drone or aerial surveys. Using an array of optical, thermal and radar sensors aboard satellites, we have blended multi-sensor data analytics from this rich, ever improving data source to create a catalogue of key leading indicators pertaining to site operations, production capacity and ESG metrics.

We present a study of two major coal mines in the Unites States and Kazakhstan using Terrabotics’ Mine Monitoring platform (Minotor™) and data streams from the Energy SCOUT™ product portfolio. Using time series optical satellite data, we provide an overview of a site’s operation with change detection algorithms highlighting areas of increased activity or new construction. Thermal and emissions data help identify step changes in mining operations, the intensity of ground vehicular movements, activation of processing facilities and outgassing from extraction. Radar imagery time series detects the location of large vehicles, mining equipment and any other changes in infrastructure across the entire mine site.

Together, we have converted the numerous data feeds from independent satellite sources into valuable integrated metrics and actionable intelligence that allows operators, stakeholders, industry observers access to critical ESG performance and production data. Our goal at Terrabotics is to shine a light on critical but opaque natural resource supply chains, with the Minotor™ EO analytics platform providing real time intelligence and ESG/production forecasting. As we transition to economies free from fossil fuels, we must also meet the demands of a greener economy based on battery technologies that will inevitably require intensive mineral processing and extraction. Without comprehensive, cost-effective routine monitoring solutions of the world’s largest and most important mining facilities, we will fail to build a sustainable economy or succeed in a phased transition away from fossil fuels while managing their lasting impact on the environment.

The German sustainability strategy uses three indicators to measure the loss of land resources due to urbanization: The daily rate of urban expansion, the loss of open space, and the density of settlements. The quantitative objective is to reduce land take from a rate of 52 hectares per day in 2019 to less than 30 hectares per day by 2030, and further to zero by 2050. The data source used to measure the indicators are land use statistics aggregated from the cadastral survey. However, changes in the nomenclature of this dataset have negatively affected the reliability of the time series since the introduction of the ALKIS cadastral system in Germany from 2015 onwards. Alternatives on a European level such as Urban Atlas, Imperviousness and CLC land cover maps are frequently used for monitoring urban land changes instead. But these datasets also come with limitations including a low update frequency (every 3 or 6 years), coarse mapping units, and the inability to capture land use characteristics that match the intentions of policy objectives for urban land use change. They are therefore not fully suitable to monitor the annual land take in line with sustainable land use objectives.

On this background, the incora project [in German: Inwertsetzung von Copernicus Daten für die Raumbeobachtung / Adding value to Copernicus data for spatial monitoring] conducted by ILS - Research Institute for Regional and Urban Development gGmbH in cooperation with the BBSR - Federal Institute for Research on Building, Urban Affairs and Spatial Development and mundialis GmbH explores the potential of Earth observation techniques to support land use monitoring progress. Based on Sentinel-2 satellite images, our project applied machine learning, automatic training data generation and sub-pixel analysis techniques to produce annual Germany-wide land cover and imperviousness maps. A dedicated companion poster covers in detail the Sentinel-2 data processing steps while this contribution presents the overall project and its results.

The mapping products and the following change detection results were used to quantitatively measure urban land, open space, as well as their changes. By integration of auxiliary data, our project further calculated the annual urban land take, annual loss of open spaces, annual change of settlement density, structural characteristics as well as other indicators that are of interest to stakeholders. The project results demonstrate the added value of Copernicus data for support sustainable land use, including firstly, the derived products are spatially continuous and highly consistent in time series, which provides a solid base to study urban land dynamics at the national, regional and municipality levels. Secondly, it provides not only quantitative measurement but also the spatial distribution of urban land changes, which can be used for the analysis of urban sprawl and land fragmentation.

Expert feedback emphasizes that the monitoring of sustainable land use targets requires high validity of mapping results. We thus highlight our imperviousness mapping, an innovative subpixel analysis method, which improves the measurement accuracy in annual land take (see attached figure). The key advantages of this method are that firstly, it deals with the mixed pixel problem which often causes misclassification in the urban area. Secondly, the change layer contains less noise caused by the random changes in atmospheric conditions, sun angle, soil moisture, vegetation phenology. One disadvantage is that imperviousness is restricted to one land cover only. This was exactly compensated by the land cover classification map. Image classification provides specific land use classes and is, therefore, suitable to detect and characterize land use flows from previously open spaces to urban land in more detail. The conference presentation will show how satellite image analysis helps to validate and improve the information sourced from land use statistics, and how the incora project results contribute to more up-to-date and consistent information on land take for the monitoring of sustainable development targets.

The mitigation of human micro-nutrient deficiency (MND) is a major aim of United Nations Sustainable Development Goal 2 to “End hunger, achieve food security and improved nutrition and promote sustainable agriculture” by 2030. MND has been coined the “hidden hunger” because food supply may be sufficient but lack quality in terms of vitamins and minerals. Hidden hunger can lead to serious health problems including impaired physical and mental human development, susceptibility to various diseases, and reduced learning capacity. The prevalence of MND is assessed at the national level with Food Consumption Tables (FCTs) which report the nutrient content of grain or other crop yield. FCTs are seldom updated and many countries who lack resources employ FCTs from other countries. In doing so, it is assumed that the nutrient content is relatively stable over space and time. This assumption is a major source of uncertainty in nutrition analysis, because in reality nutrient content varies according to various crop growth conditions.

Satellite image data may be able to improve estimates of nutrient content, because they are able to monitor crop growth and development frequently over large areas. The bulk of data however consist of a few broad ( > 10nm) spectral bands. These bands are too coarse to distinguish many of the biophysical properties and biochemical processes related to nutrient content. Hyperspectral image data consist of several narrowbands that are able to make this distinction. Until recently, these data were collected mainly with spectroradiometers in the laboratory, field or mounted to drones and aircraft. The PRecursore IperSpettrale della Missione Applicativa (PRISMA) platform was launched on March 22, 2019. It is the first spaceborne hyperspectral mission in almost 20 years and ushers in a new era in such missions.

In our study, we used PRISMA hyperspectral narrowbands to predict the concentration of eight important nutrients (Ca, N, S, P, K, Fe, Mg, Zn) in four important global staple grains (corn, rice, soybean, wheat). We compared performance of PRISMA to Sentinel-2. Sentinel-2 is a multispectral broadband platform, but contains five experimental red-edge and near infrared narrowbands. The images were collected at the three main stages of crop development (vegetative, reproductive, maturity) in the 2020 growing season. A field campaign was conducted concurrently to sample grains over 60×60m2 survey frames. The nutrient content of the grains was determined in a laboratory using inductively coupled plasma - optical emission spectroscopy and a carbon, hydrogen, and nitrogen analyzer. The nutrients were predicted using Random Forest.

The accuracy of the PRISMA-based predictions (mg kg-1) of the nutrient composition of wheat ranged between a minimum of R2 = 0.49 (RMSE = 0.25) for N and a maximum of R2 = 0.74 (RMSE = 9.20) for Zn. The prediction accuracies for rice ranged from R2 = 0.54 (RMSE= 448.01) for P and to R2 = 0.73 (RMSE = 80.33) for S, for corn from R2 = 0.51 (RMSE = 279.17) for P and R2 = 0.73 (RMSE=99.92) for S. For soybean, the highest prediction accuracy was obtained for Ca (R2 = 0.88, RMSE=241.99). Nutrient composition predictions for wheat using Sentinel-2 ranged from R2 = 0.4 (RMSE = 0.29) for N to a maximum of R2 = 0.76 (RMSE = 373.5) for K, from R2 = 0.39 (RMSE= 12.17) for Fe. Good prediction results were obtained for rice, especially for Ca (R2 = 0.55, RMSE = 90.38), P (R2 = 0.51, RMSE = 235.4) and K (R2 = 0.50, RMSE = 288.7). Nutrient composition of soybean showed better prediction accuracy than the other crops with R2 >0.78 for all target nutrients. The sensitive spectra for various nutrients varied across the four investigated crops but they were mainly concentrated in the visible and Short-wave infrared (SWIR) regions for RPISMA data and, NIR, red-edge and SWIR regions for Sentinel-2.

Our study represents a first important step towards using remote sensing imagery for predicting the nutrient concentrations of crops. Future work will focus on testing the robustness of our predictions across larger geographic areas and several growing seasons.

In the framework of the United Nations (UN) 2030 Agenda for Sustainable Development and the New Urban Agenda (Habitat III), local and regional authorities require indicators at the intra-urban scale to design adequate policies in support of the Sustainable Development Goal (SDG) 11: Make cities and human settlements inclusive, safe, resilient and sustainable. Nevertheless, the current literature provides mainly national, regional and city scale indicators. Earth Observations (EO) data have been recently recognized as an essential source of information to achieve the SDG 11 targets and progress measurements with respect the SDG 11 indicators. However, the complexity of EO data handling and processing in SDGs monitoring and reporting mechanisms makes difficult a direct integration in evidence-based decision-making process.
In order to fill such gaps, this work presents the development and implementation of a set of workflows aimed at the automatic computation of some SDGs 11 indicators at intra-urban scale. A workflow is a process for generating knowledge from observation/simulation data and scientific models. The Virtual Earth Laboratory (VLab) framework (Santoro et al., 2020) was used as a cloud-based platform for sharing and facilitating the invocation of such scientific workflows from urban planners or technical employers of public administration without an extensive expertise on the EO domain. VLab implements all required orchestration functionalities to automate the technical tasks required to execute a model on different computing infrastructures, minimizing the possible interoperability requirements for both model developers and users.
A first workflow has been designed to extract essential variables devoted to the study of urban ecosystems such as the settlement map (built-up) and the population density map. The former can be obtained from a semi-automatic Sentinel-2 data classification procedure or directly by downloading the available European Settlement Map from the Copernicus Land Monitoring Service. Both maps are available at 10 m spatial resolution. Concerning the second variable, a specific workflow was developed for generating a population density map at a fine grid size (100 m X 100 m) from the ancillary population data per census area (Aquilino et al., 2020).
Additional workflows have been implemented for the computation of SDG 11.2.1 “Proportion of population that has convenient access to public transport” and SDG 11.3.1 “Ratio of land consumption rate to population growth rate.” The output maps are generated at regular grid of 100 m spatial resolution size.
Ancillary input, such as the population census data, as well as the local public transport map and additional auxiliary data need to be obtained from local authority providers. The diffusion of open data web portals results promising for the acquisition of such data for many cities.
The height of the buildings information (e.g., LiDAR data) is an optional input that, if available, allows to generate a population density map by applying the improved approach suggested by (Aquilino et al., 2021).
The workflows are validated considering the three Italian towns of Bari, Bologna and Reggio Calabria.
For reproducibility in other cities, the workflows are flexible to a wide variety of formats and geographical reference systems as input. GDAL/OGR standard formats are accepted. An advanced version of workflows is available for those expert users who intend to customize configuration parameters of the models. Besides, VLab frameworks make available a set of Web APIs designed to enable application developers to create dedicated Web applications based on models already available in VLab. By exploiting these latter functionalities, a dedicated web application will be developed as a tool for urban planner and policy-making to make EO data integration in SDGs measuring and monitoring operational for countries.

Aquilino, M.; Adamo, M.; Blonda, P.; Barbanente, A.; Tarantino, C. (2021). Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale, Remote Sensing, Special Issue “Earth Observations for Sustainable Development Goals”, 13, 2835, https://doi.org/10.3390/rs13142835

Aquilino, M.; Tarantino, C.; Adamo, M.; Barbanente, A.; Blonda, P. (2020). Earth Observation for the Implementation of Sustainable Development Goal 11 Indicators at Local Scale: Monitoring of the Migrant Population Distribution, Remote Sensing, Special Issue “EO Solutions to Support Countries Implementing the SDGs”, 12(6), 950, ISSN: 2072-4292, doi:10.3390/rs12060950

Santoro, M., Mazzetti, P., & Nativi, S. (2020). The VLab Framework: An Orchestrator Component to Support Data to Knowledge Transition. Remote Sensing, 12(11), 1795. https://doi.org/10.3390/rs12111795

The SDGs are a universal agenda to address the world’s most pressing societal, environmental, and economic challenges. Robust monitoring mechanisms and timely, accurate and comprehensive data are essential in guiding policies and decisions for successful implementation of the SDGs. Yet official statistics alone cannot provide all of the data needed to populate the SDG indicator framework. Along with EO data, citizen science, briefly defined as public participation in scientific research and knowledge production, offers a new solution and an untapped opportunity to complement traditional sources of data for monitoring progress towards the SDGs. The complementarity of citizen science and EO approaches for SDG monitoring has also been acknowledged in recent literature. For example, Fraisl et al (2020) carried out a systematic review of all SDG indicators and citizen science initiatives, demonstrating that citizen science data are already contributing and could contribute to the monitoring of 33 per cent of the SDG indicators. As part of this review, Fraisl et al. also identified overlap between contributions from citizen science and EO for SDG monitoring based on the mapping exercise undertaken by GEO (2017). The GEO analysis demonstrated that EO data can contribute to the monitoring of 29 SDG indicators, and Fraisl et al showed that citizen science could support 24 out of these 29 SDG indicators, which shows the complementarity between both data sources. One specific citizen science tool integrating citizen science and EO approaches that could complement and enhance official statistics to monitor several SDGs and targets is Picture Pile. Designed to be a generic and flexible tool, Picture Pile is a web-based and mobile application for ingesting imagery from satellites, orthophotos, unmanned aerial vehicles or geotagged photographs that can then be rapidly classified by volunteers. Picture Pile has the potential to contribute to the monitoring of fifteen SDG indicators covering areas, such as deforestation, post disaster damage assessment and identification of slums, among others, which can provide reference data for the training and validation of products derived from remote sensing.

This talk presents the potential offered by Picture Pile and other citizen science tools and initiatives to complement and enhance official statistics to monitor several SDGs and targets. Another example of the use of citizen science for SDG monitoring that will be highlighted in this talk is the Citizen Science for the SDGs project implemented in Ghana for monitoring SDG indicator 14.1.1b on plastic debris density. This project is a partnership between IIASA, the Ghana Statistical Service, the Ghana Environmental Protection Agency, UNEP, Ocean Conservancy, Earth Challenge, and others. The main achievement of the project is that citizen science data for monitoring beach litter have been integrated into the official SDG monitoring and reporting mechanisms of Ghana, which makes Ghana the first country to report on SDG indicator 14.1.1b and the first country using citizen science data for that purpose. Additionally, these data will serve as inputs to Ghana’s Ocean Plan, currently under development, as well as other relevant policies to address the marine litter problem. At the end of the talk, recommendations will be provided for how to enable partnerships and collaborations across data communities and ecosystems in order to bring citizen science and EO data into official statistics for SDG monitoring and reporting.

Coastal eutrophication is a global challenge that can result in harmful algal blooms, hypoxia, fish kills and other negative environmental impacts. To track the status of coastal eutrophication, Sustainable Development Goal 14 (as set by the United Nations General Assembly) includes an indicator, 14.1.1a - Index of Coastal Eutrophication Potential. Indicator 14.1.1a monitors changes in eutrophication directly, by analysing nutrients, or indirectly, by analysing processes that are caused by or are related to nutrient inputs such as algal growth. One of the challenges for tracking global eutrophication is lack of globally available and comparable nitrogen and phosphorus measurements for coastal areas. To address this gap, GEO Blue Planet, Esri and the UN Environment Programme have developed sub-indicators for the methodology to report on SDG 14.1.1a that use global satellite-derived chlorophyll-a products as outlined in United Nation Environment Programme’s Global Manual on Measuring SDG 14.1.1, SDG 14.2.1 and SDG 14.5.1. These global indicators are part of a progressive monitoring approach that seeks to build a foundation of available data that countries can build upon as they develop capacity for reporting on regional and national satellite data and in situ data. In this talk, we will present these global indicators along with efforts to work with member countries to utilize the resulting data for decision making and additional dashboards and visualizations being produced to assist with the identification of potential eutrophication hot spots and inform further analysis. The indicators include an indicator derived from the global ocean, 4km spatial resolution per pixel monthly mean product of the Ocean Colour Climate Change Initiative project’s product for each pixel within a country’s EEZ and an indicator derived from the National Oceanic and Atmospheric Administration’s VIIRS chlorophyll-a ratio anomaly product produced for the globe at 2km spatial resolution. The indicators will be compared to in situ data from member countries. Challenges to data access, product validation and other issues will be addressed.

ADVANCED EARTH OBSERVATION SATELLITE TECHNOLOGY. AN INTEGRATED SYSTEM TO SUPPORT THE ACHIEVEMENT OF SUSTAINABLE DEVELOPMENT GOALS AT THE COLOMBIAN AMAZON
Quiñones, M.J; Vissers, M.; Hoekman, D.; Kooij, B.; Luiken, R.; VanRooij, W & Pratihast, A.K.; Murcia, U.; Gómez, L.A.; Cuchía, A.; Acosta, H.H.; Carvajal, H.E.; Gil, C.; Rojas, A. & Erazo R.
In the frame of the ESA supported “EOSAT 4 Sustainable Amazon” project, advanced earth observation (EO) radar satellite technology was demonstrated to support the achievement of Sustainable Development Goals (SDG) for the Colombian Amazon. In close collaboration with Colombian governmental and non-governmental stakeholders, working for the sustainable development of the Colombian Amazon, EO radar-based products were defined in terms of required thematic information, spatial and temporal resolution.
Standardized Radar pre-processing steps follow automatic routines using in house developed algorithms and other commercial software like Gamma and IDL. Data downloads, interferometric registration, multitemporal filtering and orthorectification, precede thematic processing using in house time series analysis algorithms like SarSentry, SarFlood and baseline mapping algorithms like SarEcomap and SarSoil. Thematic Products include: 1) Deforestation historical change time series 2007-2017, based on ALOS-Palsar data; 2) Deforestation and degradation historical change time series 2017-2020, based on Sentinel-1 time series; 3) Baseline Ecosystem Map 2017 with 40 vegetation structural classes based on the classification of radar and optical data; 4) Flooding dynamics maps and flood frequency map, based on ALOS PALSAR ScanSAR data; 5) Time series 2007-2020 Fire dynamics time series 2017-2020, data from MODIS and VIIRS systems; 6) Soil degradation map-Area of Florencia 2017-2020, based on combined classification of ALOS PALSAR and Sentinel-1; 7) High resolution ecosystem mapping; 8) River border and island dynamics 2017-2020, based on Sentinel-1 time series analysis; 9) Location of Rock formations: Tepuis, based on SRTM analysis and 10) Near real-time monitoring for deforestation and degradation in 2021. Products were presented to the users through an in-house developed web-based GIS platform, created especially for the efficient exploration and integration of the data including some analysis tools.
Product evaluation and validation was done both by producers and users. Validation included the use of georeferenced aerial photographs acquired by the users and the comparison with existing available data sets, like thematic maps and deforestation information form the Biomass, GLAD-2 and RADD systems. In addition, the usefulness of the data was evaluated using a dedicated questionnaire. In general, all products were considered to be of excellent quality in terms of contents, and spatial and temporal resolution to monitor the environmental conditions of the Colombian Amazon. Some unique thematic information like the forest degradation, flood frequency, soil degradation and high-resolution ecosystem mapping exceeded the expectation of the partners, and were considered as unique and necessary to achieve the SDG. In general, the EO products created in the frame of this project can support the monitoring of the implementation of Sustainable Development Goals. Specifically: 1) Creation of new protected areas; 2) Improved management of protected areas; 3) Support Land use plans and Life plans for indigenous communities; 4) Climate change monitoring; 5) Sustainable Forest management; 6) Land and forest restoration projects; 7) Support Productive systems and value chains for green business; and last but not least communication and sensitization on environmental crisis to the Colombian civil society.
An integrated Monitoring system was proposed and evaluated by the users, in order to integrate the developed EO based products into the different activities supporting the achievement of the SDG. The frame consists of three main components: 1) Data production; 2) Data integration and 3) Data communication.

ESA and JAXA has been developed in the context of observation, monitoring and study of the Earth’s surface and atmosphere from space, with a view to cooperate for the use of synthetic aperture radar (SAR) satellites in the fields of earth science and earth observation applications.

ESA and JAXA recognize and agree to have an agreement for SAR cooperation. Since ESA and JAXA have both developed new generation L-band SAR missions, ESA and JAXA recognize the value to share an important experience in operational use of L-band SAR and intend to increase the benefits of synergies in the use of C- and L- band spaceborne assets. To proceed this cooperation, ESA and JAXA agreed to share the existing available SAR data from Sentinel-1 in Copernicus program and from ALOS-2 in JAXA to validate the value of C-band and L-band data to mutual interest area.

At present, both agencies jointly work for Polar Area Monitoring, Forest and Wetland Mapping, Ocean Monitoring, Snow Water equivalent, Soil Moisture, Monitoring Agriculture and GHG, Urban Monitoring, Natural and Urban Forest Monitoring, Monitoring of Geohazards and Joint validation Algorithm development of SAR.

In this session, invited speakers are expected to report ongoing and planning SAR satellites missions including ALOS-2, ALOS-4, Sentinel-1 and ROSE-L. Invited speakers are also expected to report the joint science and application early results using Sentinel-1 and ALOS-2 with ground-based observation data.

During the yearlong MOSAIC expedition (2019-2020) a significant number of synthetic aperture radar (SAR) images were collected from different sensors and in different modes. Here, we investigate the change in polarimetric features over sea ice from the freeze up to the advanced melt season using fully polarimetric L-band images from the ALOS-2 PALSAR-2 and C-band images from the RADARSAT-2 satellite SAR sensors. The sea ice is separated into four different sea ice types: (1) lead, (2) young (YI), (3) level (second-year ice (SYI) and first year ice (FYI) and (4) deformed sea ice.

Data and Method
R/V Polarstern drifted with two different floes and here we focus on the first drift that took place between 1 October 2019 and 31 July 2020. Areas of all four different ice types are observed in the vicinity of R/V Polarstern. These areas are included whenever possible in the yearlong time series of sea ice types. Though to densify the time series images not containing the ship are also included.

The SAR images were analyzed for seasonal changes in backscatter intensity values, and the scattering mechanisms were employed to further investigate separability between the different ice types. In particular there was a focus on the separability between the high backscatter older sea ice, typically SYI, and YI and FYI. Both sets of L- and C-band images were radiometrically calibrated using the included meta-data information and a 9 × 9-pixel median filter is applied to the data to reduce the noise effect before extraction of the polarimetric features. The normalized radar backscatter information for the HH, HV and VV channels were extracted together with the polarization difference (PD, VV-HH on a linear scale), the co-polarized and cross-polarized ratios (VV/HH and HH/HV) and the circular correlation coefficient. The images were incidence angle corrected to 35◦ using the method and slope values outlined in Mahmud et al. (2018), and the different sea ice types were identified using manually drawn regions of interests (ROIs). Data from helicopter borne instruments as well as in-situ data was used to evaluate the different sea ice types. The results are also compared to images collected during the Norwegian Young Ice (N-ICE) campaign in January-June 2015 (Johansson et al, 2017, 2018).

Results and discussion
Analyzing the backscatter values several observations can be made: (i) as expected we observe that there is a larger difference in the co-polarization channels between smooth and deformed ice in L-band compared to C-band during the freezing season, though (ii) this separation is significantly reduced during the early melt season. Moreover, we observe (iii) larger differences between young ice and deformed ice backscatter values in the L-band data compared to the C-band data, and (iv) linear kinematic features (LKF) are easier to detect in the L-band images. Throughout the year the HV-backscatter values show larger differences between level and deformed sea ice in L-band than C-band. The L-band data variability is significantly smaller for the level sea ice compared to the deformed sea ice, and this variability was also smaller than that observed for the overlapping C-band data. Thus L-band data could be more suitable to reliable separate deformed from level sea ice areas, as well as investigating the LKFs.

Within the L-band images a noticeable shift towards higher backscatter values in early melt season compared to the freezing season for all polarimetric channels is observed. Though no such strong trend is found in the C-band data. The change in backscatter values is first noticeable in the C-band images and later followed by a change in the L-band images, probably caused by their different penetration depth and volume scattering sensitivities. This change also results in a smaller backscatter variability for all polarimetric channels.

PD show a seasonal dependency for the smooth and deformed sea ice within the L-band data. For the L-band data were the PD variability for all ice classes reasonably small for the freezing season, with a significant shift towards larger variability during the early melt season, though during this period the mean PD values remained similar. However, once the temperatures reached above 0°C both the variability and the mean values increased significantly. For the C-band data no such trend is observed. However, for C-band the absolute PD values show significantly higher mean values for the thinner sea ice areas regardless of if these areas are low or high backscatter, and these areas also have low standard deviations. Compared to the high backscatter areas offered by SYI there is a significant difference were the PD values have a high standard deviation but a low mean value. Using PD, we can therefore separate out the young ice types from the surrounding sea ice and the SYI types. PD is also suitable for separation between the level and deformed sea ice areas during the freeze-up, as the variability is much higher for the deformed sea ice areas than for the level ice areas. To confirm the roughness level from the different ice types the circular correlation coefficient (CCC) is calculated, compared to airborne laser scanner (ALS) data and show good separability between the deformed and level sea ice types. However, CCC is sensitive to the signal-to-noise levels and care must be taken when analyzing the results. PD on the other hand has a small incidence angle dependency and a low sensitivity to the signal-to-noise ratio (SNR).

We observe that fully polarimetric C- and L-band data are complementary to one another and that through their slightly different dependencies on season and sea ice types, a combination of the two frequencies can aid improved sea ice classification. The availability of a high spatial and temporal resolution dataset combined with in-situ information offered during the MOSAiC expedition ensures that seasonal changes can be fully explored.

References
Johansson A.M., C. Brekke, G. Spreen, J. King, 2018, X-, C-, and L-band SAR signatures of newly formed sea ice in Arctic leads during winter and spring, Remote Sensing of Environment, 204: 162-180

Johansson A.M, King J.A., Doulgeris A.P., Gerland S., Singha S., Spreen G., Busche T, 2017, Combined observations of Arctic sea ice with near-coincident co-located X-band, C-band and L-band SAR satellite remote sensing and helicopter-borne measurements, JGR-Oceans, 122: 669-691

M. S. Mahmud, T. Geldsetzer, S. E. L. Howell, J. J. Yackel, V. Nandan and R. K. Scharien, 2018, Incidence Angle Dependence of HH-Polarized C- and L-Band Wintertime Backscatter Over Arctic Sea Ice, IEEE Transactions on Geoscience and Remote Sensing, 56(11): 6686-6698

Strong winds induced by typhoons and hurricanes cause disasters and have a great impact on social activities, therefore there is an increasing demand for their monitoring and prediction. A Synthetic Aperture Radar (SAR) is only satellite sensor capable of measuring sea surface winds with high spatial resolution O (100 m). For wind speed detection by Japanese L-band SAR named the Phased Array type L-band Synthetic Aperture Rader-2 (PALSAR-2) and use for operational weather forecasting under typhoon conditions, Japan Aerospace Exploration Agency (JAXA)-Meteorological Research Institute (MRI) joint research has launched. The purpose of the research is to verify the effect of the data assimilation of the SAR-retrieved winds on typhoon forecasting. Typhoons/hurricanes observations were being carried out under the joint research by programming the PALSAR-2 observations based on the predicted course of typhoons and hurricanes. So far, simultaneous observations with the National Oceanic and Atmospheric Administration (NOAA)'s airborne Stepped Frequency Microwave Radiometer (SFMR) have been made for five cases of hurricanes. Based on these data, estimating the wind structure of the hurricane by PALSAR-2 was developed.
The 3 km average PALSAR-2 normalized radar cross section (σ0) and the incidence angle were collocated with the SFMR-measured ocean surface wind speed and rain rate. It was confirmed that the incidence angle dependence was small for the cross-polarized (HV) σ0, so we developed a model function for the strong winds for the HV polarization. In order to investigate the dependency of σ0 on wind speed and incidence angle, the match-ups were classified into “bins” of 2 m/s wind speed and 5° incidence angle. Any data of which deviation exceeded 2σ in each bin were excluded.
A relationship between the PALSAR-2 HV σ0 and ocean surface wind speeds measured by SFMR showed that σ⁰ increased with respect to the wind speed up to about 55 m/s. Based on the method proposed by Hwang et al. (2015), a geophysical model function (GMF) was constructed as a function of wind speed and incidence angle. The wind speed was then inversely estimated from the matchup data (HV σ⁰) and compared with the wind speed of SFMR. Bias and RMSE are -0.2 m/s and 4.1 m/s, respectively. It indicates that the wind speed can be detected up to about 50 m/s or more without depending on the incidence angle.
The derived GMF was applied to the PALSAR-2 HV image of Hurricane Laura to calculate the ocean surface wind speed, and the comparison was performed along the SFMR observation tracks. Although there are some biased differences, fluctuation trends including maximum wind speed of about 60 m/s and sudden changes in wind speed near the eyewall are captured. The derived wind speed structure of the hurricane was compared with the best track data. Omnidirectional surface wind profiles as a function of distance from the hurricane center for the four geographical quadrants (NW, SW, SE, and NE) were calculated from the PALSAR-2-derived wind speed and compared with wind speed radii at three wind speed levels (34 Knot, 50 Knot, 64 Knot) obtained from the best track data. Wind speed radius is smaller in NW and SW than in NE and SE, which indicates the same spatial asymmetry structure as the best track. In addition, the absolute value of the wind speed radius and the decreasing tendency with respect to the distance are approximately the same.
A comparison of hurricane sea surface winds calculated from L-band PALSAR-2 and C-band Sentinel-1 was performed. The Sentinel-1 wind product was obtained from the Earth Observation Data Access (EODA) (https://eoda.cls.fr/client/oceano/) operated by Collecte Localisation Satellites (CLS). Among the typhoons and hurricanes observed by PALSAR-2, there were two cases where wind speed calculation was also carried out by Sentinel-1.
The first case is Hurricane Douglas, observed by Sentinel-1 at 3:59 on July 25, 2020 (UTC), and by PALSAR-2 at 9:41 on July 26, approx. 1 day and 6 hours later. According to the best track data, the hurricane has weakened during this period, and the sustained maximum wind speed has dropped from about 49 m/s to about 40 m/s. On the other hand, the maximum wind speeds by SAR were 52.8 m/s and 42.9 m/s, respectively, which is almost consistent with the best track data in terms of the decreasing tendency and width.
Another example is Hurricane Laura, which was observed by PALSAR-2 at 17:49 on August 26, 2020, and approx. 6 hours later by Sentinel-1. According to the best track data, the hurricane has been strengthened from about 60 m/s to 69 m/s in the 6 hours. The maximum wind speed of SAR is 65 m/s and 76 m/s, which tend to be strengthened in the same way as the best track data although absolute values are large.
It was confirmed that the L-band HV σ⁰ has a relationship with wind speed up to about 55 m/s in the data used in the present study and the wind speed can be estimated. On the other hand, the stronger the wind, the lower the increasing rate of σ0 with respect to the wind speed, so the radiometric accuracy of the SAR product has a strong impact on the wind speed estimation especially under the extreme wind condition. The comparison with C-band's Sentinel-1 product showed that increasing observation opportunities could enable detailed detection of hurricane temporal evolution at high spatial resolution.
As a next step, it is expected that the effect of these data on typhoon forecasting will be verified through data assimilation experiments. In addition, it is necessary to improve the detection accuracy by accumulating data and clarify the characteristics due to the difference in bands such as the maximum detectable wind speed and the rain contamination.

The complementarity between C- and L-band Synthetic Aperture Radar (SAR) images for the separation of sea ice types and identification of ice structures such as ridges or ice floe edges was first systematically analyzed when the first airborne multi-frequency data over sea ice were acquired in the late 80s. Since then, differences in the scattering characteristics and advantages of each band in ice classification and ice parameter retrieval have been analyzed and presented in various studies. Also, different supervised and unsupervised classification methods were applied to combinations of C- and L-band multi-polarization images, which showed improvements in classification accuracy. However, in contrast to data from different C-band satellite missions, L-band images were not available for operational ice mapping on a regular basis, which means that the gain of adding L-band to operational interpretation of ice conditions has not been evaluated in detail yet. For the identification of icebergs in open water and in sea ice, comparisons of C- and L-band images were lacking.
In a recent project of the European Space Agency (ESA) supported by the Japan Aerospace Exploration Agency (JAXA), the synergies between C- and L-band SAR missions for the retrieval of ice conditions and iceberg occurrences have been explored. The project aims to better define the benefits of future SAR missions working together at C- and L-band e.g. as part of a multi-agency international constellation of radar missions in the post-2026 time frame. The project team involves researchers from different universities and research institutes; and analysts from operational ice services in Canada (CIS), Denmark (DMI), and Norway (Met.No.). Input is also provided by the International Ice Patrol (IIP) and by a task team of the International Ice Charting Working Group (IICWG). The work comprises a literature study (e.g. [1]), and comprehensive analyses of Sentinel-1 and PALSAR-2 images acquired over different test sites in the Arctic: Labrador Sea, Baffin Bay, Lincoln Sea, Fram Strait, Belgica Bank and the Cape Farewell region. All-in-all, more than 1000 PALSAR-2 images have been acquired in WBD and FBD mode of which many were interpreted and analysed as stand-alone. About 200 images have a sufficient spatial overlap with and a sufficiently short time difference in acquisition to Sentinel-1 images at EWS or IWS mode over Lincoln Sea, Fram Strait, and Belgica Bank. For the Labrador Sea and Baffin Bay, 59 PALSAR-2 WBD images could be compared to Radarsat-2 SCWA images. In our presentation, we give an overview of the results achieved during project work. It has to be noted that pros and cons determined for each band also depend on the respective image properties (spatial resolution, number of looks, noise level, local incidence angle). The experts from the operational centres found that L-band is superior for earlier detection of fractures and fast ice breakup, for easier FY/MY discrimination during the melt season, for recognizing more structures in the ice, and for better discrimination of ridges. The latter, however, requires Stripmap mode but is not possible at the coarser ScanSAR mode. Because of the very low backscattering level of young thin ice at L-band it can be better distinguished from thicker ice. The weaknesses of L-band are the low separability of thin ice types relative to one another and relative to open water. The latter affects determination of ice concentration. At L-band, multi-year ice floes appear less prominent relative to first-year ice under freezing conditions. L-band is less sensitive to wind / sea state compared to C- or X-band which is, e.g., important for the Cape Farewell and the Labrador Sea region. Specifically for icebergs located in open water, it was observed that the detection rate depends on sea state and is decreased in melting conditions. L-band seems to be better at detecting icebergs in rough seas than C-band. Icebergs inside sea ice are easier to identify in L-band HV-images than in Sentinel-1 HV-images. Another topic in the project was to test classification and detection algorithms which also requires to align PALSAR-2 and Sentinel-1 images corresponding to each other but acquired with a temporal gap. This has been mainly carried out by the university partners. For moderately dynamic ice conditions with distinct ice structures, the alignment of C- and L-band image pairs was possible for time separation of hours up to even a few days in a very stable ice cover, but not always over the full overlap area between the C- and L-band image. For some ice regimes (e.g. South Greenland, Labrador Sea) and during the melting season, an alignment is extremely difficult or not possible. Experiments with supervised classification in a decision tree reveal for which specific ice conditions the additional use of L-band is beneficial. Investigations of icebergs captured in fast ice show that they appear brighter relative to the background (the ice) in L-band than in C-band imagery, and that icebergs and sea ice are in general difficult to distinguish at C-band. At L-band internal reflections in the interior of an iceberg increase the backscattering and may cause ghost reflections next to icebergs. The conclusion is that L-band SAR imagery clearly provides an advantage for ice charting and iceberg detection. Requirements are regular acquisitions with a sufficient coverage of the sea ice regions monitored by the operational centres and timely availability of the images. Acquisitions in a tandem mode, i.e., with a C- and L-band SAR satellite pair collecting their data with the smallest possible time gap between them, would benefit in particular automated dual-frequency classification and detection.
The following members of the project team contributed to the investigations that will be reported: Melanie Lacelle, Tom Zagon, and Benjamin Deschamps from CIS, Keld Qvistgaard from DMI, Nick Hughes from Met.No., Mike Hicks from IIP, Leif Eriksson, Anders Hildeman from Chalmers University of Technology, Denis Demchev from Nansen Environmental and Remote Sensing Center, and Johannes Lohse, Laust Færch, and Anthony P. Doulgeris from the Arctic University of Norway / Tromsø.

[1] Dierking, W., Synergistic Use of L- and C-Band SAR Satellites for Sea Ice Monitoring, Proceedings 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, DOI: 10.1109/IGARSS47720.2021.9554359

In this paper, we present recent results of volcano monitoring using the Advanced Land Observing Satellite-2 (ALOS-2) and Sentinel-1 satellites. ALOS-2 carries an L-band synthetic aperture radar (SAR) named PALSAR-2 and enables quick disaster response with high resolution (1 by 3 meters by the Spotlight mode and 3 to 10 meters by the Stripmap mode). Sentinel-1A/B satellites carry C-band SAR and focus on periodic observations (every 6 or 12 days) with medium spatial resolution (20 meters by the Interferometric Wide mode).

Mount Nyiragongo located in the eastern area of the Democratic Republic of the Congo erupted in May 2021. During the eruption, we estimated quasi-vertical displacements and quasi-east-west displacements around the volcano by multi-angle interferometric SAR (InSAR) analysis using the data from ascending and descending orbits. The results from both ALOS-2 and Sentinel-1 data implied the occurrence of dyke intrusion by underground magma, but the two results had different characteristics. Sentinel-1 (C-band) showed more fringes corresponding to the shorter wavelength and higher sensitivity for small displacements. ALOS-2 (L-band) showed higher coherence in largely displaced and highly vegetated areas, as the longer wavelength has higher penetration capability.

Kilauea Volcano in Big Island, Hawaii began erupting in May 2018 and caused fissures in residential areas. We performed InSAR, multiple aperture interferometry (MAI), and polarimetric analysis using a series of ALOS-2 data. The displacements derived from InSAR and MAI of Stripmap and ScanSAR data revealed the presence of dyke intrusion in the East rift zone and subsidence in Kilauea Caldera. Using polarimetric images by HH and HV polarization data, we also created a map of lava flow in Leilani Estates. Moreover, a series of Spotlight mode data captured the detailed process of the collapse of Halemaumau Crater in Kilauea Caldera.

In conclusion, multi-frequency (L-/C-bands), multi-angle (ascending/descending), multi-mode, and multi-polarimetric SAR data provides us with various information on tectonic and surface changes to better understand volcanic activities.

Land use change and land management accounts for approximately 14% of total global anthropogenic CO2 emissions, predominantly due to forest fluxes, but with an uncertainty of around ±50%, it is the most uncertain term in the carbon budget1,2. The Land Use Land Use Change (LULUCF) sector is unique because it not only includes anthropogenic Greenhouse Gas (GHG) emissions but also considers anthropogenic sinks such as removals from afforestation/reforestation and, to different extents, forest conservation on lands considered to be “managed”. The numerous approaches to estimate LULUCF each have their own scope, intentional use, input datasets and methods. Recent analysis found a difference of ~5.5GtCO2yr-1 between national GHG inventories (NGHGI) and global models3. This difference can largely be explained by the extent to which each approach considered the forest sink to be “managed” and thus anthropogenic3.
It has been hoped that Earth Observation-based estimates might reduce the uncertainty in the LULUCF flux. They can support climate policy by providing local, spatial detail to aid countries’ reporting and verification of their NGHGI as well as potentially providing a benchmark to evaluate the land sink from global models in a globally consistent manner. However, as noted in the (draft) IPCC 6th Assessment Report, there are large differences between CO2 flux estimates based on activity data of forest cover loss and gain from global Earth Observation data4 and other methods1-3. The comparison in the draft IPCC 6th Assessment Report showed that, globally, Earth Observation-based estimates4 find a considerable global forest sink of -6.7 GtCO2 yr-1 during the period 2001 – 2019 in non-intact (i.e. managed) forests. In contrast, the global LULUCF flux is estimated to be a small net sink of -1.1 ±1.0 GtCO2 yr-1 in NGHGIs3 and a considerable net source of 5.7 ±2.6 GtCO2 yr-1 in global bookkeeping models2 for the period 2010 - 2019. This Earth Observation result therefore increases uncertainty in the LULUCF net flux, as noted in the (draft) IPCC 6th Assessment Report. Understanding the reasons for the differences is crucial if Earth Observation is to be used in the context of supporting countries inventories and national/global policy.
Here we conduct a detailed comparison of the methods and datasets used to produce inventories and the Earth Observation-based estimates of forest-relaxed carbon fluxes2. We aim to (i) quantify the differences between estimates of forest-related GHG fluxes based on Earth Observation data and NGHGI; (ii) provide an understanding of the potential reasons for the differences on a (sub)country-level scale; and (iii) provide recommendations for policy makers and inventory compilers on the best approach to using global Earth Observation within reporting and verification.
We use different land cover masks to compare fluxes from the Earth Observation study4 across equivalent areas of forest that could be considered managed according to national inventories. Results show the Earth Observation based estimates of the non-intact forest flux to be a large net carbon sink, whilst all other methods suggested the LULUCF sector to be a small net source on a global scale, with similar findings regionally. We have carried out an initial detailed analysis focusing on Brazil and the Amazonian biome where there is in-country remote sensing data available at high spatial and temporal resolution, which are used during the compilation of the NGHGI. We have identified several reasons for differences and discuss implications for improving methods as well as comparability between Earth Observation approaches and inventories.

References:
1 Jia et al., Chapter 2 Land-Climate interactions. In: Climate Change and Land: an IIPCC special report on climate change, desertification, land degradation, sustainable land management, food security and greenhouse gas fluxes in terrestrial ecosystems. Editors: Shukla et al. 2019.
2 Friedlingstein et al. Global Carbon Budget 2021. Earth Syst. Data Discuss (preprint), https://doi.org/10.5194/essd-2021-386, in review, 2021.
3 Grassi, G. et al. Critical Adjustment of land mitigation pathways for assessing countries’ climate progress. Nature Climate Change. 11, 425-434 (2021). https://doi.org/10.1038/s41558-021-01033-6
4 Harris, N.L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Chang. 11, 234–240 (2021). https://doi.org/10.1038/s41558-020-00976-6

Biomass, and especially forest biomass, plays a crucial role in sequestering and storing carbon and is a key component of both national Greenhouse Gas (GHG) inventories and the global carbon budget. Accordingly, mapping aboveground biomass is a priority of space agencies such as NASA, ESA and JAXA and is highlighted by the several new and upcoming satellite missions, including GEDI, ICESat-2, BIOMASS, ALOS-2, ALOS-4 and NISAR. Using satellite biomass products seems a natural progression for country Parties when preparing their reports of GHG emissions and removals from the forest sector to the United Nations Framework Convention on Climate Change (UNFCCC). In particular considering the new 2019 Refinement to the 2006 IPCC Guidelines that include generic guidance on the use of biomass density maps for GHG inventories. However, the availability of multiple satellite products can be confusing to policy users and national technical teams. The Earth Observation (EO) global biomass monitoring community recognizes that widespread uptake of these biomass products requires their differences to be addressed, their accuracies known, and associated metadata provided to derive estimates compliant with the IPCC guidelines on bias and uncertainty. Furthermore, products need to be flexible to adhere to the land representation according to national definitions. A dedicated international effort coordinated through the Committee of Earth Observing Satellites (CEOS) and the Global Forest Observation Initiative (GFOI) R&D component is addressing some of the existing limitations that jeopardize the successful uptake of biomass products by the UNFCCC, including by national GHG inventory teams. The objectives of the CEOS Agriculture, Forestry and Other Land Use (AFOLU) group, and specifically its Biomass Harmonization Team, are to develop i) “best available” harmonized global forest biomass estimates from the next generation of biomass maps as input to the Global Stocktake (abstract by Duncanson et al.) and ii) examples of the practical implementation of the 2019 Refinement to the 2006 IPCC Guidelines to enhance the uptake of these maps by countries in their reporting to the UNFCCC.

Here we demonstrate these two objectives are only successfully achieved if the disconnect between producers and users is addressed and the interface for collaboration between EO global biomass monitoring, GHG inventory, and National Forest Inventory (NFI) experts is created. For the first objective, NFI plot data together with national expertise are required to understand and quantify the accuracy and usefulness of the maps. To achieve both objectives, we show how a collaborative interface was created between CEOS agencies and national teams from several countries (e.g., Japan, Paraguay, Peru, Solomon Islands, Wales UK) and emphasise the preparatory work required for an effective dialogue between the different groups. First, it is necessary to recognize that only individual countries, given their national circumstances, can determine if satellite-based data and derived products developed over large areas are suitable for use in national GHG inventories. To determine needs and requirements regarding the potential use of space-based biomass data we analyzed the information available in national submissions to the UNFCCC and in the reports from the technical review process, in particular in the section covering areas for future technical improvement. The second step is to understand the IPCC Guidelines, which IPCC variables we need biomass information for, what methods are used and assumptions made by national teams, and reflect on possible procedures to derive those estimates with associated bias and uncertainty according to the Guidelines. Finally, a mapping exercise between variables, areas for improvement of the estimates for those variables, and the characteristics of the available products support the discussions on how the data needs to be handled and presented following IPCC guidance and principles. We conclude with a demonstration on how this strategy and interface between CEOS agencies and national technical teams led by SilvaCarbon provided a few first examples of the successful uptake of maps in reporting to the UNFCCC and of the practical implementation of the 2019 IPCC Refinement.

Numerous global and regional biomass maps have been produced in recent years by multiple international programs using a combination of Earth observation, ground, and airborne data. Among these efforts, ESA’s Climate Change Initiative (CCI) Biomass project, has released global biomass maps for four epochs. Recent and upcoming space missions even have biomass maps as dedicated target products. Examples include the ESA Biomass mission (P-band SAR) and the NASA Global Ecosystem Dynamics Investigation (GEDI) mission (waveform lidar). The potential usefulness of such maps for greenhouse gas (GHG) reporting has been acknowledged by the Intergovernmental Panel on Climate Change (IPCC) in the form of new guidance on their use for national GHG inventories.

A common denominator of all the maps published to date, possibly with the exception of some maps produced by the GEDI mission, is that they lack the metadata required to estimate the uncertainty of map-based estimates of biomass or carbon stock for arbitrary geographical regions. As a consequence, estimates obtained directly from these maps fail to comply with the IPCC good practice guidelines, a serious shortcoming.

Biomass maps may be used in their existing forms to enhance estimates for any region of interest only if there already exists a ground-based inventory, such as a national forest inventory program, that has adopted a probabilistic field sampling design. In this case, the value of the maps as auxiliary data can be realized in the form of increased precision of the estimates, which can be substantial. However, many countries finding global biomass maps attractive in their efforts to estimate biomass, do not have extensive ground sampling programs.

In this study, being part of the ESA “Sentinel-1 for Science Amazonas” project, we demonstrate our inability to profit from an existing global biomass map – the ESA CCI 2017 biomass map – when estimating carbon stock across a 4.1 million square kilometer study region in the Brazilian Amazon Biome. To validate the map, we independently estimated mean carbon stock per unit area using an existing random sample of 501 airborne lidar transects with an approximate length of 12 km each collected by the Brazilian EBA project (“Estimativa de biomassa na Amazôni”), a coincident sample of 224 field plots, and hybrid estimators. A standard error and a confidence interval were also estimated for the mean estimate. Following the terminology of the Global Forest Observation Initiative’s “Methods and Guidance Document”, we considered this a “greater quality” estimate and, therefore, useful for validation purposes. We compared the lidar-based estimate to a mean estimate obtained from the CCI 2017 biomass map via “pixel counting”, i.e., estimating the mean biomass across all map units and applying a conversion factor to obtain a mean carbon stock estimate. Despite a substantial difference between the lidar-based and the map-based estimates, we were unable to conclude that this difference was statistically significant because the map-based estimate is just a point estimate without any accompanying information on the uncertainty.

Further, we discuss the nature of the metadata map producers must deliver with their map products to facilitate rigorous statistical inference based solely on the map products. We also illustrate the limitations of the uncertainty information that some of the current maps provide and argue that this information is insufficient to such a degree that uncertainty will be greatly underestimated.

National forest inventories (NFI) provide forest-related biomass and carbon information for country forest monitoring and greenhouse gas (GHG) accounting systems. While many tropical countries are actively working on their NFIs, many still struggle to establish a complete national inventory or to guarantee frequent updates of their NFI owing to budget constraints, inaccessibility of areas, institutional circumstances, developing capacities, internal conflicts, lack of initial information on forest resources and the like. This has limited the usefulness of their national aboveground biomass (AGB) estimates and ultimately hinders the accuracy, completeness and consistency of reporting country greenhouse gas (GHG) emissions and removals under the international climate frameworks as well as the formulation of domestic mitigation targets.

At the same time there has been significant progress in developing large-area biomass density maps to characterize the distribution of forest carbon stocks around the globe. These efforts are further developed through dedicated space-based biomass missions that increasingly provide open access and continuous coarse-scale biomass information. The extent to which global information on space-based estimates of aboveground biomass (AGB) can support national forest biomass monitoring and GHG accounting is still under investigation. In the presentation we cover the approaches and assess whether the use of a global biomass map as a source of auxiliary information can produce a gain in precision of sub-national AGB estimates. For that purpose, we made use of model-assisted estimators that best accommodated the country NFI sampling design, and we explored hybrid inferential techniques to account for additional sources of uncertainty associated with the integration of the remote sensing products (in our case the ESA CCI biomass map) and the NFI plot data. We will present results from two case studies with rather different NFI designs and forest characteristics: Peru and Tanzania.

For the case of Peruvian Amazonia, we found that the CCI biomass map tends to overestimate AGB across the Peruvian Amazonia. The most striking result was that, after calibrating the map using NFI data, it substantially increased the precision of our model-assisted stratum-wise AGB estimates by as much as 150% at the strata-level, and 90% at the Amazon level. even though the initial relationship between the plot-based biomass and space-based estimates of biomass varied for different strata and was not that strong for some strata. Our results show that more precise AGB estimates can be achieved by integrating NFI and space-based biomass data (i.e. through a locally calibrated remote sensing product) in the tropics. In the hybrid inferential analysis, we found that the very different spatial support for the NFI plots compared to the remote sensing-based units contributed 86% of the total uncertainty in the map-NFI integration. The uncertainties concerning the reference data at plot level owing to measurement error and allometric model prediction uncertainty contributed 13%. When accounting for these uncertainties, the precision of our AGB estimates was still increased by as much as 90% and 60% at the stratum and Amazon levels, respectively. Our results show that Peru could benefit from the application of global biomass maps that contribute to more precise and complete AGB estimates for GHG monitoring and reporting, particularly while the NFIs is still incomplete.

The analysis for the case of Tanzania follows a similar framework but the estimation procedures have been modified because of the different NFI plot and sampling designs. The quantitative results are not final at the time of abstract submission but will be included in the presentation to compare both country conditions.

Terrestrial CO2 fluxes from land use, land use change, and forestry (LULUCF) accounted for about 12% of total anthropogenic CO2 emissions in the last 20 years, while land simultaneously provided a natural sink for about 29% of all CO2 emissions (Friedlingstein et al., 2021). Comparisons of anthropogenic LULUCF emissions in global models and in country reports to the United Nation Framework Convention on Climate Change (UNFCCC) revealed a substantial gap between both estimates, globally amounting to about 5.5 Gt CO2/year (Grassi et al., 2021). This gap was mainly attributed to discrepancies in areas considered as managed land in models and country reports, and to the (partial) inclusion of natural CO2 fluxes (e.g. from carbon removals by CO2 fertilization, forest fires, insect outbreaks) on managed land in many of the country reports (Grassi et al., 2021).
Building on this proposed explanation, we provide a disaggregation of country-reported CO2 emissions from LULUCF into contributions from anthropogenic and natural CO2 fluxes on country level, considering eight countries with high emissions from LULUCF. We focus on natural fluxes in managed forests since the majority of natural CO2 removals occurs in forested areas. For each country we use process-based models to estimate the natural CO2 fluxes on managed forests (which we identify through a mask of non-intact forest due to lack of information on the spatial distribution of managed forests in the country reports) and add them to the model-based LULUCF emission estimates. This approach is in line with the methodologies used by almost all investigated countries to estimate CO2 fluxes from LULUCF, which imply that natural fluxes on managed land are included in their CO2 flux estimates.
In the majority of the eight countries investigated, including natural fluxes on managed forests substantially reduces the gap between model estimates and country reports of LULUCF emissions, highlighting that the methodology suggested by Grassi et al. (2021) provides a feasible approach to make estimates more compatible also at the country level. Countries include about half of the domestic natural CO2 fluxes in their LULUCF emission reports, which shifts the reported CO2 fluxes downward, i.e., towards lower emissions or, accordingly, towards larger sinks. Large gaps present in Russia and the USA can be closed almost completely by adding natural fluxes on managed forests to model-based LULUCF emissions, revealing that the CO2 sinks from LULUCF reported by these countries are predominantly due to natural fluxes on managed forests. Also in the EU, Indonesia and China, which is the country that has the largest gap and reports the largest CO2 sink of the investigated countries, the gap is considerably reduced by including natural CO2 fluxes on managed forests. These results highlight that the methodological discrepancies between country reports and model estimates of LULUCF emissions are primarily due to accounting definitions and need to be reconsidered in a proper assessment of the country contributions to the global climate mitigation targets, as planned in the Global Stocktake in 2023.
While the presented approach provides an important step forward in bringing together model estimates and country reports, it does not achieve a complete closing of the gap. For some countries, estimates from models and country reports still differ substantially, such as for China, where differences might be due to overoptimistic estimates of the actual effects of afforestation on CO2 fluxes in the country report, underestimations of the afforested area in the input datasets used by models to calculate LULULCF fluxes, and/or limitations in the capability of models to fully integrate the large-scale afforestation in China. There are several potential reasons for the remaining gaps, including incomplete reporting by countries, uncertainties in historical land use dynamics, and model limitations. Moreover, most countries report the areas considered as managed without explicit information on their location, which prevents a precise spatial identification necessary for correctly quantifying natural fluxes on managed forests in models. For some of these factors, remote-sensing products might provide independent and spatially explicit estimates through satellite-derived classifications of land use and land cover change, quantifications of changes in biomass, or identification of managed forest areas. Additionally, the near real-time availability of satellite data might be useful for providing a temporal extension of country reports, which are usually published with a lag of several years. Remote-sensing products might thus constitute an additional, strong pillar in establishing a sound and viable methodology for a translation between model estimates and country reports of anthropogenic CO2 emissions from LULUCF.


References:

Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J., Le Quéré, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., . . . Zeng, J. (2021). Global Carbon Budget 2021. Earth Syst. Sci. Data Discuss., 2021, 1-191. https://doi.org/10.5194/essd-2021-386

Grassi, G., Stehfest, E., Rogelj, J., van Vuuren, D., Cescatti, A., House, J., Nabuurs, G.-J., Rossi, S., Alkama, R., Viñas, R. A., Calvin, K., Ceccherini, G., Federici, S., Fujimori, S., Gusti, M., Hasegawa, T., Havlik, P., Humpenöder, F., Korosuo, A., . . . Popp, A. (2021). Critical adjustment of land mitigation pathways for assessing countries’ climate progress. Nature Climate Change, 11(5), 425-434. https://doi.org/10.1038/s41558-021-01033-6

The need for accurate information to characterize the dynamics of forest cover at the tropical scale is widely recognized particularly to assess carbon losses from deforestation and forest degradation (Achard & House 2015). In particular, the contribution of the degradation is a key element for REDD+ activities and is still missing in most national reports due to lack of reliable information at such scale. The main scientific gaps in the estimation of carbon losses at the pantropical scale from Earth Observation data remain on (i) integrating temporal dynamics of various types of forest disturbances; (ii) assessing and combining uncertainties from ‘activity data’ (deforestation and degradation) and from emission factors (in particular in relation to biomass maps and degradation processes).

To address these gaps, we use a wall to wall tropical moist forest change product (Vancutsem et al. 2021, hereafter called TMF) at 30 m resolution, which depicts both deforestation and degradation over the past 3 decades and allow a better understanding of the interlinkage between the two processes. We combine the TMF annual changes with a pan-tropical map of aboveground biomass density (AGB) at 30 m resolution valid for year 2000 (Harris et al. 2021) to quantify the annual losses in above-ground carbon stock associated with degradation and deforestation in tropical moist forests over the period 2001-2020. The carbon loss due to direct deforestation is accounted as full carbon loss while for degradation we consider partial loss of the initial carbon stocks with a range from 20 to 75% depending on the intensity of degradation processes (Andrade et al. 2017). We also account for induced carbon losses from forest fragmentation and edge effects as an indirect consequence of deforestation (Silva Junior et al., 2020) by considering that forest areas in a 120m edge lose 50% of their biomass linearly over a 50 years period (Brinck et al. 2017). Carbon losses due to deforestation happening after a prior degradation event or edge effect are accounted as the carbon stock remaining after degradation or fragmentation effect. Finally we assess the sensitivity of our approach by replacing the AGB map of year 2000 from Harris et al (2021) with the ESA CCI Biomass map for year 2010 at 100m resolution (Santoro et al. 2021).

This approach allows to produce estimates of annual loss in above-ground carbon stock associated with deforestation and degradation in tropical moist forests. Our initial results show that deforestation and forest degradation led to losses of 580 TgC / year and 365 TgC / year respectively for the period 2011-2020 when using the Harris et al. map, or 395 TgC / year and 267 TgC / year respectively when using the ESA CCI map. These estimates show a lower contribution of degradation to the total carbon loss than recently reported from a coarser resolution study (Baccini et al 2017), i.e. 38% (Harris et al.) or 44% (ESA CCI) from our study versus 69% for period 2003-2014 from this previous study. Further analysis will be performed to better assess the sensitivity of the estimates to the AGB maps and scenarios used for the degradation and fragmentation processes.

We intend to quantify the uncertainties of area estimates from the TMF product following GFOI guidelines (GFOI, 2020). To carry out this accuracy assessment, a stratified random sampling scheme is used to create a reference dataset of 6000 sample plots (with Landsat pixel size). The sample is optimized to target omission errors of disturbances in stable forest areas (by using a buffer zone around changed strata) and commission errors in disturbed forest areas. For each sample plot, the most recent high resolution image that is available from Google Earth platform, or from Planet and Sentinel 2 databases is visually interpreted as well as the time series of available Landsat images from 1990 to 2020. The reference sample will be used to produce unbiased estimates of activity data with uncertainty range and related estimates of Carbon losses through the combination of the sample with biomass maps.



References

Achard F, House JI (2015) Reporting carbon losses from tropical deforestation with Pantropical biomass maps. Environ. Res. Lett. 10, 101002

de Andrade RB et al. (2017) Scenarios in tropical forest degradation: carbon stock trajectories for REDD+. Carbon Balance Manage 12, 6.

Baccini A et al. (2017) Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234

Brinck K et al. (2017) High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat Commun 8, 14855.

Hansen MC et al (2013) High-resolution global maps of 21st-century forest cover change. Science 342, 850–853

GFOI (2020) Methods and Guidance from the Global Forest Observations Initiative Edition 3.0.

Harris NL et al (2021) Global maps of twenty-first century forest carbon fluxes. Nature Climate Change 11, 234–40

Santoro, M.; Cartus, O. (2021): ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2. Centre for Environmental Data Analysis, 17 March 2021. doi:10.5285/84403d09cef3485883158f4df2989b0c.

Silva Junior CH et al (2020) Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Science Advances.

Vancutsem C et al (2021) Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Science Advances.

The International Charter ‘Space and Major Disasters’ (“Charter”) has been founded in 1999 with three initially participating space agencies: ESA, CNES and CSA (from Europe, France, and Canada). It started its operations in November 2000 with the goal to provide quick access to satellite-based information in cases of major disasters, free of any cost for the user. In 21 years, the Charter has been activated more than 730 times worldwide and covered disasters in 129 countries (as of Nov 2021). The Charter is now composed of 17 agencies worldwide (ABAE, CNES, CNSA, CONAE, CSA, DLR, ESA, EUMETSAT, ISRO, INPE, JAXA, KARI, NOAA, ROSCOSMOS, USGS, UAESA, UKSA).

The Charter covers a large variety of disasters, both natural and man-made. In about 50% of the cases Charter activations are for flood events, including tsunamis. In addition, tropical storms, earthquakes, fires, landslides, volcanic eruptions, ice jams, oil spills, and large industrial accidents are being covered. The Charter is functioning on a best-effort basis.

The Charter illustrates the fact that Earth-observing satellites are able to deliver key information that brings benefit to the definition, planning, implementation and monitoring of disaster relief operations when human life and infrastructure are at stake. Its service is user-driven, i.e. the Charter becomes active after being triggered by an “Authorized User” (AU). Typically, Charter AUs are national disaster management agencies or emergency operations units. Besides these AUs, the Charter can also be triggered by Cooperating Bodies from the UN family in order to make Charter support available for user organisations within the UN, the Red Cross/Red Crescent, as well as countries that do not yet have a Charter AU.

The “Universal Access” policy allows national disaster management agencies, which do not yet have Charter access, to register with the Charter and become an AU after a training. It is also encouraged that countries struck by a disaster bring in their own EO expertise in case of an activation, i.e. Charter activations can be managed by and/or maps generated by in-country capacities. The Charter support is free of charge and based on international collaboration among the Charter member agencies and their humanitarian motivation.

This paper will give an overview of the Charter’s work with a focus on user aspects. Various examples of information products resulting from Charter activations will be shown, with a special focus on products based on synthetic aperture radar (SAR) satellite data.

The idea for the International Charter ‘Space and Major Disasters’ came into being at the third UN space conference UNISPACE III held in Vienna in July 1999. In the face of increasing destruction and damage to life and property caused by natural disasters and conscious of the benefits that space technologies can bring to rescue and relief efforts, the European Space Agency (ESA) and the Centre national d'études spatiales (CNES) set out to establish the text of the Charter, which they themselves signed on 20 June 2000, while inviting other space agencies to do the same. The Canadian Space Agency (CSA) was the first to come onboard and sign the Charter on 19 October 2000. These three founding space agencies then went on to establish the architecture essential for implementing the Charter.

As one of the three founding members of the Charter, CSA will provide in this paper an overview of the Charter’s history, the role played by CSA in its development, and highlight the notable Charter milestones in its evolution. Examples of Charter images that have provided valuable assistance to disaster relief efforts will be presented. The benefits and strengths of the Charter from a Canadian perspective will be examined. The paper will conclude by discussing some of the Charter’s future challenges.

The Sendai Framework for Disaster Risk Reduction (SFDRR) provides an internationally agreed agenda for evidence-based policy with the overall aim to achieve progress in disaster risk reduction (DRR). Main components of the framework are four priorities for action and seven Global Targets to monitor progress in DRR based on thirty-eight indicators. However, the progress of monitoring the set targets is very often obstructed due to a lack of available, accessible, and validated data on disaster-related loss and damage, especially in developing countries. This weakens the accuracy, timeliness, and quality of the SFDRR monitoring process. In the case of floods, which account for the highest number of people affected by hazards, there is a strong need for innovative and appropriate tools for monitoring and reporting impacts, for which Earth Observation (EO) can provide solutions. Previous attempts to address this gap via a geospatial model approach could not be validated due to a lack of in-situ measured loss and damage reference data. The presented research here addresses this gap by developing a geospatial model approach that was validated against reference data provided by partner national institutes from the country of Ecuador. The methodology in this study aimed at combining EO-based information products with additional geospatial data to result in quantitative measures for indicator B-5a of the SFDRR and validate them against the reference data. A semi-automated derivation of flood event characteristics from a full year of Sentinel-1 synthetic aperture radar data was applied to three Ecuadorian focal provinces that best represented the ecological diversity of the country in order to assess flood hazard. An automated thresholding algorithm was applied to delineate flooded areas which yielded flood statistics when applied to Sentinel-1 data for an entire reference year. This assessment was complemented by census and agricultural in situ data to spatially model SFDRR indicator B-5a for the year 2017. The validation procedure involved various steps to produce different models that combined elements of flood exposure and vulnerability to be cross validated with the reference data. A statistical analysis was used to assess the agreement of the models with reference data and their ability to reproduce the reference data. The validation procedure resulted a geospatial model, which integrated also flood vulnerability and has high agreement with the reference data. However, the models were sensitive to different ecological regions.

This validated geospatial model approach, is - to the best of the authors’ knowledge - the first attempt to validate geospatially measured Sendai indicators against reference data. The derivation of open source information products was conducted in close collaboration with the National Service for Risk and Emergency Management of the Government of Ecuador, the Ecuadorian Ministry of Agriculture, the Ecuadorian National Institute for Statistics and Census, the Ecuadorian country office of the United Nations Development Program, and the United Nations Office for Disaster Risk Reduction. It is thereby assured that the development and validation of this methodology is in line with the Ecuadorian national and the international approach of implementing the SFDRR. The information products were shared with partner institutions through a “training-of-trainers” capacity building workshop which enabled the sustainable transfer of results and learning outcomes for Sendai Monitoring.

Consequently, this validated geospatial model approach provides an opportunity to support countries without information on disaster-related loss and damage in monitoring indicator B-5a of the SFDRR. Due to its retrospective ability to assess loss and damage, a baseline measure of the indicator can be derived as a reference for monitoring progress. This approach also successfully integrates characteristics of vulnerability into the UNDRR methodology to better capture the heterogeneous nature of flood impacts. Future research should seek the application and modification of the developed and validated model for additional Sendai indicators and targets, and predominantly explore solutions to overcome the sensitivity of the models for different ecological regions.

Satellite imagery has great potential for monitoring and understanding the behaviour of volcanoes, especially at the 45% of potentially active volcanoes without ground-based monitoring. Here, we present results from Committee on Earth Observation Satellites’ Volcano Demonstrator. Our primary goal is to increase the uptake of satellite imagery, and especially Interferometric Synthetic Aperture Radar (InSAR), for volcano research and monitoring. We aim to support the use of satellite data for disaster risk reduction by providing otherwise restricted civilian data to volcano observatories, and by supporting capacity building. The Volcano Demonstrator developed from a 4-year Pilot (2013-2017) to demonstrate the usefulness of satellite data for monitoring large numbers of volcanoes. This pilot focussed on Latin American volcanoes and detected unrest not observed by ground based networks, as well as feeding into volcanic observatory decisions about alert levels and sensor deployments. Satellite measurements of deformation contribute both our knowledge of sub-volcanic magmatic zones, and to volcano monitoring, especially when used in combination with satellite measurements of thermal anomalies and volcanic gases.

Since 2019, the Volcano Demonstrator has targeted volcanoes that present the greatest risks and potential for useful remote sensing measurements in Latin America, SE Asia and Africa. In some cases, we have provided a route for scientists at volcano observatories to access CosmoSkyMed, TerraSAR-X and Pleiades imagery, while in others we have contributed to data processing and interpretation. We have responded to unrest and eruptions at volcanoes including in St Vincent, Guatemala, Peru, Chile, Indonesia, DRC and the Canary Islands. Research based on CEOS demonstrator data has included novel retrievals of topographic change during eruptions (Fuego 2018, St Vincent 2021, Nevados de Chillán) and forensic analysis of recent eruptions with high spatial resolution imagery (Agung 2017, Anak Krakatoa 2018). In addition, we coordinate the acquisitions, and where possible, the analysis, of ‘baseline’ high resolution radar images at active volcanoes to complement open datasets such as Sentinel-1. From our experience in the CEOS pilot and demonstrator programs, we believe that integrating multiple SAR platforms is critical for volcano monitoring because it maximises temporal coverage and because some volcanic events can only be observed at specific radar wavelengths, geometries or repeat times. Integration of these observations with satellite measurements of gas, thermal anomalies and the measurements of ground-based monitoring networks are critical for interpretation of these data.
The CEOS volcano demonstrator links volcano observatories around the world with experts in satellite remote sensing and space agencies. Our long-term aim is to make this project sustainable and to demonstrate the necessity of inclusive, international coordination of satellite imagery acquisition for volcanology.

On August 14th, 2021, a 7.2 magnitude earthquake hit the southern peninsula of Haiti. Two days later, Grace, a tropical storm, struck the three departments of Nippes, South & Grand’Anse. These combined events, and all aftershocks caused many landslides, especially along the Enriquillo-Plantain Garden fault. Following these disasters, emergency management services have been triggered such as the International Charter Space and Major Disasters and the Copernicus Emergency Management Service Rapid Mapping.

The third CEOS Recovery Observatory demonstrator was activated on September 6th, 2021 on behalf of the European Union, United Nations Development Programme (UNDP), and the World Bank, in charge of the Post Disaster Needs Assessment (PDNA). The priority was to estimate the number of landslides and their location throughout the peninsula. Taking into account the size of the affected area, the difficulty in accessing this region, it was decided that the best way to obtain and provide this information was through satellite imagery. Earth Observation provides an overall and factual view of the situation.

Many contributors assembled to provide a rich package of information in a rush and best effort mode. Automatic landslide extraction layers realized by NASA and BGC Engineering Inc. were provided. ICube-SERTIT as RO liaison officer, but also as a value-added producer and expert in Earth Observation aggregated, validated, and completed these data over the whole southern peninsula. Landslides were extracted from optical satellite imagery. The International Charter “Space and Major Disasters” provided a Pléiades-HR image over the national Reserve of Macaya Park located in one of the most affected areas. In order to cover the whole peninsula, Sentinel-2 and Landsat-8 data were used.

The Haitian National Center of Geospatial Information (CNIGS) shared several reference datasets (land use / land cover maps, Digital Elevation Model…) which enabled the elaboration of statistical information concerning the landslides. Nearly 7.000 ha of landslides were mapped within the southern peninsula, especially affecting wood and bushy areas, in mountainous agro-ecological zones.

This landslide dataset and its related statistics helped the PDNA team to complete and precise their report. It represents also the principal input data to the second RO phase’s products, which ends at the end of February 2022. To support the reconstruction phase, RO will provide products aimed at user needs concerning land cover and the hydrological changes induced by these disasters. Furthermore, the computation of a soil erosion susceptibility index is planned to identify new risks in the areas that need to recover.

To address the needs of the Haitian community in the south-west of the country involved in recovery and rehabilitation after the impact of Hurricane Matthew in October 2016, the Committee on Earth Observation Satellites (CEOS) triggered the 4 year-long Recovery Observatory (RO) pilot project, led by the National Center for Geo-spatial Information (CNIGS) with technical support from the French National Centre for Space Studies (CNES) [www.recovery-observatory.org].
During the RO pilot, the Italian Space Agency (ASI) contributed with scientific research aiming to develop – jointly with the RO management and technical team from CNES and CNIGS – a workflow which encompassed: (i) tasking of satellite high resolution Synthetic Aperture Radar (SAR) data for regular observations of the priority areas defined by the Haitian users; (ii) image processing, also testing computing and algorithm resources that could ensure future sustainability; (iii) generation of value-added geohazard products providing information on terrain motion and change detection; and (iv) in-situ validation. In addition, a basic SAR image course was given to the URGEO master in Université d’État d’Haiti (UEH). The motivation to focus on SAR data (to complement more consolidated techniques based on optical satellite imagery) was provided by the Haitian partners’ expressed need to approach the domain of SAR remote sensing and interferometry (InSAR) that, on one side, bring well-known advantages for land applications and disaster risk reduction in tropical regions but, on the other, require computing facilities, training and capacity building to be feasibly used as sources of geospatial information.
The RO pilot was successfully completed at the beginning of 2021. A final workshop was held by involving all the space agencies, national champions and users who collaborated to demonstrate the benefits of better integrating information from satellite imagery and their derived products into the post-Matthew reconstruction and recovery process. That event provided the opportunity to reflect collectively on the lessons learnt and the legacy of the RO pilot experience.
The present paper aims to contribute to the key objectives of ESA Living Planet Symposium session D1.05 “International Collaboration to better understand risks using satellite EO (GEO, CEOS, etc.)”, by:
- sharing the technical achievements and challenges in the use of repeated SAR data from high revisit sensors (e.g. Sentinel-1) and on-demand acquisitions from high resolution sensors (e.g. COSMO-SkyMed) for terrain motion and land surface change applications;
- highlighting the role that the collaboration with users and stakeholders can play to add value to SAR-based scientific products.
For the purposes of a wide-area regional analysis, Sentinel-1 data were processed and interferometric products were generated using ESA’s Geohazards Exploitation Platform (GEP) [Cigna et al. 2020]. In particular, we will showcase the value of accessing such infrastructure and its hosted processing routines, discuss the impact of possible external constraints that may limit the exploitation by users (e.g. skill gap, limited internet connectivity), and outline possible actions towards an effective use of this resource (e.g. dedicated training).
In parallel, a bespoke campaign to monitor three priority areas defined by the Haitian users – i.e. Jérémie, Camp Perrin and Carrière Arniquet – was undertaken with ASI’s COSMO-SkyMed constellation using the Enhanced SpotLight mode, at 1-m spatial resolution, 16 days site revisit, in both ascending and descending modes, from December 2017 to December 2020 until the RO completion. These data were used to generate maps that allowed the identification of different categories of surface changes including:
(a) environmental, located along the estuarine section of the Grand’Anse River south of Jérémie and mixed with anthropogenic activities mostly related to quarrying and unregulated waste disposal [De Giorgi et al., 2021];
(b) geological, along the rock cliffs north-west of Jérémie where susceptibility of local lithologies to fracturing, toppling and lateral spreading may be worsened by the impact of hurricanes and storms, thus causing potential risks to small villages and isolated dwellings.
(c) urban, within the outskirts of Jérémie due to reconstruction, as well as new constructions, in areas where the Sentinel-1 InSAR analyses highlighted ground motions;
(d) rural, due to landslides to be distinguished by similar signals associated with agricultural practices along the slopes in the Camp Perrin.
Each of the above categories was validated based on ground-truth data collected during a technical mission that was jointly carried out by ASI, CNIGS, CNES, ICube-SERTIT and Bureau des Mines et de l'Energie d'Haïti (BME). Lessons learnt will be discussed in detail, in order to outline some recommendations on how to effectively integrate a range of SAR observations and products and pave the way for their embedding into the decision making process for recovery and resilience building.
In this regard, the discussion will also encompass the analysis of the feedback received by Haitian stakeholders (e.g. the Civil Protection, mayors of the municipalities affected by the hurricane Matthew, the Comité Interministériel d'Aménagement du Territoire) during the dedicated workshops that were held in Jérémie and Port-au-Prince to present these SAR and InSAR derived products, as well as from the group discussion at the final workshop. Among the feedback:
- the integration between the Sentinel-1 InSAR ground motion products and the COSMO-SkyMed-based urbanization map was positively assessed for purposes of urban planning, as a satellite evidence-base to highlight areas where cascading hazards may be triggered and thus new urbanization is not recommended;
- in light of the proven benefits of InSAR and SAR change detection techniques, there is the need for capacity building not only to transfer knowledge, but also to create a technical capability (also through the involvement of the local university) to exploit the RO pilot technical legacy after the project completion.

References:
- Cigna, F.; Tapete, D.; Danzeglocke, J.; Bally, P.; Cuccu, R.; Papadopoulou, T.; Caumont, H.; Collet, A.; de Boissezon, H.; Eddy, A.; Piard, B.E. Supporting Recovery after 2016 Hurricane Matthew in Haiti With Big SAR Data Processing in the Geohazards Exploitation Platform (GEP). Proceedings of 2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Waikoloa, HI, USA, 26 September–2 October 2020; pp. 6867–6870. https://doi.org/10.1109/IGARSS39084.2020.9323231
- De Giorgi, A.; Solarna, D.; Moser, G.; Tapete, D.; Cigna, F.; Boni, G.; Rudari, R.; Serpico, S.B.; Pisani, A.R.; Montuori, A.; Zoffoli, S. Monitoring the Recovery after 2016 Hurricane Matthew in Haiti via Markovian Multitemporal Region-Based Modeling. Remote Sensing 2021, 13 (17), 3509. https://doi.org/10.3390/rs13173509

Aeolus was launched over three and a half years ago and the L2B winds have been operationally assimilated at ECMWF for over two years.
The latest results from the assessment of the impact of L2B HLOS winds in ECMWF’s global NWP Prediction system will be presented. In particular, the impact with the second reprocessed Aeolus data from July 2019 - October 2020, the early part of which Aeolus winds had the largest signal-to-noise ratio and hence largest positive impact. This early FM-B laser period helps to give an impression of what could at least be achieved with a potential operational ESA/EUMETSAT Doppler Wind Lidar follow-on mission, in which significantly better SNR than Aeolus is sought.
In Observing System Experiments (OSEs), Aeolus provides statistically significant improvement of a good magnitude in short-range forecasts as verified by observations sensitive to temperature, wind and humidity, peaking at ~200 hPa in the extratropics and ~150 hPa in the tropics. Longer forecast range verification shows positive impact strongest at the 2-3 day forecast range, e.g. ~2% improvement in root mean square error for vector wind and temperature in the tropical upper troposphere and lower stratosphere and polar troposphere. Positive impact of up to ten days is found in the tropical lower stratosphere wind and temperature. This impact appears to be larger with the 2nd reprocessed versus the 1st and the NRT datasets; even some impact on the 500 hPa geopotential height in the N. Hemisphere is found. Experiments with variational QC modifications and a bias correction of the Rayleigh-clear winds as a function of temperature will be presented if time permits.
The operational Forecast Sensitivity Observation Impact (FSOI) metric shows that Aeolus is a useful contribution to the global observing system; with the Rayleigh-clear and Mie-cloudy winds providing similar overall short-range forecast impact in 2020-2021. Relative FSOI for the 2019 reprocessed dataset shows that Aeolus is amongst the most important satellite instruments; a good result for a demonstration mission. The relative FSOI is reduce to ~2% in late 2021 versus ~5% when the maximum atmospheric path signal was available in July 2019 (offline testing) due to SNR decreasing. Also, Aeolus winds have been absent for large fractions of 2020 and 2021 due to being flagged invalid (blocklisted) during instrument testing (aimed at understanding/mitigating the signal loss).

The tropics are the region with the largest uncertainties in the initial states for numerical weather prediction (analyses). Analysis uncertainties are largest in the tropical upper troposphere and the lower stratosphere (UTLS). One of the reasons is a lack of wind profiles which are more useful than temperature profiles in the tropics. This classical dynamical effect was described by J. Smagorinsky as “Not all data are equal in their information-yielding capacity. Some are more equal than others.”

Despite of their relatively small number and a relatively large random error in the Rayleigh channel, Aeolus wind profiles have a positive impact on the quality of global weather forecasts, with the maximal positive impact in the UTLS. Here we discuss one process contributing to the forecast improvements in the ECMWF model: the vertically-propagating Kelvin waves which are a major contributor to tropical variability.

Previous work showed that short-range forecast errors project on Kelvin waves significantly more in the easterly phase of the quasi-biennial oscillation (QBO) than in the westerly phase. Furthermore, it was shown that missing variance associated with Kelvin waves explains a large part of underdispersiveness of the ECMWF ensemble prediction system in the medium range in the tropics. It is unclear how well Kelvin wave dynamics is represented in global climate models, as they still poorly simulate the QBO and Madden-Julian oscillation, and their connections.

By filtering the Kelvin waves from the ECMWF analyses with and without Aeolus winds, we demonstrate that Aeolus alters the vertical structure of Kelvin waves in the layers with the strongest shear within UTLS. Changes in the Kelvin wave zonal wind within UTLS by Aeolus data can be up to +/-4 m/s. Similar to the Kelvin wave dynamics, the impact of assimilated Aeolus winds varies with periods 1-3 weeks. The coupling between the phase of QBO and the Aeolus winds is argued to happen through the Kelvin waves. The studied period May-September 2020 was characterised by a weakening easterly phase of the QBO. We suggest that a greater improvement of ECMWF forecasts in the tropical tropopause layer by Aeolus winds in May 2020 than later in summer 2020 was associated with the state of the QBO.

ESA’s Doppler wind Lidar mission, Aeolus, is an important new satellite observing system. It provides global coverage of wind profile information from the surface to 10 hPa. ECMWF was the first numerical weather prediction (NWP) centre to operationally assimilate the Aeolus horizonal line of site (HLOS) wind information on January 9, 2020. Impact studies have proven that the assimilation of Aeolus wind observations improves the quality of NWP forecasts of wind, temperature, and humidity, particularly in the upper troposphere and lower stratosphere, with the main improvements seen in the Tropics. This shows that Aeolus provides a valuable dataset that complements the existing Global Observing System (GOS).

The quality of the Aeolus HLOS wind information has evolved since launch. Reduction in instrument performance has caused degradations. This has been compensated by improvements in the Aeolus ground processing software and reprocessing activities. To achieve a long timeseries of high-quality Aeolus wind measurements, the Aeolus DISC (Data, Innovation, and Science Cluster) carried out a second reprocessing campaign that provided a new, high quality dataset covering the period from July 2019 to October 2020.

An ongoing ESA-funded project at ECMWF is investigating if the assimilation of Aeolus L2B winds improves the predictability of high impact and extreme weather events. The focus is on tropical cyclones and European extra-tropical storms. Also, the impact of Aeolus wind assimilation on the European forecast bust statistics is under evaluation. For this purpose, NWP observing system experiments with/without Aeolus L2B winds are being performed using the second reprocessed dataset and the operational data. A set of European and tropical severe weather events from July 2019 until the end of 2021 has been identified using various databases. The impact of Aeolus wind assimilation on these case studies and on the occurrence of forecast busts will be evaluated.

In this presentation the preliminary results will be presented and discussed.

The jet stream represents a meridional barrier for air masses, but also energy fluxes. So-called "streamer events" in the upper troposphere / lower stratosphere are an example of how this barrier can be disrupted. During such events large-scale air masses from lower latitudes are irreversibly mixed into the circulation at higher latitudes with various consequences for atmospheric chemistry, energy and momentum balance. Streamers are the consequence of poleward planetary wave breaking, which modulate the jet stream and thus effecting the exchange of air masses and energy between the equator and the pole. Aeolus wind measurements allow the derivation of atmospheric wave structures on different temporal and spatial scales in particular above the oceans, where wind measurements from ground-based instruments are sparse and streamer events most likely occur.
We use Aeolus L2B wind measurements to derivate the planetary wave activity, so-called dynamical activity index (DAI). The DAI represents the mean amplitude of planetary waves up to wavenumber 10 in the mid-latitudes. A comparison of the DAI based on the Aeolus data and ERA-5 reanalysis data is presented. First case studies are shown addressing the structure of streamers and their relation to planetary wave breaking. Due to planetary wave breaking gravity waves might be excited at the flanks of streamers. The flanks of the streamers are characterized by comparatively strong wind shear. To identify the flanks of the streamer, and so possible source regions for streamers, we calculate the wind gradients along the track.

Since its launch in 2018, ESA’s Aeolus satellite provides global height resolved measurements of horizontal wind in the troposphere and lower stratosphere. It carries the world’s first spaceborne high spectral resolution wind lidar, the Atmospheric Laser Doppler Instrument (ALADIN). With its high-power laser, which operates at a wavelength of 354.8 nm, ALADIN can acquire measurements from roughly 30 km altitude down to either the ground or to the highest optically thick cloud layer. Besides the height resolved wind profiles, Aeolus also provides information on optical properties of clouds and aerosols.
The main objective of Aeolus is to improve numerical weather prediction (NWP). Multiple NWP centres have already shown the positive impact of Aeolus data and started its operational assimilation. However, detailed wind information is not only beneficial for NWP, but also for atmospheric dynamics research. Many dynamic features show characteristic wind patterns and/or are strongly influenced by the prevailing wind. Especially in the upper troposphere / lower stratosphere region, Aeolus measurements can provide valuable information for the investigation of dynamic features such as for example gravity waves.
In this study, we will highlight the use of Aeolus data to investigate the vertical change in gravity wave activity throughout the upper troposphere and lower stratosphere. This is of particular relevance for understanding the importance of oblique gravity wave propagation on a global scale. With its height resolved wind measurements, Aeolus provides a unique dataset for such investigations.
Additionally, we will show how the unique combination of wind measurements with optical properties provided by Aeolus can be used to gain a global picture of wave induced polar stratospheric cloud formation. Polar stratospheric clouds play a crucial role for the stratospheric ozone depletion above the poles. They form if the temperatures in the polar winter stratosphere fall below a certain temperature. The negative temperature perturbations of gravity waves can lead to the formation of polar stratospheric clouds even if the synoptic-scale temperature is still well above the formation threshold. Most of the current global chemistry-climate models do not yet contain this formation mechanism of polar stratospheric clouds through gravity waves. However, several modelling groups have already started to look into this issue. We will provide these modelling groups with a climatology and test data set of polar stratospheric clouds in the Artic/Antarctic with a special focus on wave induced polar stratospheric clouds. The Aeolus data set is predestined for creating such a climatology.

The European Space Agency's Aeolus satellite mission is designed to provide global information on the wind speed from the ground up to 30 km, which is highly demanded for weather forecasting. Aeolus satellite has been set into orbit in August 2018 and its payload consists of a sophisticated ALADIN lidar instrument measuring wind velocity by sensing Doppler spectral shift of the laser echo scattered by the different layers of the atmosphere.

Since the global atmospheric circulation is largely driven by middle atmosphere dynamics, it is essential that the climate models take a proper account for the dynamical processes. Small-scale atmospheric waves, called internal gravity waves (IGWs) pose a particular challenge for models, whereas inaccurate parameterization of IGWs can dramatically bias the predictions of future atmospheric circulation changes.

In this paper, we explore the capacities of Aeolus wind observations in capturing and resolving dynamical processes in the upper troposphere and lower stratosphere (UTLS) such as IGWs at various temporal and spatial scales. The perturbations in the vertical profiles of Rayleigh horizontal line-of-sight (HLOS) wind velocity, associated with IGW activity, are derived by subtracting the Aeolus-derived “background” wind profiles from the individual measurements. Then, the global distribution of the IGW kinetic energy in the UTLS and vertical wavelength is derived using Aeolus measurements over the entire mission lifespan. The derived evolution of IGW activity over the Aeolus mission lifetime is analyzed in consideration of the time-varying performance of ALADIN instrument. The latter is evaluated using two French ground-based Doppler wind lidars operating at a mid-latitude site (Observatoire de Haute-Provence) and at a southern tropical site (Maïdo Observatory at la Réunion island) as well as collocated meteorological radiosoundings.

The global spatiotemporal distribution of IGW from Aeolus observations is compared with that derived from global high-resolution temperature profiling data provided by GPS radio occultation (RO) GRAS (GPS Receiver for Atmospheric Sounding) instruments operating onboard MetOp satellites. The comparison of Aeolus and RO-derived global IGW distribution allows concluding on the capacities and limitations of Aeolus wind profiling for studying UTLS dynamics.

Along with global warming, marine plastic pollution has been described as one of the most pressing matters that our oceans will face in the coming decades. In this context, as part of the Decade for Ocean Science for sustainable Development (2021-2030), the Sustainable Development Goal 14.1 targets the prevention and significant reduction of all kinds of marine pollution by 2025, particularly from land based activities, including marine litter. However, accurate observations of the sources, composition and densities of floating marine litter in the world’s ocean are sparse and lacking. Remote sensing can play a significant role in the detection and monitoring of marine litter, and to this extent adequate in situ observations of calibration and validation data are essential.

Towards this goal, we have since 2018 launched a series of experimental field campaigns, the Plastic Litter Projects, that aim to enrich the scientific community’s understanding of floating marine litter’s (FML) spectral properties and behavior. By developing, constructing and deploying artificial floating targets containing various types of FML, we aim to produce a comprehensive remote sensing image database which can be used for the development, calibration and validation of FML detection algorithms. Throughout the years, we have used various types of marine litter items such as PET bottles and HDPE bags, as well as natural floating materials such as reeds, in the construction of artificial floating targets. Our efforts have been focusing mostly on the ESA-deployed, multispectral satellite Sentinel-2, mainly due to its frequent revisit intervals, relatively high spatial resolution and very importantly, the open access data provision.
In the past two years, we have shifted our focus away from the small-scale re-deployable artificial floating targets, and started moving towards semi-permanent target infrastructure. We have also focused on answering the scientific community’s call for replicability in terms of the experimental field campaigns, both in regards to the materials being used but also the experimental set-up. To that extent, in 2020 under the scope of the ESA funded OSIP project “Plastic Litter Project: Detection and monitoring of artificial plastic targets with satellite imagery and UAV”, we investigated the use of a single reference material that can act as a representative proxy to replace the use of different FML items in artificial floating targets. In addition, we developed and deployed two different types of prototype targets, in order to assess the possibility of long-term deployment.

Moving on to 2021 we constructed two large long-term deployment artificial floating targets that were deployed in the Gulf of Gera, in the Island of Lesvos in Greece. The first target consisted of a circular 28 m diameter HDPE pipe frame, on which we fastened a series of white HDPE mesh sheets, creating an effective target area of about 600 m2. The second target was made out of more than 350 3 m wooden planks, in order to represent natural floating marine debris, approximating the same effective target area. Deployment of such large surface area targets guaranties the acquisition of at least one Sentinel-2 10x10 m pixel that contains solely the target material. However, both the HDPE mesh and the wooden planks targets do not completely cover the water surface, hence part of the signal response is attributed to water leaving reflectance, resulting in a more realistic set-up.
The targets were deployed in the Gulf of Gera for a period of 4 months, from June to September 2021, resulting in a total of 22 cloud-free Sentinel-2 acquisitions. In addition to the Sentinel-2 data, we acquired very-high resolution UAV images for the production of orthophoto maps, hyperspectral UAV measurements in the range of 400 to 900 nm, in situ spectrometer measurements in the same spectral range, as well as ancillary metadata including wind speed, water turbidity using a Secchi disk and incident light intensity measurements. The experimental set-up and the large number of acquisitions allowed for the acquisition of data under different conditions and target configurations, providing the opportunity to examine the effect that a number of parameters, such as biofouling and degree of submersion have on the spectral response of the target materials. Combining the two targets for a number of Sentinel-2 overpasses allowed for a number of acquisitions with mixed FML and natural debris scenarios.

Biofouling accumulations can potentially affect the spectral signature of FML, both in the visible and infrared range. Deployment of the floating targets in productive waters such as those of the Gulf of Gera translates into the fact that the target materials are susceptible to bio-fouling. The long-term deployment of the large surface area HDPE target during the PLP2021 acquisition campaign, has allowed us to produce images of the target with and without biofouling accumulations, thus presenting the possibility to assess the effect of biofouling on FML reflectance. Preliminary analysis results show that biofouling mostly affects the intensity of the FML signal and specifically on the near-infrared range, without major effects to the signal shape.

Together with the above mentioned parameters, turbidity and wind velocity are also factors that can influence the water leaving reflectance and FML signal, affecting spectral classification methodologies. Turbidity is especially important for semi-enclosed basins with very productive and turbid waters, making it especially challenging in terms of spectral analysis. However, the somewhat varying turbidity of the Gulf of Gera waters presents the possibility to correlate detection accuracy with degree of turbidity. This can be especially useful in operational detection applications in river outflow situations in estuaries and river mouths, from where a high percentage of FML originally reaches the ocean.

Initial results suggest that spectral classification methodologies such as partial unmixing algorithms, can successfully be used for FML detection using the mean spectral signature acquired during the PLP2021 deployment period. Further analysis of the correlation between the above mentioned parameters and the resultant signal of FML can prove especially useful in operational scenarios of FML detection and monitoring.

Oceans receive solid waste from anthropogenic activities. A significant amount of the produced solid waste is made of plastics. The amount of plastic debris in the ocean and coastal areas is steadily increasing and is now a global major environmental problem. Accumulation of marine debris poses considerable threats to aquatic species, ecosystems and human beings too, as microplastics are eaten by fish and shellfish and consequently enter our food chain. At the global scale, the 2030 Agenda for Sustainable Development, adopted by the United Nations in 2015, calls for action to conserve and sustainably use the oceans, seas and marine resources with the Sustainable Development Goal (SDG) No. 14. Among the SDG 14 targets, the 14.1 calls to prevent and significantly reduce marine pollution of all kinds, in particular from land-based activities, including marine debris and nutrient pollution. From the European perspective, the Marine Strategy Framework Directive (MSFD) requires the EU Member States to ensure that "properties and quantities of marine litter do not cause harm to the coastal and marine environment".
For monitoring marine plastic litter, ground-based monitoring systems and/or field campaigns provide precise information on the quantity and quality of the marine litter. Nevertheless, ground-based monitoring campaigns for collecting data on marine litter are limited, time-consuming, expensive, require great organisational efforts, and provide very limited information in terms of spatial and temporal dynamics of marine debris.
Earth Observation by satellite has the potential to contribute significantly to marine plastic litter monitoring thanks to its global synoptic point of view. However, remote sensing of marine plastic litter is in its infancy, and it is a significant scientific and technological challenge.
In 2020, a consortium led by Planetek Italia (Italy), including the National Technical University of Athens (Greece) and the Environmental Prevention and Protection Agency of Puglia Region (Italy), participated in an ESA call for ideas, the Discovery Campaign on Remote Sensing of Plastic Marine Litter, with a novel idea for assessing the feasibility of marine plastic litter detection from space, which was selected.
The project was titled "Crowdsourcing, Copernicus and Hyperspectral Satellite Data for Marine Plastic Litter Detection, Quantification and Tracking" or REACT.
The REACT project focused on providing a proof-of-concept on remote sensing of marine plastic litter by developing the following methodology to detect plastic litter offshore and onshore. The methodology exploited data fusion of multispectral (MS) satellite data from Sentinel-2 and WorldView and hyperspectral (HS) satellite data from PRISMA, together with in situ data collection, and took advantage of two different approaches. The first was based on Spectral Signature Unmixing (SSU), and the second was based on Machine Learning (ML) methodologies.
The main objectives of the project were to:
• Assess how plastic litter can be detected and possibly quantified with current and future remote sensing technology;
• Develop adaptive indices insensible to biases induced by sunglint on satellite radiometric products and indicate the constraints of current satellite missions under various atmospheric and illumination conditions;
• Exploit data fusion methods between remote sensing data with high spectral (PRISMA hyperspectral, Sentinel-2) and high spatial resolution (PRISMA panchromatic, WorldView) to increase the sensor detectability of marine plastic litter;
• Explore SSU methodology for sub-pixel detection of floating marine plastic debris;
• Explore ML techniques for detecting plastic litter;
• Conduct controlled experiments under real conditions to better understand the effect of the atmosphere and the illumination conditions on the spectral properties of marine plastics in visible and infrared wavelengths.
The key target user of the REACT project was the Environmental Prevention and Protection Agency of Puglia Region, Italy (ARPA Puglia), which is in charge of detecting and monitoring marine plastic litter in the framework of the European legislation (i.e., MSFD).
The critical target users' needs can be summarised in reaching the capacity to:
• support field campaigns by environmental agencies to implement data collection plans by field operators;
• perform regular monitoring of marine litter over broad areas;
• provide spatial and temporal distribution of marine litter;
• forecast paths of floating litter;
• identify potential sources of plastic litter into the marine environment and forecasting possibly places of beached litter;
• achieve a cost-efficient, repeatable, and flexible methodology, from local to the national level.
During the project, some controlled experiments were performed to understand better the effect of the atmosphere and the illumination conditions on the spectral properties of marine plastics in visible and infrared wavelengths. Twelve floating plastic targets were constructed for the experiment. Their sizes were selected according to the spatial resolution of data expected to be achieved by image fusion. Targets were realised with four types/compositions of plastic materials with various colours: 1) low-density polyethylene (tarps in white, yellow and green colour), 2) polyethylene terephthalate (transparent water bottles, green oil bottles), 3) polystyrene (sheets for building insulation in cyan colour), and 4) all the above materials in equal surface extent. The 12 targets were placed offshore and onshore during satellite passages. In-situ measurements using a spectro-radiometer were also carried out during the controlled experiments. The controlled experiments were realised in Mytilini and Koplos Geras, on the Greek island of Lesvos, with the support of the remote sensing group of the University of Aegean.
The MS and HS satellite imagery collected during the controlled experiments were processed with image fusion techniques to obtain merged images with higher spatial resolution than the initial high spectral resolution images. On the fused images, SSU techniques and ML algorithms were used.
SSU contributes to the extraction of information at the sub-pixel level. Its main scope is to detect the distinct spectra in the fused MS and HS scenes, which can represent different materials, and to estimate their apparent quantification in a pixel in terms of a fraction. Endmembers correspond to different signals. In contrast, abundances refer to the fractions of these endmembers within a mixed pixel. The project achieved marine plastic litter detection by separating endmember spectra that best characterise plastic materials and waters. The abundance maps of these plastic material spectra led to plastic targets detection.
Plastic targets were also detected by supervised and unsupervised ML algorithms. The output was a probability map representing the probability that a pixel contains or not plastic.
The main findings of the project are summarised below:
• Plastic and water spectral signatures are significantly correlated in the original images and the fused HS data. The Principle Component Analysis (PCA) and the Gram-Schmidt Adaptive (GSA) algorithm gave better results in terms of spectral discrimination between water and plastics;
• Sunglint correction provided no beneficial in spectral discrimination between water and plastics;
• Image fusion based on matrix factorisation performed better than the deep learning methods. The Coupled Nonnegative Matrix Factorisation (CNMF) method produced better results when plastic targets were placed offshore. On the other hand, the Hyperspectral Superresolution (HySure) method presented slightly better results with targets placed onshore;
• SSU was able to detect plastic targets, although some manual tuning is necessary. Similar conclusions in the case of plastic spectral indexes were used;
• PS and LDPE were detectable, while PET was not;
• SSU on fused PRISMA data efficiently detected floating plastic accumulations up to 2.40x2.40m (about 1/2 of the panchromatic PRISMA band resolution);
• SSU on fused Sentinel-2 and WorldView data efficiently detected floating plastic accumulations of 0.60x0.60m (about 1/4 of WorldView resolution);
• ML algorithms provided promising results in plastic detection, although with a small dataset;
• Both SSU and ML require land and shallow waters masking;
• No significant results were highlighted with the targets onshore with both SSU and ML.
This work was supported in part by the Discovery Element of the European Space Agency's Basic Activities under ESA Contract 4000131235/20/NL/GLC (REACT Project: Crowdsourcing, Copernicus and Hyperspectral Satellite Data for Marine Plastic Litter Detection, Quantification and Tracking).

Over the past few years, spectral signature of marine litter has been studied both for virgin and weathered plastic samples and several spectral techniques based on indices have been developed to detect them. In this research, we present artificial intelligence (AI) results based on RGB, multispectral and short wave InfraRed (SWIR) for detection of floating marine litter using different sensors ranging from fixed camera, drone, airborne and high resolution satellite based multispectral data. All the data from the different cameras have been preprocessed considering the type of camera and the installation set-up. To reach this aim, several modifications have been made to the VITO’s cloud processing workflow for UAV data named MAPEO.
Part of the study has been done through making synthetic plastic accumulation zone in Belgium, while the other part was done in one of the hotspot source of marine litter in Hanoi, Vietnam which is part of an ESA project called “Artificial Intelligence and drones supporting the detection and mapping of floating aquatic plastic litter (AIDMAP)”. For fixed cameras, Detectron2 from Facebook AI research which provides state of the art detection and segmentation has been used for detecting individual floating litters. Faster Region based Convolutional Neural Network (Faster-RCNN) has been applied and 88.74 percent accuracy obtained at intersection of unit 50.
For detecting litter accumulation zones from multispectral UAV images, Decision Tree, Random Forest and Support Vector Machines (SVM) applied for classification of litter versus no litter using several widely used spectral indices, band ratios and normalized band ratios. Random forest classification showed better performance compared to the other algorithms.
Finally, first tests in an outdoor condition have been performed with a multispectral SWIR camera with six spectral bands that have been tuned to be sensitive at specific wavelengths. These wavelengths correspond to spectral features that are of particular interest to marine plastic based on the literature. The SWIR camera was installed as a fixed camera over a bridge in Belgium and its performance in detecting plastic objects was tested through several campaigns, here we present the detection results for the first time to the community.

Pristine and wilderness landscape of Patagonia is continoulsy threated by antropoghenic marine debris. Here, we presented a new approach to detect marine litter over beaches using UAV multiespectral imagery. The eBee UAV using the Parrot SQ ® were used to detect marine litter in two different beaches over the Patagonian Fjords located in the North part (Playa Muerta) and southern part close to Sn Rafael glaciers (Playa Leopardo). The Multispectral imagery were processed and calibrated in order to estimate the Surface reflectance. Then, support vector machine, Random forest and automatic digital classification were used to tratin and detect several litter such as styrofoam and buoys. Results shown 3 tons of litter per 16 km2 with an accuray about 6%. Color buoys are better classified than styrofoam which might be correlated with brightning pixel such as branches, shells and rocs. The combination of machine learning and very high spatial resolution imagery provided by UAV is validated to provide better information about marine litter in Patagonia mainly produced by the acquaculture systems.

In the past decade, plastic marine litter (PML) research has grown into a serious endeavour, exposing various aspects of plastic marine litter. For example, several studies have investigated and described the diversity and complexity of PML as well as the need to address not only floating or semi-floating plastic marine litter, but also plastic debris distributed in the first meters of the water column. Although remote sensing techniques could offer unique opportunities for addressing PML from local to global scale, it is agreed by the scientific community that a single sensor cannot provide all the required information since PML can vary considerably as for type, size, shape, chemical composition, buoyancy, and the way it manifests in the environment. While several optical passive remote sensing techniques (e.g. panchromatic cameras, multi-/hyperspectral sensors, thermal infrared cameras) have already been applied to the remote sensing of plastics either in controlled experiments or in real marine scenarios, the potential of LIDAR techniques to address marine litter issue is still almost unexplored.
The fluorescence LIDAR is an active remote sensing technique that can be exploited to acquire information on the chemical-physical characteristics of a volume target by using a pulsed laser. Fluorescence, actually, is an inelastic process due to the spontaneous emission of photons after the absorption of the incident radiation by the material system, given that such radiation belongs to an absorption line or band of the target components. The excited level decays after a characteristic time , called the fluorescence lifetime, in lower energy levels and in the fundamental level. The signal acquired by the LIDAR system can be analysed either in the time domain, e.g. by using a streak camera, in order to perform lifetime spectroscopy, or in the frequency domain, e.g. by using a spectrometer coupled to a linear array detector, in order to perform fluorescence spectroscopy. In both cases, the fluorescence LIDAR system can provide information on the chemical-physical properties of the target.
In this paper, we present a set of experimental tests, carried out in the laboratory, by using an in-house developed hyperspectral fluorescence LIDAR system. Different types of raw plastics and ocean-harvested objects have been investigated as for their fluorescence properties. The LIDAR sensor was operated from a distance of about 10 m while the samples were measured both in dry conditions and while immersed in a simulated water column, down to a depth of about 1 m. Besides the results of the experiments for the detection and characterisation of plastic marine litter, several factors that can affect the detection and characterisation of plastic marine litter are discussed: these includes photobleaching effects on plastics, fluorescence of dissolved organic matter and Raman scattering due to the water molecules.
This study was funded by the Discovery Element of the ESA's Basic Activities, contract no. 4000132184/20/NL/GLC.

Since the 1950’s, positively buoyant plastic objects have been accumulating at the surface of the oceans, transported by currents, wind and waves. Small millimeter-sized pieces (less than 4.75 mm), known as microplastics, count in trillions at global scale and pose an increasing risk to marine biota. Floating microplastics concentrate along large-scale convergence zones associated with Ekman dynamics in the five major ocean basins, but a comprehensive analysis of the spatial and temporal distributions is lacking and the monitoring tools are not well developed to assess global distributions. Through our recently funded NASA project Spaceborne Quantification of Ocean MicrO-Plastics (SQOOP), we are conducting a feasibility study of remote detection of surface microplastics, in the context of different surface particle properties and uncertainties in atmospheric correction with multiple, advanced detection techniques including hyperspectral and polarimetric approaches.

Here we will present preliminary results evaluating: 1) Geospatial and temporal trends in existing ocean color products across hot spots that may be related to enhanced reflectance from plastics; 2) Simulations of ocean surface hyperspectral reflectance using simple mixed pixel models to the Top of the Atmosphere (TOA) under different microplastic concentrations and atmospheric conditions; and 3) Simulations of microplastics using robust vector radiative transfer models for coupled ocean-atmosphere systems that include polarization. Detectability of floating marine microplastics will be conducted using statistical information content assessment in terms of current and future instrument characteristics, microplastic quantity and nature, and external conditions, such as observation geometry and atmospheric state. The sensitivity analysis will compare simulated and ocean color data to historic data of microplastic concentrations measured in surface net tows across the world ocean. The influence of floating microplastics on atmospheric correction and on standard ocean color retrievals of aerosols and of other ocean properties will also be explored.

In collaboration with visual artist Oskar Landi, we are producing an unconventional and engaging art exhibit to engage the public further in this pressing environmental problem. This unique art and science collaboration also provides new insights and informs our scientific models of sea surface dynamics including sun glint, sea foam, and floating plastics.

In preparation of the FLEX satellite mission we have conducted a series of field campaigns, during which we aimed to (i) correct real world data on SIF to (i) develop a complete FLEX-like reference data set of field data that can be used to develop and test FLEX satellite data processing concepts and data products, (ii) quantitatively understand the dynamics within the SIF signal and their quantitative link to structural and functional vegetation traits to support the development of vegetation stress indicators, and (iii) to evaluate and test the components required for a FLEX Cal/Val concept.

We have conducted two large FLEX campaign activities, namely the AtmoFLEX and FlexSense campaigns, in the years 2017, 2018 and 2019, which aimed to collect complete optical and SIF data from various ecosystems across five European countries. Additionally, we have finalized the PhotoProxy activity (funded by EO4Science), during which we included further field data from the US cornbelt (cooperation with colleagues from the University of Nebraska, USA) aiming to improve our knowledge on the option to include SIF in GPP predictions. Finally, we have completed the AtmoFLEX campaigns, which focussed on the acquisition of atmospheric data and the development and testing of ground-based SIF references systems (FloX system). These ‘FLEX driven’ campaign activities were integrated with other synergistic campaign activities, such as SARSense, SurfSense, or Chime (Vila-Guerau de Arellan et al. 2020, Mengen et al. 2021). With this presentation we will give an overview on the main outcomes and the conclusions, which we could draw from these challenging campaign activities.

For a correct retrieval of the relatively weak solar-induced fluorescence signal from a satellite platform, a stringent atmospheric correction is essential. This challenge was already identified during the selection of the FLEX satellite mission, and the tandem concept between FLEX and Sentinel-3 is a direct answer to this challenge. During above-outlined campaigns, we could close a crucial gap by collecting a complete data set on synchronously recorded detailed atmospheric data, ground based and airborne radiation and SIF retrievals, as well as top-of-atmosphere satellite data, acquired from a tandem constellation between Sentinel-3A and Sentinel-3B during the commissioning phase. At five times slots, we were successfully underflying the Sentinel tandem constellation with the airborne FLEX-like sensor HyPlant, while flying over dedicated atmospheric measurement stations. This dataset is complemented by 14-month long time series of FloX measurements, more than 700 flight lines from the high-performance imaging spectrometer HyPlant, and various associated measurements of bio-physical plant traits, carbon and water flux measurements and dedicated functional measurements for monitoring the effects of environmental stresses on plant health. Using this large reference dataset, which is currently used for the development and testing of the FLEX satellite and FLEX data processing scheme and which delivered quantitative sensitivity parameters on the impact of atmospheric characterization for SIF retrieval, we could:
- show that solar-induced fluorescence is sensitive to early signs of vegetation drought and shows significant changes of its far-red peak already 3 days after the onset of drought, while reflectance indication was only sensitive after 7 days, once drought effects already left visible marks (Damm et al., submitted),
- detect the effect of summer heat waves, which significantly reduced photosynthetic carbon uptake, as clear changes in the solar-induced fluorescence signal predicted by previous model assumptions (Martini et al. 2021),
- establish a data downscaling approach, which can be used to bring top-of-canopy SIF measurements closer to leaf-level SIF, which is the relevant input parameter for many mechanistic vegetation flux models (Siegmann et al. 2021),
- deliver an overview of relative uncertainty and bias estimates for FloX time series and derive requirements towards a calibration and validation network for SIF satellite missions (e.g. FLEX, Buman et al., submitted).

Thus, executing this ambitious and integrated campaign concept, we could (i) lay the basis for the development of the FLEX Cal/Val scheme, (ii) confirm some hypotheses on SIF being a sensitive drought and heat stress indicator, and (iii) greatly extend the data basis on the natural variability of the SIF signal across different ecosystems, the diurnal and seasonal cycle, and as a reaction to environmental extremes. The campaign data has been delivered to ESA and is currently being made freely available via the ESA campaign webpage and data portals.



Selected publications that emerged from these campaign activities:

Siegmann B., Cendrero-MateoM.P., Cogliati S., Damm A., Gamon J., Herrera D., Jedmowski C., Junker-Frohn L.V., Kraska T., Muller O., Rademske P., van der Tol C., Quiros-Vargas J., Yang P. & Rascher U. (2021) Downscaling of far-red solar-induced chlorophyll fluorescence of different crops from canopy to leaf level using a diurnal data set acquired by the airborne imaging spectrometer HyPlant. Remote Sensing of Environment, 264, article no. 112609, doi: 10.1016/j.rse.2021.112609.

Martini D., Sakowska K., Wohlfahrt G., Pacheco-Labrador J., van der Tol C., Porcar-Castell A., Magney T.S., Carrara A., Colombo R., El-Madanay T., González-Cascón R., Martin M.P., Julitta T., Moreno G., Rascher U., Reichstein M., Rossini M. & Migliavacca M. Heat-wave breaks down the linearity between sun-induced fluorescence and gross primary production. New Phytologist, accepted.

Vila-Guerau de Arellan J., Ney P., Hartogensis O., de Boer H, van Diepen K., Emin D., de Groot G., Klosterhalfen A., Langensiepen M., Matveeva M., Miranda G., Moene A., Rascher U., Röckmann T., Adnew G., Brüggemann N., Rothfuss Y. & Graf A. (2020) CloudRoots: integration of advanced instrumental techniques and process modelling of sub-hourly and sub-kilometre land-atmosphere interactions. Biogeosciences, 17, 4375-4404, doi: 10.5194/bg-17-4375-2020.

Mengen D., Montzka C., Jagdhuber T., Fluhrer A., Brogi C., Baum S., Schüttemeyer D., Bayat B., Bogena H., Coccia A., Masalias G., Trinkel V., Jakobi J., Jonard F., Ma Y., Mattia F., Palmisano D., Rascher U., Satalino G., Schumacher M., Koyama C., Schmidt M., Vereeken H. (2021) The SARSense campaign: air- and space-borne C- and L-band SAR for the analysis of soil and plant parameters in agriculture. Remote Sensing, 13, article no. 825, doi: 10.3390/rs13040825.

The FLuorescence EXplorer (FLEX) will be the first mission designed to monitor the photosynthetic activity of the terrestrial vegetation through the retrieval of the solar-induced chlorophyll fluorescence and the true vegetation reflectance (Level-2 products). To provide reliable estimates on the photosynthesis efficiency on large spatial and temporal scales, the intermediate Level-2 products demand specific uncertainty determination. The validation of the Level-2 products is based on the comparison between the retrieved FLEX products and the ground truth measurements. However, similar to the satellite products, ground truth measurements have also associated uncertainties and variances mainly related to 1) instrument performance and calibration, 2) retrieval error, 3) and site dependent spatial/temporal variability, which need to be characterized to perform a fair comparison.

In this context in July 2020 a ground-based and airborne campaign was carried out to evaluate the calibration/validation (Cal/Val) protocols developed between the Spanish National Institute of Aerospace Technology (INTA) and the University of Valencia for the future FLEX mission in the experimental agricultural site of Las Tiesas, Barrax, Spain. During this field campaign various surface types were investigated including heterogeneous crop fields such as melon, pepper, alfalfa, onion, and corn as well as homogenous surface types such as festuca and bare soil fields. In each surfaces type five elementary sampling units (ESUs) were assigned, and during the airborne overpass simultaneously top of canopy and leaf level measurements were performed at each ESUs. The airborne system was equipped with an Airborne Hyperspectral Scanner (AHS, Sensytech), Compact Airborne Spectrographic Imager (CASI 1500i, ITRES), and a high-resolution Chlorophyll Fluorescence sensor (CFL, Headwall). Top of canopy measurements were performed with ASD FieldSpec3 (Malvern Panalytical) and at leaf scale, the FluoWat leaf clip was used, coupled with an ASD FieldSpec3, to characterize the optical properties and fluorescence emission on the different ESU. Furthermore, with the objective of characterizing the real reflectance and solar-induced fluorescence spatial and temporal variability in a continuous and not supervised mode, a CableCam system was tested for continuous measurements over the festuca and melon field. The CableCam is a custom system consisting in a semi-autonomous trolley traveling along a 70 meters zip line, equipped with two high resolution spectroradiometers (one covering the VIS/NIR spectrum range and the other one covering the spectral range of the O2 A and O2 B atmospheric absorption bands – Piccolo system), a multispectral camera (MAIA-S2, SAL Engineering), and a thermal camera (ThermalCapture Fusion, TEAX). The CableCam was set 4 meters above the canopy. Moreover, continuous measurements in the festuca field were collected by a FloX system (Hyperspectral Devices) placed in a two-meter tower. Finally, a sun tracker attached to a second Piccolo system was used to monitor the direct and global solar irradiance during the campaign.

In this study, first the uncertainty of each measurements system was estimated by comparing the measured irradiance with the modelled irradiance generated using the radiative transfer model libRadtran. Secondly, the instrument uncertainty was propagated to estimate the true reflectance and fluorescence retrieval error. Additionally, the retrieved products were compared with the FluoWat leaf level measurements. Thirdly, considering the absolute uncertainty (instrumental and retrieval error) the spatial and temporal variability of the cited Level-2 products was estimated for each studied site. Finally, the airborne imagery absolute uncertainty and the spectral variability among pixels was used to estimate the number of sampling units needed to capture the spatial heterogeneity of a 300x300 meters FLEX pixel. The strategies tested in this study aim to set the roadmap for the Cal/Val protocols of the future FLuorescence EXplorer mission.

The validation of SIF products that will be provided by the FLEX mission is a challenging issue and has been and will be the focus of both past and future ESA campaigns and initiatives. In brief, a good validation strategy implies i) that a reliable mean fluorescence signal (full spectrum) is provided for an area of at least 3x3 pixels of FLEX and ii) the uncertainty associated to the SIF estimation does not exceed the error requirement recommended for FLEX measurements.

Past experiences acquired over a series of field campaigns and modelling studies, has indicated that the first requisite can be potentially satisfied either by using different solutions. For example by exploiting mobile platforms flying at relatively low elevation and that can rapidly cover (or sampling) the entire area required for FLEX validation, or by integrating a sufficient number of ground-based fixed or mobile measurements over that same area. Here, we will show comparison of measurements at different scales, errors, caveats and potential improvements. The second requisite poses an additional challenge, considering the rather incompressible uncertainty associated with the use of airborne or ground-based spectrometers and the obvious dependency of such uncertainty on measurement height and optical features of the near-surface atmosphere.

We present different validation approaches, sites and instruments requirements and the overall sampling approach for performing the validation of the fluorescence metrics throughout the mission lifetime of FLEX. This presentation will summarize all those aspects, by bringing together theoretical considerations as well as a large amount of data which have been acquired over the last 5 years using ground/tower based, drone-based and airborne-based fluorescence sensors. However, some points still need to be better investigated and our contribution will also highlight new directions and additional activities to be undertaken.

In the last decade, intensifying efforts on field campaigns to measure solar-induced fluorescence (SIF) at different scales have been made by distinct Universities, Research Institutions, and within the framework of space mission activities like the FLuorescence EXplorer (FLEX), 8th Earth Explorer from the European Space Agency (ESA). In this context, the SIF research community has gathered increasing interest in canopy-SIF monitoring at the tower and Unmanned Aerial System (UAS) scale both to continue advancing vegetation-orientated scientific studies and to provide accurate validation reference datasets for current and future fluorescence-related satellite-derived products.

Focusing on satellite validation purposes, the estimation of reliable canopy-level SIF spectra to be used as 'ground truth' is not straightforward. Enlarging the footprint size is usually achieved by placing the measurement instrument at a certain distance from the canopy level, e.g., by using sensors mounted on towers or tilting the observation angle. However, the larger the optical path between the target and the measurement instrument, the stronger the atmospheric impact, being oxygen absorption effects the most critical ones to be corrected.

Oxygen absorption features, particularly the oxygen-A band around 760 nm, have been traditionally used in passive remote sensing experiments at the canopy level to measure the chlorophyll fluorescence signal emitted by plants. Absorption features are advantageous spectral regions to measure SIF since the difference between canopy-reflected solar irradiance and emitted fluorescence is reduced; therefore, increasing the sensitivity to detect SIF. However, monitoring SIF at discrete wavelengths could not be sufficient in the context of the FLEX mission where satellite-derived spectrally-resolved SIF is planned to be estimated.

In this work, we propose a 3-step physically-based processing chain mainly designed for fluorescence retrievals at the tower level. The proposed processing accounts for (1) the oxygen absorption estimation through the own-developed toolbox, the O2_TRANS, given the environmental and observation conditions, (2) the oxygen correction and calibration procedure of the acquired spectra, and (3) the application of a SIF retrieval strategy.

In the proposed approach, only main parameters regarding the illumination and acquisition geometry are required to estimate oxygen absorption effects at a specific tower setup, i.e., solar zenith angle, downward-looking observation zenith angle, and target-sensor distance. Also, meteorological parameters of air temperature and pressure (T,p) can be accounted for in the developed O2_TRANS software package for a more detailed oxygen transmittance modeling. The O2_TRANS toolbox is based on the HITRAN database, https://hitran.org/, producing line-by-line oxygen transmittance modeling for the O16O16, O16O17, O16O18 isotopologues. The developed toolbox (publicly available through a git repository) can be easily configured for a specific tower setup. The O2_TRANS provides the set of oxygen transmittance spectra needed to correct the pairs of quasi-simultaneous downwelling and upwelling radiance spectra typically measured to estimate SIF.

In a second step, the set of acquired upward- and downward-looking radiance spectra are oxygen-corrected and calibrated. Here, the word calibration refers to the application of a calibration spectral factor required to compensate for errors associated with the algebra manipulation at the sensor resolution during the oxygen absorption correction. This calibration spectral factor has been empirically formulated to be merely dependent on the O2_TRANS outputs to avoid any more advanced approximation error approach involving model training dependencies.

Finally, in a third step, the preferred SIF retrieval strategy can be applied, i.e., either a differential absorption technique or(and) a spectral fitting method.

This work illustrates with simulated and real datasets a physically-based approach that can be used to correct measurements for oxygen atmospheric effects at the proximal sensing level and is flexible enough to be coupled with any SIF retrieval technique.

Earth observations via satellite are becoming more and more important for all environmental studies and to monitor political restrictions. Most satellite products are based on level 1 top of atmosphere radiance measurements. However, the quality of those data is difficult to estimate. Instruments can be characterized and calibrated on the ground but the changes during the launch and ageing must be characterized in space. Uncertainty estimates can be done comparing with ground truth stations. To apply that comparison the atmosphere must be characterized completely as level 1 data are dependent on interaction within the atmosphere.
A different method for the uncertainty estimate is the comparison of two satellites with similar instrumentation. If the satellites have similar viewing geometries of the same geographic targets within a short time frame, no atmospheric correction is needed. Different instrument characteristics such as the central wavelength and the width of a channel must be considered .
The Sentinel 3 tandem mission was a unique opportunity to compare the same instruments on two different satellite as Sentinel 3A and 3B were flying in the same orbit with a time shift of only 30 s [Clerc et al. 2020, Lamquin et al. 2020, Hunt et al. 2020]. During the tandem phase, OLCI-B was reprogrammed to mimic the future Earth Explorer 8 FLEX for a number of scenes over Europe. The reprogrammed OLCI (OLCI-FLEX) measured in 45 bands within the visible spectral range with high resolution in the oxygen absorption bands.
This data set is used to establish and test a validation strategy for the future FLEX campaign. One step of this validation strategy is the comparison of OLCI and FLEX level 1 radiances. It is based on a transfer function that modifies radiance measurements at OLCI-FLEX bands towards OLCI-A bands to make the measurements comparable. The transfer function requires possible characterization of the atmosphere and the surface. It is done applying an optimal estimation algorithm on the high resolution OLCI-FLEX measurements. The second step of the transfer function is a simulation of OLCI-A measurements based on the found environmental characteristics using a forward model. The optimal estimation algorithm and the forward model are based on look up tables calculated with the radiative transfer model MOMO [Hollstein et al. 2012].
The application of the transfer function for the OLCI-FLEX data showed a difference between OLCI-FLEX and OLCI-A of about 2 % which agrees well with the findings of Lamquin et al. 2020. Furthermore, possible processing issues could be identified for large wavelengths. The results show that the method of the transfer function is sensitive for both instrument differences as well as processing uncertainties. Thus, a good quality control is possible.


Clerc, Sébastien, Craig Donlon, Franck Borde, Nicolas Lamquin, Samuel E. Hunt, Dave Smith, Malcolm McMillan, Jonathan Mittaz, Emma Woolliams, and Matthew Hammond. ‘Benefits and Lessons Learned from the Sentinel-3 Tandem Phase’. Remote Sensing 12, no. 17 (2020): 2668.

Lamquin, Nicolas, Sébastien Clerc, Ludovic Bourg, and Craig Donlon. ‘OLCI A/B Tandem Phase Analysis, Part 1: Level 1 Homogenisation and Harmonisation’. Remote Sensing 12, no. 11 (3 June 2020): 1804. https://doi.org/10.3390/rs12111804.

Hunt, Samuel E., Jonathan PD Mittaz, David Smith, Edward Polehampton, Rose Yemelyanova, Emma R. Woolliams, and Craig Donlon. ‘Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles’. Remote Sensing 12, no. 18 (2020): 2893.

Hollstein, André, and Jürgen Fischer. ‘Radiative Transfer Solutions for Coupled Atmosphere Ocean Systems Using the Matrix Operator Technique’. Journal of Quantitative Spectroscopy and Radiative Transfer 113, no. 7 (1 May 2012): 536–48. https://doi.org/10.1016/j.jqsrt.2012.01.010.

With an ever-changing environment the need for accurate, timely and high-resolution information on land use/land cover and its changes has increased tremendously over the past years. Until now however, regional or continental land cover maps were solely based on high resolution optical earth observation data such as Sentinel-2 or Landsat while the use of SAR data such as Sentinel-1 in the production of large area land cover maps is still in its infancy.

For this purpose, the European Space Agency (ESA) initiated the WorldCover project which has released in October 2021 a freely accessible global land cover product at 10 m resolution for 2020, based on both Sentinel-1 and Sentinel-2 data. WorldCover contains 11 classes and has been independently validated with an overall accuracy of 74.4%.

A crucial aspect for WorldCover was the involvement of several end users such as WRI, UNCCD, FAO, CIFOR & OECD active in different domains who provided primary input for all engineering aspects and followed the whole project workflow from design up to validation and uptake. Consequently, WorldCover intends to provide a substantial benefit to various user communities and expands the established global land cover base of users and the development of novel services.

In this presentation we will present you the WorldCover product, illustrate the complementary power of Sentinel-1 and 2 for global land cover mapping, discuss tradeoffs made and lessons learned in the production of the WorldCover product, zoom in on the user feedback on the product and show how the product can be improved towards the future.

Decision making at regional, national and international scales can be greatly improved with the availability of regular, consistent, and reliable maps of the land cover and how it changes over time and space. With modern improvements in data accessibility and the advancement of computational resources, operationalizing the production of these products at a large scale is now achievable. The next challenge comes with building systems which are not only just meeting today’s needs, but also have the ability to easily incorporate future anticipated improvements.

Geoscience Australia’s Digital Earth Australia (DEA) in collaboration with Aberystwyth University (Wales, UK) and Plymouth Marine Laboratory (PML) have built a globally applicable method for generating consistent, large-scale land cover maps from satellite imagery. The approach builds on the Earth Observation Data for Ecosystem Monitoring (EODESM) system (Lucas and Mitchell, 2017), which constructs and describes land cover classifications based on environmental descriptors derived from Earth observation (EO) data. The system’s land cover structure is based on the globally applicable United Nations Food and Agriculture Organisation Land Cover Classification System (UN FAO LCCS) taxonomy). This new land cover classification system has been built to be adaptable and modular, allowing for its application to a range of different landscapes, at different spatial and temporal resolutions and with a variety of data sources. One major feature of this new system is the inclusion of native support for the Open Data Cube environment. The system is open source, meaning that the algorithms and code are openly available to researchers anywhere in the world. The structure of the code enables researchers to utilise their own, regionally specific methods to build land cover products tailored to their own needs, while still adhering to the overall UN FAO LCCS framework. It can be run using data from a range of sensors including multispectral optical, radar, LIDAR, as well as custom georeferenced datasets.

This method is currently being used operationally by both DEA and Aberystwyth University to create national land cover products. For Australia, DEA has generated DEA Land Cover, a high resolution (25 m) continental, annual land cover map for each year from 1988 to 2020 by utilising over 30 years of Landsat sensor data. This data is being utilised in Australia’s environmental economic accounting, and is providing valuable insights to researcher and decision-makers. Similarly, Aberystwyth University has worked with DEA and Welsh Government through the Living Wales project to generate national land cover maps for Wales over for four years (https://wales.livingearth.online/) using multiple sensors including Sentinel 1 and Sentinel 2 and is currently extending the time-series back to the mid 1980s using Landsat sensor data (Lucas et al., 2018). In addition, several research bodies across the globe have begun a community of practice to share ideas, algorithms and support each other in implementing this land cover methodology in their own Open Data Cube environments.

References:

Lucas, R.M.; Mitchell, A. Integrated Land Cover and Change Classifications. In The Roles of Remote Sensing in Nature Conservation: A Practical Guide and Case Studies; Díaz-Delgado, Lucas, R., Hurford, C., Eds.; Springer: Cham, Switzerland, 2017; pp. 295–308.

Lucas, R., Bunting, P., Horton, C., 2018. Living WALES — National Level Mapping and Monitoring Though Earth Observations, Ground Data and Models. IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium. Presented at the IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 6608–6610. https://doi.org/10.1109/IGARSS.2018.8519452

Owers, CJ, Lucas, RM, Clewley, D, Planque, C, Punalekar, S, Tissott, B, Chua, SMT, Bunting, P, Mueller, N & Metternicht, G 2021, 'Living Earth: Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development', Big Earth Data. https://doi.org/10.1080/20964471.2021.1948179

High quality training data is crucially important for any land cover/ land use mapping. There are different sources of training data available, including on-ground observations and visually interpreted very high resolution images. Additionally, while creating new maps, already existing land cover/land use maps are being used to extract more training samples. Here, we have explored impact of using various sources of training data on quality of the World Cover land cover map at 10m resolution. The key input used for developing the 2020 World Cover map at 10m resolution was the 2015 CGLOPS data set collected through the Geo-Wiki engagement platform. A specific branch of Geo-Wiki (http://geo-wiki.org/) was developed for collecting reference data at the required resolution and grid (PROBA-V UTM 100 m pixels). It showed the pixels to be interpreted on top of Google Earth and Microsoft Bing imagery, where each pixel was further subdivided into 100 sub-pixels of 10 m x 10 m each, in line with the Sentinel 2 grid. Using visual interpretation of the underlying very high resolution imagery, experts (a group of people trained by International Institute for Applied Systems Analysis staff) interpreted each sub-pixel based on the land cover type visible, which includes trees, shrubs, grassland, water objects, arable land, burnt areas, etc. This information could then be translated into different legends using the UN LCCS (United Nations Land Cover Classification System) as a basis. While this data set was a very good input for mapping land cover at 100m resolution, it was a bit noisy at 10m pixel level due to the shifts of underlying images used for visual interpretation as well as due to land cover changes happened between 2015 and 2020. This was critical for mapping highly heterogeneous areas, such as savannas, with mixed woody vegetation, grasslands, and bare land. We’ve worked on optimizing the training data set and we would like to present our approach to improving the training data quality at 10m resolution for mapping heterogeneous areas, and also present our iteration approach for improving maps quality by using spatial accuracy. Finally, we would like to discuss the requirements for training data collection with a particular focus on highly heterogeneous landscapes.

Semantic segmentation with convolutional neural networks (CNN) has proven an effective method for accurate land use/land cover classification with high-resolution satellite imagery in recent years (Carranza-Garcia et al., 2019, Scott et al., 2017). However, CNNs need full coverage of training labels over input features. Generating such training label data is time-consuming and expensive, which has limited this technique’s applicability for continental-scale mapping efforts.

The LUISA Base Map 2018 (Pigaiani and Batista E Silva, 2021) provides a Europe-covering land use/land cover (LULC) database for the year 2018, distinguishing 46 LULC classes in a hierarchical legend at 50m resolution. The LUISA dataset is the result of combining several human-annotated LULC datasets of Europe for 2018, and therefore presents a unique opportunity to train a CNN to classify LULC for the entirety of continental Europe, potentially in other years than the source year of its training data.

We explored the potential of the LUISA Base Map for deep learning-based LULC classification at multiple levels of spatial and thematic resolution. To do this, we trained UNET-based CNNs on 30m Landsat, 10m Sentinel-2, and 3m Planet satellite imagery of multiple regions in Europe, recorded in 2018. We did this for each of the four levels in the LUISA legend hierarchy (5, 14, 41, and 46 classes), resulting in 12 models. After training, each model was used to classify LULC in a left-out set of European sample locations in 2018 and 2015.

Our accuracy assessment consists of a validation on LUISA data and satellite imagery from 2018 for a number of left-out regions, as well as a cross-reference to all observations from the Land use and land cover survey (LUCAS) of both 2018 and 2015. We also calculated the agreement between the LUCAS observations of 2018 and the LUISA Base Map itself to provide an objective estimate of the maximum attainable accuracy of this validation method.

Our objective was to train a model that can reproduce the LUISA basemap on left-out data, and achieve a similar score on LUCAS validation points from 2015 and 2018. Initial results suggest that the unprecedented spatial and thematic resolution of the LUISA basemap can be reproduced for other years without requiring the costly efforts of creating and combining its component datasets for each target year.

References
Carranza-García, M.; García-Gutiérrez, J.; Riquelme, J.C. A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks. Remote Sens. 2019, 11, 274. https://doi.org/10.3390/rs11030274

Pigaiani, C. and Batista E Silva, F., The LUISA Base Map 2018, EUR 30663 EN, Publications Office of the European Union, Luxembourg, 2021, ISBN 978-92-76-34207-6 (online), doi:10.2760/503006 (online), JRC124621.

Scott, G. J. and England, M. R. and Starms, W. A. and Marcum, R. A. and Davis, C. H.,"Training Deep Convolutional Neural Networks for Land–Cover Classification of High-Resolution Imagery, in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 4, pp. 549-553, April 2017, doi: 10.1109/LGRS.2017.2657778.

About half of Germany's total land area is used for agricultural production, and about one third is forested. Gathering detailed information on the land cover of these landscapes is of great importance for their ecological and economic valuation. This could improve, for example, the estimation of ecosystem services such as pollination, the quantification of nitrate and nutrient inputs into water bodies and the determination of forest conditions in times of climate change. Forests in particular play a central role in environmental impact assessments for large infrastructure projects. However, spatially explicit information on on tree species and the conservation value of forest types is missing.

High temporal and spatial resolution satellite data of the Copernicus mission allow continuous monitoring of plant dynamics on the land surface. Optical remote sensing is suitable for capturing the spectral characteristics of plant species. Using time series analysis, plant species can be distinguished based on their different phenology. However, approaches are needed to deal with cloud contaminated data and to take into account regional biogeographical conditions when classifying plant species at national level.

In this context, we present the classification approach APiC that has been used to produce maps of agricultural crops and tree species. APiC is a highly automated, data-driven machine learning approach to land cover classification that works dynamically at pixel level. The thematic dimension (definition of the land cover classes) of the training data is determined solely by the algorithm, as is its temporal dimension (time periods for which sufficient cloud-free pixel observations are available). For land cover prediction, a large number of classification models are computed to take the individual cloud cover at pixel level into account. In this way, model performance can be specified for each pixel to provide a more detailed insight into the accuracy of the classification. It follows that with APiC neither cloud-free image mosaics are created nor are artificial reflectance values generated for gap filling.

First, APiC was used to classify crops across Germany based on Sentinel-2 data from 2016. LPIS data served as reference data for training the machine learning algorithm ‘random forest’ and for validating the results. The different growing conditions in Germany were taken into account by carrying out the classification independently in six landscape regions. With an overall accuracy of 88%, a total of 19 crop types were classified, namely winter wheat, spelt, winter rye, winter barley, spring wheat, spring barley, spring oat, maize, legumes, rapeseed, leeks, potatoes, sugar beets, strawberries, stone fruits, vines, hops, asparagus and grassland (www.ufz.de/land-cover-classification). Agricultural crops for subsequent years (2017-2020) are currently being mapped using the same routine. Based on time series analyses, the effect of crop rotation and landscape configuration on pollination performance of bees can be better estimated.

Secondly, the main tree species in Germany were classified using forest inventory data and Sentinel-2 data from 2015-2017. Pine, larch, spruce, Douglas fir, oak, beech, hornbeam, alder and willow were classified with an overall accuracy of 76,6 % across three landscape regions. Due to the heterogeneity of forests and the design of the reference data/inventory data collection, the classification of tree species turned out to be more challenging than that of crop plants. We used the tree species classification map together with information on the potential natural vegetation of Germany, the Red List status of forest types and the canopy height (derived from high-resolution LiDAR data) to determine a conservation value of forested areas. Provided by the Federal Agency for Nature Conservation, the potential natural vegetation represents the vegetation of Germany as it would exist under current climate and soil conditions without human influence. This information was essential to assess the tree species classification map from a nature conservation perspective. Our approach does not take into account other important nature conservation aspects such as deadwood occurrence and undergrowth of the forest. However, through the use of remote sensing, a preliminary conservation assessment of forests could be made at the national level.

The climate is changing faster than planned, emergencies and crisis are in the news everyday. In parallel, population is growing, exchanges are across the world, the economy needs to develop. In this context, we need to keep the balance to protect our planet as a resource and a patrimony. Sustainable development and green transition is at the heart of this nexus. And The Green Deal is the Commission action plan in this long term endeavour.

With Copernicus, we have been collectively visionary decades ago through its public services to monitor the planet and support green economic development. However, we need to go beyond as the situation is evolving, because policies need to be science and evidence based more and more and we need to act quickly. Therefore we have the opportunity to benefit for transformative changes within the context of the dual digital and green transition.

Destination Earth (DestinE) is a Commission flagship initiative aiming to develop gradually over the next 10 years, a highly accurate digital model of the Earth (a digital twin of the Earth) to monitor, simulate and predict natural and human activity, and to develop and test scenarios for more sustainable development and for achieving both the green (Green Deal) and digital (Digital Strategy) priorities of the EU.

Leveraging on EU’s substantial investments and activities in high-performance computing (HPC), Artificial Intelligence (AI), cloud computing, high-speed connectivity networks, data from multiple sources (space, in-situ, socio-economic data) and by bringing together European scientific and industrial excellence to achieve the objectives of the initiative. DestinationEarth will be as well a new way to interact with users and consider individual usages through user-defined scenarios-building including impact sectors, and considering new forms of cooperation and co-design for best practices.

DestinationEarth has to be understood first as an operational and trustable portfolio of digital services, applications and tools to create content, to support decision-making including in extreme situations, anticipate environmental disasters and resultant socio-economic crises to save lives and avoid large economic downturns.

The presentation will illustrate these concepts and how DestinE will build on the capacities provided among others by a digital Copernicus and act in synergy. The implementation of the two programmes will benefit from powerful synergies while at the same time, both initiatives will maintain their focus on their respective, distinct, scope of work and related activities.

The rapid growth of Earth Observation (EO) data, especially in the last decade, combined with the ever-increasing development of analytical theories and tools, has generated a wide range of practical applications covering land, maritime, climate change and atmospheric monitoring. The comprehensive and systematic structuring and processing of this data and their transition to valuable knowledge has helped us model and predict natural processes, improve Atmospheric, Marine and Land monitoring as well as understand the complex dynamics of our planet’s environment.
Current EU data services and repositories offer EO data of staggering volume and diversity. However, data access and use are yet to fully spread beyond EO experts and scientists to the wider industry. EO data services contribute to the European Green Deal data space, facilitating data flows inside the EU and across sectors, for the common good. European programs like Copernicus, Galileo, EGNOS and INSPIRE already provide a significant amount of invaluable EO data that is currently being used by many organizations and SMEs to deliver their value-added services.
In this presentation, we will be discussing some existing projects and initiatives offering cloud infrastructures for processing Copernicus data and information like DIAS (Data and Information Access Services) and the ECMWF and EUMETSAT European Weather Cloud infrastructure for processing meteorological and satellite data close to their physical location. DestinE, the new EC coordinated initiative implemented by ESA, ECMWF and EUMETSAT will be utilising diverse HPC and Cloud infrastructures and distributed cloud-based storage infrastructures to provide access and tools for processing EO data., Finally, we will be discussing some related EC Research and Innovation Action (RIA) projects like AI4EU, AI4Copernicus and eo4eu. Their key objectives are to provide the means and tools for implementing technologies based on AI/ML, AR/VR and semantic-enhanced knowledge graphs augmenting the FAIRness of EO data and support a sophisticated representation of data entities and their dynamics.

We will talk about ESA and its plans in EO, why space EO is a key to European space economy growth and to understand and tackle climate changes. Transformative technologies that we explore and develop at the ESA ɸ-lab@ESRIN, such as Artificial Intelligence, Could and Edge computing, are instrumental to this and transforming the way we’ll use EO.

Based on CloudFerro experience from developing and operating DIAS and DIAS-like platforms, we will discuss the challenges, implemented technology solutions and future trends regarding Copernicus platforms. It will cover such important topics like:
- federation of data, resources and users
- renewable energy for processing
- cloud computing vs HPC for big EO processing
- expansion from EO data to all spatial information
- semantic layers supplementing data
- optimization of storage, computing and network consumption in federated environment

In the current Big Data and EO architectures which are utilising various EO and non-EO data sources, as well as machine learning and Artificial Intelligence tools, there is an emerging need to establish “trust” mechanisms by ensuring end-to-end data and software traceability, auditability and provenance chain. Moreover, the “EU Ethics Guidelines for Trustworthy AI”, which is a part of EU AI strategy, requires the transparency and explainability of elements relevant to the AI-driven systems: the data, the system and the processing models, as well as that the decisions made by an AI systems can be understood and traced by human beings. As such, for AI to be “trustworthy”, state the Guidelines, we must be able to understand why it behaved a certain way and why it provided a given interpretation. Today, this is still an open challenge thus a whole field of research, called Explainable AI (XAI) tries to address this issue to better understand the system’s underlying mechanisms and find solutions.
What are the implications of these requirements for EO and Copernicus data and applications? Do we need to develop new tools that can ensure the provenance of the datasets (origin and history of modifications) as well as systems for tracking of the AL/ML model training processes?

This presentation will focus on the EO data provenance which is a new concept that allows to track the EO data along the digital supply chain and capable to provide in the end-to-end traceability of data, software tools and models. In particular, the EOGuard project will be presented to showcase the use of KSI blockchain technology for verification of the “authenticity, integrity and time” of EO data products and their traceability throughout different cloud infrastructures, including results of the “proof of concept” implementation on the Copernicus DIAS cloud for the Common Agriculture Policy use cases. The question of how a similar technology can be used for auditing and certification of AI driven EO applications will also be discussed.

The European Space Agency (ESA) Arctic Weather Satellite (AWS) currently planned for launch in 2024 could be a pathfinder mission in expanding the support of the EUMETSAT Polar System Second Generation (EPS-SG) Microwave Sounder (MWS) mission to global and regional numerical weather prediction (NWP) applications.
A successful AWS in-orbit demonstration in the 2024-2025 period would represent an opportunity for EUMETSAT to expand the products’ envelope of the EPS-SG mission for the users, by considering the implementation of a constellation of satellites with microwave sounding capability based on the AWS design.
ESA and EUMETSAT are cooperating on the preparatory activities that could possibly lead to a constellation of flying AWS recurrent models, providing sounding information on global humidity and temperature profiles to the users in near real time. The Phase A activities for the constellation definition are currently ongoing at EUMETSAT, and include various complementary scientific impact assessment studies, performed by CNRS/Météo France, the European Centre for Medium-range Weather Forecasts (ECMWF) and by a Consortium led by Met Norway (MetNO), including the Swedish Meteorological and Hydrological Institute (SMHI) and the Finnish Meteorological Institute (FMI). All studies are considering the same constellation scenarios, in terms of orbits, number of satellites, and instrument sampling.
The main goal of the various studies is to assess the impact of various possible constellation designs on NWP using different methodologies and focussing on different, but complementary aspects. CNRS/Météo France is performing a series of Observing System Simulation Experiments (OSSEs) considering a realistic representation of the global observing system, with focus on global NWP impact. Forecast lead times are considered over the range of few days.
ECMWF is performing an Ensemble of Data Assimilations (EDAs) series of experiments, assessing the benefit of the various constellation scenarios by measuring the reduction in variation across different forecast ensemble members, with focus on global NWP. The EDA methodology requires only data from the various constellations that need to be simulated, including a realistic observation error. Focus is on the short term forecast error reduction.
An additional study, performed at MetNO, SMHI and FMI, includes a series of regional OSSEs aiming at estimating the expected impact of the selected constellations scenarios with focus on regional NWP at high latitudes. Other important aspects to be investigated include the support of Nowcasting (NWC) and a societal impact assessment for high latitude regions and the Arctic.
This presentation describes the objectives and status for each of these studies and gives an outlook of the various future activities to support the definition of the AWS constellation.

The Arctic Weather Satellite (AWS) aims at paving the way towards a constellation of satellites, carrying a microwave instrument each, to give input to weather forecasting with short revisit times. However, already the prototype satellite will provide a novel element, as it is equipped with four channels around the 325 GHz water vapour transition. Existing operational microwave radiometers are limited to frequencies below 200 GHz. The upcoming Ice Cloud Imager (ICI) mission will also cover this range. Presently AWS has an earlier planned launch date than ICI and the later instrument will only have three 325 GHz channels. In addition, ICI is a conically scanning instrument with a footprint of 15 km while AWS is a cross-track scanner with a resolution around nadir of about 10 km.

For clear-sky conditions, AWS' 325 GHz channels will provide a somewhat improved precision for humidity, by complementing the channels around the 183 GHz transition. There is little information on surface emissivities around 325 GHz, and it is hard to judge if the addition of this frequency range can help constrain atmospheric humidities when the 183 GHz channels are disturbed by the surface. This happens in dry conditions and at high surface elevations. In the presence of clouds, there is a much more clear synergy between the two sets of channels. In assimilation of clear-sky character, 325 GHz should be the ideal complement to filter out cloud affected 183 GHz data. In simulations, this approach has also been shown to clearly outperform existing filtering methodologies. In fact, a significant fraction of the cloud-affected 183 GHz radiances can be corrected to create synthetic cloud-free counterparts with help of the 325 GHz channels. This indicates that the combination of 183 and 325 GHz allows to constrain humidity below and inside a broader range of clouds than what is now the case with just 183 GHz at hand. Assimilation of all-sky type should be the optimal manner to make use of this synergy between 183 and 325 GHz.

The information on ice hydrometeors provided by the 325 GHz channels has also inherit value, in line with the objective of ICI. Fewer cloud variables can be constrained by AWS, as ICI has additional channels further into the sub-mm range, up to 664 GHz. Still, AWS's partly smaller footprint and varying incidence angle can be beneficial for special studies, e.g. to verify assumptions on ice particle shape and orientation, and it can thus indirectly support ICI.

Context: The arctic region is poorly served by geostationary observations. The arctic weather satellite (AWS) will provide frequent coverage of the polar regions to support nowcasting and numerical weather prediction. The AWS Prototype Flight Model (PFM) satellite is the forerunner of a potential constellation of sixteen satellites that would supply a constant stream of temperature and humidity data.
Instrumentation: Onboard the AWS four receivers (19 channels) will perform passive remote sensing of the atmosphere with frequency coverage between 50 and 325 GHz. Radiometer Physics GmbH is in charge of developing and building the 325 GHz Front-End featuring 4 channels especially designed for humidity sounding and cloud detection. It relies on extensive heritage from the MetOp-SG Ice Cloud Imager receiver developments, but considers the New Space approach to prove innovative integration concepts in a cost-effective and timely manner. The 325 GHz receiver integrates in three aluminum based modules, all DC and RF functionalities that were previously separated (ICI), allowing for strong mass and size reductions.
New Space Approach: The involvement of private companies, like RPG, and investors in the commercial space sector has led to the “New Space” development. The most significant characteristics of the “New Space” approach include the use of new production methods, the incorporation of new cutting-edge technology, modularization and standardization, and the use of commercial off-the-shelf parts, as well as the willingness to take on higher risks while still remaining flexible enough to react quickly on customer needs. This approach enables us to incorporate novel business ideas in designing and manufacturing while still save development time and reduce costs.
Here, we will present the 325 GHz AWS Front-End with special focus on the chances and implications that New Space has on its design, development and manufacture.

The ESA Raincast study is a multi-platform and multi-sensor study to address the requirement from the research and operational communities for global precipitation measurements. It aims at identifying and consolidating the science requirements for a satellite mission that could complement the existing space-based precipitation observing system and that could optimally liaise with efforts currently made by other agencies in this area. One objective in the study is to provide criteria and guidelines in the design of future missions dedicated to global snowfall quantification.
Improvement in both the monitoring of high latitude precipitation and in our understanding on microphysical and dynamical processes that influence high latitude precipitation patterns, intensity and type must be driven by concerted observations of active radars and passive microwave radiometers. This has been recently demonstrated through the development of machine learning-based algorithms for snowfall detection and retrieval, exploiting global observational datasets built from passive and active microwave spaceborne sensors. In particular, the CloudSat/Calipso-based machine learning snowfall retrieval methodology developed for the GPM Microwave Imager (GMI) (SLALOM), which has been developed within the EUMETSAT Hydrology SAF in preparation for the EPS-SG Microwave Imager (MWI) mission, has proven to be very suitable for snowfall detection and retrieval. SLALOM is able to reproduce CloudSat snowfall climatology, but with better coverage (up to 65°N/S for GMI), outperforming other state-of-the-art GPM products.
The increasing number of operational cross-track scanning radiometers in the future (e.g., EPS-SG Microwave Sounder (MWS) mission) requires dedicated efforts to study the potentials of these radiometers to improve snowfall global monitoring. Moreover, the Arctic Weather Satellites (AWS) mission, carrying a cross-track scanning microwave radiometer covering the frequency range 50–325 GHz, will provide unprecedented spatial and temporal coverage at high latitudes. In this context, SLALOM has been recently adapted and applied to the currently available most advanced cross-track scanning radiometer, the Advanced Technology Microwave Sounder (ATMS), on board Suomi NPP, NOAA-20 and the future JPSS platforms. A dedicated study has been carried out to assess ATMS snowfall observation capabilities at high latitudes. The study is based on the use of a ATMS/CloudSat-Calipso coincident observation dataset. The main findings from the study will be presented by: 1) reporting on the different scientific aspects and on the complexity related to snowfall detection and quantification in extreme dry/cold conditions (e.g., sea ice/snow cover variability), 2) analysing and providing evidence of such complexity, and 3) proposing observation and retrieval strategies to be adopted in the future to improve detection and quantification of snowfall in the Arctic, also in view of the AWS mission. These findings pave the way towards the definition of synergistic approaches exploiting the future European AWS, EarthCare and CIMR missions.

The Meteorological institutes of Denmark, Finland, Norway, and Sweden have a long history of working together to advance limited-area NWP in conditions typical to these Nordic countries. The maintenance of the state-of-the-art operational NWP systems is currently taking place in the HARMONIE-AROME framework with close links to Meteo-France and several other European National Weather Services. The development of variational data assimilation in these mesoscale NWP systems facilitates efficient exploitation of satellite measurements. In anticipation of the new polar orbiter launches within the next 2-4 years, preparations are underway for the assimilation of radiances from the Arctic Weather Satellite (AWS).

AWS is designed as a prototype satellite to demonstrate the feasibility of low-cost microwave sounding from a low-Earth orbit. A successful demonstrator mission will pave the way for a constellation of satellites, thereby providing frequent data reception over high latitude regions. The AWS demonstrator mission is scheduled for launch in 2024. While the AWS satellite has a design-life time of 5 years the ESA mission is committed only for one year of operation.

Under ESA contract a Nordic Consortium has committed to make an early performance evaluation of the AWS data, with emphasis on regional NWP and high latitudes. The project is to be kicked off in December 2021 and will finish one year after launch. To be able to do a meaningful evaluation in this very short time frame, significant research and development efforts are required to be able to bring the Nordic NWP systems ready to digest and optimally use the AWS data by the time of launch. In addition this study will also try to assess what impact a possible future AWS constellation, providing an observation frequency over the Nordic region of upto and possibly beyond 1 hour, might have on short term weather forecasting and Nowcasting.

In all Nordic Meteorological Services, microwave sounding data assimilation is performed already in clear sky conditions. The Nordic ESA study will start to monitor and experiment with active AWS radiance assimilation using both temperature- and humidity-sensitive microwave sounding channels. At the time of launch, the operational use of these frequencies is likely restricted to cloud-free conditions. But substantial efforts are devoted to eventually extending the data use to all-sky conditions and improving the usage over sea ice, land, and snow surfaces. Additionally, research will be undertaken to investigate the potential use of channels in the sub-millimetre wavelengths. For example an enhanced cloud filtering for clear-sky assimilation using the 325 GHz channels will be evaluated.

In order to have access to real-time data with low latency from shortly after launch, a ground segment is set up using the satellite Direct Readout acquisition facilities available within the domain of the Nordic Institutes.

In parallel to the development of the AWS demonstrator mission EUMETSAT is currently conducting Phase 0/A studies for a future constellation of small microwave sounding satellites. This potential AWS constellation is foreseen as an expansion to the EPS-SG programme and is expected to be put forward for decision in 2025. If approved by the EUMETSAT member states the first satellites of this constellation will start flying around 2029. To support the decision process at EUMETSAT a number of dedicated global and regional assessment studies will be performed. In a Nordic study to start early 2022 and finish mid 2023 an Observing System Simulation Experiment (OSSE) over the Nordic/Arctic region will be conducted. In addition, a first look at how AWS data can be used to support Nowcasting (independent of what can be provided by short term regional NWP) will be addressed, and a study will include first steps towards making socio-economic benefit assessments of a future AWS constellation.

Here we will present the development and research plans of the Nordic Consortium to support ESA and EUMETSAT in the evaluation of the performance of the AWS demonstrator mission data and to assess the impact of a possible future AWS constellation, with focus on Nordic regional forecasting.

Observations from microwave (MW) instruments currently provide the greatest impact of the satellite data used in the ECMWF assimilation system. However, with some satellites having already been in flight for many years combined with a lower frequency of new missions, the constellation as it stands now is expected to decline in the coming years. Recent advances in technology have allowed the possibility of launching MW sounding instruments on small satellites with a performance that is expected to be adequate for Numerical Weather Prediction (NWP). These new small satellites are expected to complement a continued backbone of larger, high performance platforms. In this study, which is carried out in collaboration with ESA, we aim to investigate different potential future constellations of small satellites carrying MW sounding instruments with a focus on the optimal design for global NWP. A range of constellations have been chosen where the different designs have been primarily motivated by two aspects. Firstly, by varying the number of satellite platforms up to a large constellation of 20 small satellites, we aim to examine how much further benefit could be achieved with improving temporal sampling. Additionally, the trade-off between humidity sounding channels only or having additional temperature sounding channels will be considered.

The impact of these possible constellations will be evaluated by the Ensemble of Data Assimilations (EDA) method. The EDA consists of running a finite number of independent cycling assimilation systems, in which observations and the forecast model are perturbed to generate different inputs for each member. The small satellite data and accompanying observation errors will be simulated and the benefit from adding the data to the observing system is measured by reducing the spread of the ensemble members which reflects improvement to the uncertainties in analyses and forecasts.

Here we present the EDA methodology and provide an overview of the simulation framework which includes use of operational resolution model fields interpolated to the small satellite observation locations and a scattering radiative transfer model to allow use of the data in an all sky framework. Several EDA experiments will be run in which the different constellations of simulated small satellite data are added to a consistent baseline observing system. This baseline comprises the full observing system but with the number of existing MW sounders reduced to only four satellite platforms - two each in the Metop and JPSS orbits - to reflect a potential reduced constellation of larger platforms in the future. Preliminary evaluation of the first EDA experiments exploring the impact of the small satellite constellations will be presented.

Session C5.02 Scalable platform architectures enabling big EO data analytics in cloud-based platform
Michał Bylicki, Monika Krzyżanowska, CloudFerro
Efficient and scalable computing cloud access to Big Data EO repositories – CREODIAS, WEkEO, CODE-DE and other platforms
Copernicus program provides users with a tremendous amount of EO data available as a public good for anyone at no cost. With an increasing amount of data, we need to provide growing capabilities to process the data and use the information derived from the data. The size of the Copernicus data makes it difficult to process the data in the local environment. To foster the usability of EO data, the DIAS platforms (Data and Information Access Services/EO platforms) were launched.
EO platforms are essential component that enhance the EO data's usability, making it possible to change data into information. The platforms provide users with a computing power that enables fast and efficient processing and allow users to access, process and analyse Copernicus products in the cloud directly connected to the EO archive. The availability of data, storage, and processing in a single place makes it easier to develop and distribute scalable digital services based on the EO data.
We have built and operate several EO platforms based on a hybrid cloud in an Infrastructure-as-a-service model. Our services are based on open-source components, such as Open-Stack for cloud orchestration, Ceph for storage management, Prometheus and Graphana for monitoring, K8s for processing and others. Within this session, we will present the challenges of building cloud platforms with direct access to big data EO repositories. On the example of CREODIAS, WEkEO, CODE-DE and other platforms built and operated by CloudFerro, we will present the main building blocks and architecture of entirely European cloud platforms based on open-source software. We will show how we managed to create a new service - the EO platform as a service. We will as well discuss how such services can become part of the emerging European federated clouds and data ecosystem.
Federation and multi-platform approach allow users and operators to benefit from the synergy between different platforms, such as shared multi PB data repository available from various platforms and elasticity cloud components for additional processing needs. At the same time, it generates a technical challenge that we need to address. We will present several such challenges and outline our approach to face them, focusing (but not limiting to) on three main topics:
• Interoperability of the environments:
how to support easy migration between different clouds and provide direct access to the repositories available from different computing clouds, with multi-cloud processing enabled and dynamically adaptable usage and payment models.
• Data access and dissemination:
the multi-petabyte, scalable EO data repositories should provide fast, instantaneous access to many heterogeneous users and algorithms with different needs. At the same time, the data needs to be discoverable and ready for analysis in a context of constantly evolving data offers (new collections, commercial data, products), and users should be supported in data dissemination.
• Cloud efficiency:
how to optimize data access, storage, and processing costs and energy usage both for individual users and for the entire ecosystem.

In our presentation, we will present our approach to these challenges from the EO platform provider point of view. As an EO platforms provider active in a fast-growing and developing market, we need to constantly add and improve new services as required by the customers in order to compete with the biggest players. It is crucial to provide more advanced services and not to compete with our customers at the same time. We will discuss the perspectives and responsibilities of EO platform providers. We shall complement the presentation with a short discussion of the applicability of computing cloud vs HPC for EO processing. We will terminate with our vision of the emerging federated cloud and data ‘green’ ecosystem.

CLEOS (Cloud Erth Observtion Services) is e-GEOS satellite data and information platform offering access to a wide variety of geospatial datasets and enabling the development, test and large scale deplyment of geospatial data prcoessing pipelines. CLEOS adopts a multicloud approach and fosters interoprability through standards and practixs such as OpenEO for APIs and STAC for metadata catalogue.

High Level Architecture.
CLEOS Architecture is modular and it is based on five main components:
• The Marketplace, where Customers can access available products and services through a user-friendly UI;
• The API Layer, which exposes purchasing, processing and data access capabilities through a RESTful web interface, also enabling machine-to-machine integration with external systems;
• The Processing Platform, which orchestrates the scalable execution of Data Processing Pipelines for both Optical and SAR data, using e-GEOS proprietary algorithms and Third-Party tools. It includes the AI Factory for the management and development of AI-based applications;
• The Data Layer that all entire the metadata catalogue of the federated data sources connected to the infrastructure and a pool of locally available assets and resources, including EO and non-EO data;
• The Help Desk, used by e-GEOS Customer Service to provide the necessary support to CLEOS Customers.

CLEOS accesses multiple satellite and non-satellite data sources through a set of data collectors adapted to the interfaces offered by each data/contents provider (e.g. satellite missions Ground Segments). CLEOS also has a multi-cloud orchestration service that allows the deployment of the whole CLEOS platform or of single processing jobs in different commercial cloud infrastructures, including all Copernicus DIAS.

Design principles and innovations.
CLEOS Platform design adheres to technical design principles widely shared in the geospatial community.
1. Multi-Cloud & Data Locality. Space Earth Observation data are notably very large datasets (e.g. Sentinel-2 satellites acquire about 10 TB of data daily). The “data gravity” associated with these huge data archives requires a shift in the processing paradigm, which means bringing the processing close to the data, to minimize network congestion and increase the throughput of the overall system. This is called the Data Locality principle. However, space and geospatial data are not located in a single infrastructure, since today there are several endpoints offering access to the same datasets (e.g. Sentinel-2 data can be accessed in AWS, Google Cloud and five Copernicus DIAS). Additionally, the selection of the infrastructure where to access and/or process data is driven by multiple considerations: not only price/performance, but also constraints from Customers (including the option to process data in a local infrastructure for certain workloads).
This scenario had the following impact on CLEOS design:
• The Data Catalogue needs to register multiple endpoints where to access the same resource. CLEOS has adopted Spatio Temporal Asset Catalog (STAC) metadata structure, as it allows extensible definition of spatial assets and resources enhancing indexing and discovery process standardization;
• The Processing Platform must have the flexibility to pilot processing requests on different cloud platforms, including on-premises infrastructures, allowing also hybrid cloud workloads. CLEOS has adopted the Max-ICS platform, which exploits open source frameworks such as Mesos, Marathon, Puppet, and Terraform, to manage Infrastructure as a Service (IaaS) resources in multiple cloud infrastructures, abstracting the complexity of the platform control and service orchestration layers.

2.Elasticity&Scalability. Geospatial data processing use-cases can define a great variety of workloads types, from large batch processes in case of multi-temporal analysis over large areas, to synchronous data analytics requests of newly acquired data, or even stream processing. The simultaneous management of such heterogeneous workloads requires a strong optimization of resources usage and deployment. This scenario requires CLEOS to be able to scale up and down the available worker nodes, according to the active workload in an elastic way. CLEOS infrastructure is based on microservices, fragmenting complex workflows into elementary steps that can executed by independent nodes and easily orchestrated. Therefore CLEOS infrastructure can dynamically scale-up those microservices to fulfill the increasing number of processing jobs, deploying ultimately new on demand virtual hosts exploiting the elastic resources allocation made available by almost all cloud providers.

3.Microservices and Data Processing Pipelines. In CLEOS all the processing services are made available by nodes and pipelines. Nodes (microservices) and pipelines (set of nodes linked to each other to perform a workflow) are the two main components upon which to build a collaborative ecosystem in which CLEOS developers can reuse available standalone blocks to create new services with a modular LEGO logic. The definition of Data Processing Pipelines is central in modern platforms, as it allows the design and implementation of a workflow that is activated once data reach the first node of the pipeline, flow through it to produce a result that can be used as the input of another pipeline or be delivered to the user.

4.Platform Federation & Interoperability. Today, a platform cannot behave as a standalone system, but it needs to be interoperable and federated with other platforms on the two sides of the market:
• Supply: the platform needs to be able to access to several heterogeneous suppliers of data and services;
• Demand: the platform needs to offer its data and services to heterogeneous customers that, more and more often, will be other platforms and not humans.
To achieve this objective, the API layer plays a central role. In particular, CLEOS has adopted also the OpenEO standard to manage the end-to-end process of searching, configuring, buying, monitoring and accessing data and services. This choice was made to enable the OpenEO clients to connect with CLEOS with minimal effort. Through this API definition, it is possible to unify the access to the different service backends, abstracting proprietary implementations made by each vendor with their own internal interfaces.

CLEOS Data Layer
CLEOS Data Layer is a set of modules dedicated to the storage and cataloguing (Technical Catalogue) of available resources, being them data, metadata or capabilities available through the Processing Platform. The CLEOS storage relies on available Object Storage services in different cloud infrastructures, since most of data collections are available at different endpoints. Therefore, the Technical Catalogue in the Data Layer has a paramount importance, insofar it allows the Marketplace and the Processing Platform to have a unique point of reference about what resources/services are available and how/where they can be accessed. Those catalogues follow the STAC (SpatioTemporal Asset Catalog) specification. The aim of STAC is to define a common language to describe a range of geospatial informations, so they can more easily be indexed and discovered. CLEOS users/developers will take all the benefits of this adoption that prevents them from writing new code each time a new data set or service is available. The Data Layer also offers methods and interfaces (API in the OpenEO standard) to access available data in multiple ways and for multiple purposes (View, Download, Subset, …)

CLEOS Processing Platform, Developer Portal & AI Factory
CLEOS Processing Platform is the module that is responsible for execution of all processing tasks, from the simple retrieval and delivery of a product up to the orchestration of complex and long batch processing jobs involving thousands of processing nodes. The Processing Platform operates and deploys the Processing Pipelines created in the Developer Portal. The Developer Portal is the environment where e-GEOS and external developers can build new Processing Pipelines re-using available modules and building blocks, taking advantage of an Interactive Development Environment (IDE) and of CLEOS API to streamline data access and processing operations

The Processing Platform is able to:
• manage the provision of the necessary infrastructure resources in a dynamic way with elastic scaling up & down in multiple cloud and on premise infrastructure;
• manage the processing pipelines using a data driven and message-based approach where requests are queued and progressively managed, enlarging or reducing the size of the available worker nodes according to the demand;
• manage DevOps through a Continuous-Integration/Continuous-Development (CI/CD) pipeline • monitor the resources usage, node by node, pipeline by pipeline and infrastructure wise
• centralize all the microservices logs so that they can be accessed, filtered and analysed cost-effectively While the processing jobs follow as a stream due to backend event based architecture, CLEOS Processing Platform offers also the capabilities to retrieve and control the overall status of a request managing also customers notifications and updates.

At last, the AI Factory is the platform section dedicated to the development and management of AI models and corpus, where AI developers and users work together to develop, test and scale new AI based applications.
The AI Factory allows:
• to access a large set of pre-defined AI models or to import custom ones;
• to import training corpus or to build new ones using a simple and intuitive interface;
• to train, re-train models, benchmark performance metrics and manage model versioning;
• to directly include trained AI models into processing pipelines via their relative inference nodes.

Capturing video from satellites is one of the most exciting innovations to hit the remote sensing world in recent times. High-resolution, full-colour EO video is enabling fundamental and disruptive changes for the Geospatial Intelligence and Earth Observation industries. When EO video analytics are combined with complementary geospatial information sources, the results can generate powerful information for end users.

EO based video delivers a new dimension of temporal information that captures instantaneous motion on and above the Earth’s surface. This allows determination of direction, speed and rate of change of moving targets on or above the ground. This is a novel observational capability that can potentially change the way we view our planet by enabling new types of analyses and could drastically improve situational awareness, critical decision-making and improved forecasting.

Earth-i and CGI are currently developing the EO Video Analytics and Exploitation Platform (VANTAGE), funded by the European Space Agency, to promote and enable the widespread exploitation of EO video. CGI provides unparalleled expertise in the development and deployment of Exploitation Platforms, whilst Earth-i has a proven track record of delivering powerful analysis of EO imagery and video, using advanced analytics and Artificial Intelligence.

The ultimate aim of VANTAGE is for users to be able to upload their own data and algorithms and use the platform for processing and interrogating that data in conjunction with EO video, and export the results back to their own working environments. VANTAGE is being built using a cloud-based EO platform architecture that enables scalable processing and analytics, tailored for large-volume satellite video data. The platform provides an archive catalogue of over 280 full-colour high-resolution EO videos for the users to interrogate, and a library of predefined analytics tools that are tailored to extract value from EO video, alongside Jupyter Hub integration for collaboration and interactive development of customised user workflows.

To showcase the types of capabilities that the platform can offer, Earth-i and CGI have been developing a number of use cases, including topics such as deforestation, urban sprawl and construction monitoring. Example workflows are being deployed, showing potential users from research, commercial business or public sector organisations how to extract unique information from EO video. A coding challenge was held in November 2021 and another is planned for February 2022, the results of which will be presented.

In this presentation, Earth-i will provide an overview of VANTAGE and its leading edge architecture and functionality, and we will demonstrate some of the key functionality, showing how users can extract cloud-free data from an EO video, or identify and track moving objects in the videos, or construct a 3D model from the video data. We will show how to use EO video data in combination with traditional satellite data acquisitions to derive higher level information products.

In recent years, advances have been made for users e.g., in Marine Safety to make Earth Observation (EO) data available shortly after data capture. However, it requires considerable infrastructure that is usually not available to the wider community. Near real-time (NRT) exploitation of EO data requires reducing the time from sensing to actionable information, including downlinking, processing and delivery of imagery and value-added data. Full utilization of the NRT potential must therefore include optimal exploitation of satellite capabilities, ground station availability, a flexible and scalable processing environment and reducing the necessary computations by intelligent data selection early in the pipeline (for example based on a user’s area of interest).

The ESA funded EOPORT project implements a cloud native subscription-based NRT exploitation platform for EO data, demonstrating the concept with live Sentinel-1 data. Today, users obtaining Sentinel-1 data on the Copernicus Open Access Hub or the national ground segments are used to getting data within 3 hours (NRT-3h) or 24 hours (NRT-24h). However, over Europe Sentinel-1 is often operated in passthrough mode (NRT-Direct), in which data is downlinked to a ground station directly following sensor data capture.

Exploitation of passthrough data have traditionally been limited to ground station providers. EOPORT offers this capability by providing a low-cost entry to exploiting true NRT data in a public cloud environment. The capabilities complement and can be federated with existing platform initiatives based on published EO products, such as the Thematic Exploitation Platforms (TEPs) and the Data and Information Access Services (DIASes).

The authors will demonstrate the state-of-the-art architecture, scalable processing and pilot achievements, demonstrating downlinking of passthrough Sentinel-1 data at KSAT’s ground station in Tromsø, making raw data chunks available within seconds on T-Systems’ Open Telekom Cloud, producing Level-1 data with Kongsberg Defence & Aerospace’s NRTSAR processor within just a few minutes after sensing. The highly scalable implementation on a Kubernetes cluster ensures that any processing stage is stated and run in parallel, as soon as sufficient measurement data and auxiliary data is available.

There are many web-based platforms offering access to a wealth of satellite earth observation (EO) data. Increasingly, these are collocated with cloud computing resources and applications for exploiting the data. Users are beginning to appreciate the advantages of processing close to the data, some maintaining accounts on multiple platforms. The ‘Exploitation Platform’ concept derives from the need to process an ever-growing volume of data. Rather than download the data, the exploitation platform offers a cloud environment with hosted EO data and associated compute and tools that facilitate the analysis and processing close-to-the-data.
The Users benefit from the data proximity, scalability and performance of the cloud infrastructure – and avoid the need to maintain their own hardware. The Infrastructure Providers gain an increased cloud user base. The Data hosted in the cloud infrastructure reaches a wider audience.
In order to fully exploit the potential of these complementary resources we anticipate the need to encourage interoperation amongst the platforms, such that users of one platform may consume the services of another directly platform-to-platform. This leads to an open network of resources, facilitating easier access and more efficient exploitation of the rapidly growing body of EO and other data.
Thus, the goal of the Common Architecture is to define and agree a re-usable exploitation platform architecture using open interfaces to encourage interoperation and federation within this Network of Resources. Interoperability through open standards is a key guiding force for the Common Architecture:
• Platform developers are more likely to invest their efforts in standard implementations that have wide usage
• Off the shelf solutions are more likely to be found for standards-based solutions
The system architecture is designed to meet a set of defined use cases for various levels of user, from expert application developers to consumers. The main system functionalities are organised into high-level domain areas: 'User Management', 'Processing & Chaining' and 'Resource Management'.
We are developing an open source Reference Implementation, to validate and refine the architecture, and to provide an implementation to the community.
Our solution comprises a OGC API Processes engine that uses Kubernetes to provide an auto-scaling compute solution for ad-hoc analytics, systematic and bulk processing - supported by a hosted processor development environment as an assist to expert users.
Data and applications are discovered through a resource catalogue offering STAC, OGC CSW & API Records, and OpenSearch interfaces. A user-centred workspace provides catalogue and data access interfaces through which the user exploits their own added value products.
For platforms to successfully interoperate they must federate user access in order for requests between services to respect the user's authorisation scope and to account for their actions. Access to resources is secured by means of our identity and access management framework, which uses OpenID Connect and User Managed Access standards to enforce all access attempts in accordance with configured policy rules.
This presentation will highlight the generalised architecture, standards, best practice and open source software components available.

Academic Technology transfer (TT) is an important source of innovation and economic development. The TT success depends on quick and easy adoption of a scientific innovation by an external partner. At FERN.Lab, Helmholtz innovation lab for TT, we use serverless Micro-Service Architectures (MSA) as the main structural style for successful development and transfer of Earth Observation (EO) technology.

MSA allows us to quickly bootstrap from a research prototype to a product, which fits the requirements of different customers ranging from public governmental sectors to private companies, in a question of months. The adopted architecture is easy to integrate into existent workflows, suitable for agile project management, understandable and reusable to facilitate collaborations, and modular to take advantage of new technologies. The latter allows us to integrate various Geo-spatial processing services which contribute to the overall functionality.

At FERN.Lab we believe that TT activities can only have large impact if innovation is continuously integrated into the product development cycles. An MSA allows us to isolate the work of a scientist and it abstracts the scientist from the complexity of an infrastructure such as a cloud. Such isolation combined with the computing environments offered by commercial cloud providers (Google, Amazon, Microsoft) makes it possible to leverage cloud main features without requiring both users and developers a deep knowledge on distributed computing, and thus let the scientist to solely focus in the core of the technology.

The ecosystem of computation environments is rich, Amazon provides AWS Lambda and Fargate, Google offers App Engine and Cloud Run, etc. Besides compute environments for micro-services, cloud providers, such as Amazon and Google, also have large archives of remote sensing data, such as Sentinel-2 and Landsat, which provide easy and efficient access to terabytes of data. Via standard catalog specifications, such as the SpatioTemporal Asset Catalog (STAC), users easily identify which blocks need to be downloaded, or in case of Cloud Optimized GeoTiffs (COGs), streamed for processing. Due to this easy and efficient access to data, micro-services can run in stateless containers and thus, open doors for serverless micro-services.

We present exemplary the use of MSA for habitat classification, land cover prediction, and land surface temperature homogenization. Our work shows the benefits and challenges in accessing and processing large EO data sets from different satellite sensors, Sentinel-1 and -2, Landsat-7, -8, and -9, and ECOSTRESS using different compute environments on Google Cloud. For each of the use-cases, the scientist algorithm is containerized and registered to either be deployed as a micro-service or as a function. For easy portability and interoperability, the algorithms interface is a WebAPI.

The algorithm needs to be stateless and its memory- and storage-footprint should not exceed the resources of the computation node. Despite the amount of resources available for an event driven computation have increased considerably, as an example, recently Google Cloud Run has doubled the amount of available memory to 16-GB main-memory, it is not always possible to deploy a service. A straightforward solution is often data partitioning, but in some cases, it is still not enough. For these situations the algorithm is instead registered as a function in OpenFaaS. With OpenFaaS on Kubernetes the resource limits are easily overcome at the cost of having a more complex setup and higher processing costs.

With service/function registered in a compute environment, their deployment is triggered by calls from a queue system. Such calls are tasks containing a set of parameters defined either by a user via an UI or a workflow from an external partner. With different levels of integration and involvement of external users and developers from four companies and one NGO, we show how MSA helped us to develop and transfer efficient, scalable, and cost-efficient products.

The interaction between plasma of solar origin and the Earth’s magnetosphere-ionosphere (MI) system is at the base of several phenomena relevant to Space Weather. Among them, the MI coupling plays a fundamental role, as it allows energy and momentum to be exchanged between magnetosphere and ionosphere, and thus provides a mechanism able to modify the energy budget of the ionosphere. In fact, part of the energy injected into the ionosphere may be converted into mechanical energy and dissipated via Joule heating. Such a dissipation may significantly affect temperature, density, and the composition of the upper ionosphere, resulting, for instance, in an increased atmospheric drag affecting the satellite orbits. The MI coupling is mainly realized by means of field-aligned currents (FACs) that connect the magnetosphere with the high-latitude ionosphere. Thus, understanding the dissipation of these currents in the ionosphere may help to clarify their contribution to the energetic budget and to shed light on some physical processes still unclear. Generally speaking, the work done by electromagnetic fields on a charge distribution is given by the dot product of current density, J, and the electric field, E, which describes the conversion of electromagnetic energy into mechanical energy. In the direction parallel to the geomagnetic field, under the reasonable assumptions of quasi-neutrality and neglecting higher order terms, the only relevant contribution to the conversion term is given by Ohmic dissipation and the consequent Joule heating. This quantity can be computed by using Swarm data instead of models. Here, for the first time, we show statistical maps of power density features dissipated by FACs via Joule heating at Swarm A altitudes (about 460 km). This is realised by using six-year time series of electron density and temperature data acquired by the Langmuir Probes onboard the Swarm A satellite at 1 s cadence together with the field-aligned current density product provided by the ESA’s Swarm Team at the same cadence. Maps of the same quantity under different levels of geomagnetic activity are also shown and discussed in light of the previous literature.

This work is partially supported by the Italian National Program for Antarctic Research under contract N. PNRA18 00289-SPIRiT and by the Italian MIUR-PRIN grant 2017APKP7T on "Circumterrestrial Environment: Impact of Sun-Earth Interaction".

Height-integrated ionospheric Pedersen and Hall conductances play a major role in ionospheric electrodynamics and Magnetosphere-Ionosphere (MI) coupling. Especially the Pedersen conductance is a crucial parameter in estimating ionospheric Joule heating, which is an important energy sink of the whole MI-system. Increased Joule heating during geomagnetic storms and substorms has large impact on the thermospheric chemical composition and circulation, and can also affect the atmospheric drag experianced by satellites at low Earth orbits. Unfortunately, the conductances are rather difficult to measure directly in extended regions, so statistical models and various proxies are often used.

We discuss a method for estimating the Pedersen Conductance from magnetic and electric field data provided by the Swarm satellites. We need to assume that the height-integrated Pedersen current is identical to the curl-free part of the height integrated ionospheric horizontal current density, which is strictly valid only if the conductance gradients are parallel to the electric field. This may not be a valid assumption in individual cases but could be a good approximation in a statistical sense. Further assuming that the cross-track magnetic disturbance measured by Swarm is mostly produced by field-aligned currents and not affected by ionospheric electrojets, we can use the cross-track ion velocity and the magnetic perturbation to directly estimate the height-integrated Pedersen conductance. The same relationship is valid both in the case of quasi-static MI-coupling and idealized Alfven wave reflection at the ionospheric boundary.

We present results of a statistical study utilizing 7 years of ion velocity and magnetic field data from the Swarm-A and Swarm-B satellites. Carefull selection and filtering of the data are required, which tends to limit the analysis to regions where the ion velocity and magnetic disturbances are reasonably strong, i.e. close to the auroral ovals. Statistical Pedersen conductance maps are derived for the Northern and Southern auroral regions during different seasons and levels of geomagnetic activity. We discuss possible applications of the results and possible limitations of the method.

Extreme lightning activity can cause electromagnetic fluctuations that propagate from the lower atmosphere up to the ionosphere. This process requires conversion of the electromagnetic perturbation propagating in the neutral atmosphere into an ionospheric plasma wave, which for ultralow frequencies (ULF) can attenuate strongly the wave amplitude. Additionally, thunderstorms have an observable effect on the ionosphere, which is manifested by small scale irregularities in the ionospheric plasma.

The ESA Swarm mission has been designed to provide high-precision registrations of the Earth's magnetic field. Since its launch in November 2013, the constellation, consisting of three LEO satellites, measures the magnetic signals that stem from Earth's core, mantle,
crust, oceans, ionosphere, and magnetosphere. However instruments onboard Swarm allow to expand main goals of the mission and give capabilities to capture signals originating from strong lightning events. In particular Swarm occurred to be quite effective in detection of transient luminous events (TLEs). In contrast to previous and on-going satellite missions fully dedicated to TLE, it is not a straightforward task for Swarm to capture such signals on a regular basis and 20 ms time resolution is the main limitation. However, Swarm allows for detection of continuing currents following the most intense +CG strokes. Having simultaneous registrations of ionospheric plasma parameters and magnetic field scintillations we are able to conduct systematic study to answer the question whether TLEs trigger perturbations in ionospheric plasma parameters in the thunderstorm active region?

The study concentrates on the longitudinal sector of both Americas, spanning through regions which belong to areas of most frequent occurrence of ionospheric scintillations, as well as lightning hotspots. In the joint analysis of in-situ registration of plasma parameters and magnetic field components from the ESA Swarm mission together with lightning observations derived from the GLM instrument on-board the meteorological GOES-R satellite, we provide classification of two types of ionospheric responses to the intensified thunderstorm activity. We reveal links between lightning and ionospheric wave properties that suggest a real causality relationship between the two phenomena.
Finally, in the context of upcoming launch of the Meteosat Third Generation (MTG): Lightning Imager, analysis shows, that synergy between in-situ ionospheric observations from the low-Earth orbit satellite and lightning imager will be beneficial for better understanding of upper ionospheric irregularities developing in the African thunderstorm cells as well as for detection of severe thunderstorms in the European region.

Ionospheric irregularities are structures or fluctuations of plasma density in different scale sizes. These irregularities can disrupt radio waves and produce an error for space-based or ground-based technologies which depend on the GNSS/ GPS signals.
Post-sunset ionospheric plasma irregularities are a common characteristic of the equatorial ionosphere. These irregularities, associated with plasma bubbles, are defined as density depletions relative to the background plasma as determined by in situ measurements. The physical mechanism which leads to the formation of plasma bubbles has been studied extensively. A seed density perturbation leads to the nonlinear Rayleigh-Taylor instability in the bottom side of the F region ionosphere. The plasma perturbation can grow spatially and reach a higher altitude in the ionosphere. One of the main features of a plasma bubble is the existence of power-law scaling in its energy power spectrum. This feature suggests that the spatial behavior of plasma bubbles can be compared and modeled with turbulent models.

The bifurcation process is a common feature of an evolving and turbulent flow. This process has been considered in the modeling of plasma bubbles in several studies. In situ bifurcated plasma bubbles have been detected with the San Marco D satellite in the west-east direction. Some examples of bifurcation-like processes have been observed in the field-aligned plasma bubbles using the Swarm Langmuir probes. In a bifurcated plasma bubble process, we need to have information about the measured plasma density and direction of flow to understand how the topology of a plasma bubble evolves to distinguish a bifurcated plasma bubble from a shrinking or expanding bubble. We use the Swarm Electric Field Instrument data to look at the flows' direction in one of the bifurcation-like plasma bubble events. In most cases, we detect an Alfvén wave inside the walls of plasma bubbles. In this study, we compare the properties of the low-frequency fluctuations in adjacent bifurcated regions.

Most satellites are equipped with magnetometers as part of their attitude and orbit control system (AOCS).

The measurements taken by these platform or navigational magnetometers are not only useful for coarse attitude determination for satellite operation but can also be used for scientific investigations, provided they have been properly calibrated.

This contribution describes the preprocessing and calibration of platform magnetometer data collected by CryoSat-2, GRACE and other Low-Earth orbiting satellites, and how they can be used for studying the dynamics of electric current systems in Earth's environment. We will also report on experiments of calibrating magnetic data of larger satellite fleets, and how existing and future ESA LEO satellites can be used for mapping Geospace and Space-Weather applications.

As an application we show a combination of magnetic field observations from the satellites CryoSat-2, GRACE, GRACE-FO with magnetic data taken by ESA’s Swarm satellite trio, in order to obtain an improved description of the space-time structure of ionospheric and magnetospheric current systems.

Fully calibrated data from CryoSat-2 (for the years 2010 - 2019) and the GRACE satellite duo (for the years 2008 - 2017) are freely available as daily files in CDF format at https://swarm-diss.eo.esa.int/#swarm%2F%23CryoSat-2 and ftp.spacecenter.dk/data/magnetic-satellites/GRACE/.

The actual state and variability of the Earth’s ionosphere are important aspects of the space weather system. Their understanding is crucial for ionospheric modelling and building the capability of predicting and mitigating severe space weather effects. One example of such effects is the degradation of communication or positioning with the Global Navigation Satellite Systems (GNSS), which is due to ionospheric plasma irregularities impacting the propagation of radio waves. Ionospheric irregularities at various scales are a result of dynamic processes in the ionosphere.

Through the project Swarm Variability of Ionospheric Plasma (Swarm-VIP), as a part of the Swarm+ 4DIonosphere initiative, we provide spatiotemporal characteristics of ionospheric plasma at different geomagnetic latitudes and uncover coupling between various scales in response to geomagnetic conditions. The project employs data from the Swarm satellites, such as the IPIR dataset [1,2], as well as auxiliary datasets. Taking advantage of the orbital characteristics of the Swarm satellites and using complementary scale analysis techniques such as wavelets or Fast Iterative Filtering, we ascertain the dominant scales at given geomagnetic conditions. Our focus is primarily on the characteristics of ionospheric plasma, i.e., plasma density and total electron content as measured respectively by the Langmuir probes and GPS receivers onboard Swarm satellites.

The result of Swarm-VIP is a semi-empiric model for the ionosphere based on the generalized linear modeling. The model determines the probability of occurrence of different scales in ionospheric plasmas with respect to geomagnetic conditions and the magnetosphere-ionosphere coupling. It also gives insight into ionospheric structuring and coupling between scales. The model can be understood in the context of space weather effects, such as scintillations of trans-ionospheric radio signals. The Swarm-VIP model is provided globally, along the whole orbits of the Swarm satellites, and a special emphasis is put on high latitudes, Arctic and Antarctica, and the European sector, where the validation study is carried out with a network of ground-based instruments.

The Swarm VIP project is funded by the European Space Agency’s in the Swarm+ 4DIonosphere framework (Contract No. 4000130562/20/I-DT).


References

[1] A. Spicher, L.B.N. Clausen, W.J. Miloch, V. Lofstad, Y. Jin, and J.I. Moen, Interhemispheric study of polar cap patch occurrence based on Swarm in situ data, J. Geophys. Res. Space Physics, 122, (2017), pp. 3837– 3851, doi:10.1002/2016JA023750.
[2] Y. Jin, C. Xiong, L. B. N. Clausen, A. Spicher, D. Kotova, S. Brask, G. Kervalishvili, C. Stolle, W.J. Miloch, Ionospheric plasma irregularities based on in-situ measurements from the Swarm satellites. J. Geophys. Res. Space Physics, 124, (2020), e2020JA028103. https://doi.org/10.1029/2020JA028103

Covering almost half of the European Union (EU), farmland has undergone major declines in biodiversity due to intensification of agriculture. This loss affects the delivery of biodiversity-mediated ecosystem services such as pollination, in turn affecting crop yield. In the EU, implementation of agri-environmental schemes through the common agricultural policy (CAP) aim to mitigate biodiversity loss in farmlands. However, the outcomes of these measures are context dependent and vary in time and space. Proper targeting of such measures and monitoring their outcomes requires knowledge of the local ecosystem down to the species level. For example, the presence of key plant species groups can determine the outcome of flower-strips, field margins and semi-natural grassland (Cole 2020).

Recent technological developments have led to computer vision-based plant species identification tools with increasing accuracy. Applications such as Pl@ntNet (Affouard 2017) allow users to determine the species of a plant from a picture. Currently, these tools are mostly used in citizen science projects and by the public. The increasing accuracy of such methods present an opportunity of integrating automated species recognition into larger monitoring schemes – potentially providing biodiversity data at spatial and temporal scales complementary to remote sensing based monitoring of the agricultural landscape. In the future, integrating such methodologies into monitoring frameworks could contribute to CAP agri-environmental schemes and baselines for biodiversity in European farmlands.

In this study we evaluate the integration of computer-vision based methods into larger biodiversity monitoring schemes. Using the LUCAS grassland survey (Sutcliff 2019) as an example we aim to reproduce variables collected in the field (number of flowering plants, presence of key species) with image recognition algorithms.

Images acquired during the survey representing grasslands throughout the EU are used to create a training (200 images) and test (50 images) dataset. We train an object detection algorithm to recognize and locate flowers (Faster R-CNN with pre-trained COCO weights). All flowers in the images are delineated and validated with the CVAT tool. To push the accuracies of the model the training is done in two steps. In the first step we train on full images and preform a hyperparameter tuning to determine the best model settings with performance metrics done on the test set. Since these kind of architectures for object detection have some problems to overcome overpopulated images, at a second step we split all the dataset into smaller slices and revalidate the objects inferred by the net with CVAT. With this new split dataset, we retrain the model for 10K iterations. Once trained, we extract the final accuracy metrics for the model.

Using the final model, we extract all flowers predicted from the selected set of LUCAS grassland images. Each flower detected is then used as an input to the Pl@ntNet application, an image classifications neural net, to determine the species. Using a threshold for the Pl@ntnet probability score to guarantee certainty on the Pl@ntNet predictions, we compile a list of species in each image. After matching legends with LUCAS survey, we can compare species detected using computer vision methods with the species reported by surveyors. By comparing our results with the surveyor data, we evaluate the accuracy of computer vision-based monitoring of grasslands. We discuss the limitations of this methodology and provide recommendations on how to better integrate computer vision-based tools in large scale biodiversity monitoring of grassland flowering plants.

Successful integration of automated species recognition into monitoring schemes would allow for large scale and high frequency monitoring of biodiversity in agricultural landscapes.

References:

Cole, L. J., Kleijn, D., Dicks, L. v., Stout, J. C., Potts, S. G., Albrecht, M., Balzan, M. v., Bartomeus, I., Bebeli, P. J., Bevk, D., Biesmeijer, J. C., Chlebo, R., Dautartė, A., Emmanouil, N., Hartfield, C., Holland, J. M., Holzschuh, A., Knoben, N. T. J., Kovács-Hostyánszki, A., … Scheper, J. (2020). A critical analysis of the potential for EU Common Agricultural Policy measures to support wild pollinators on farmland. Journal of Applied Ecology, 57(4), 681–694. https://doi.org/10.1111/1365-2664.13572

Sutcliffe, L. M. E., Schraml, A., Eiselt, B., & Oppermann, R. (2019). The LUCAS Grassland Module Pilot – qualitative monitoring of grassland in Europe. Palearctic Grasslands, 40, 27–31. https://doi.org/10.21570/EDGG.PG.40.27-31

Affouard, A., Goëau, H., Bonnet, P., Lombardo, J., Joly, A., Lombardo, J., & Joly, A. (2017). Pl@ntNet app in the era of deep learning.

Dating of phenological events, such as the beginning of greening in spring, falling of leaves in autumn, and the growing season length, is essential for the understanding of ecosystem dynamics and the interplay of different species. Therefore, land surface phenology (LSP) and phenological date estimates using remote sensors have been a subject of many studies (Berra et al., 2021), especially using optical satellite imagery (e.g., Landsat, Sentinel-2, VHRR, MODIS, SPOT, MERIS, VIIRS). Orbital sensors have the capability to provide global coverage data for large-scale phenological research. However, accurate estimates of the beginning of the spring and especially the autumn timing remain a challenge. For instance, many phenological events cannot be directly detected with the present temporal and spatial resolutions that are available from satellite imagery. Furthermore, different methodologies to derive LSP can be applied, resulting in distinct phenological dates estimates and leading to substantial uncertainties in understanding the interaction of phenological responses and ecosystem events and changes (Richardson et al., 2018, Berra et al., 2021). Overall, the estimated LSP based on satellite images requires a comparison with independent and high-resolution spatial-temporal data to support phenological modeling efforts. Over the past few years, the lack of adequate ground-scale phenological observations have been highlighted as a major challenge for interpretating and validating phenological date estimations derived from satellite time-series (Cleland et al., 2007, Richardson et al., 2018, Berra et al., 2021).

Recently, close-range remote sensing technology has advanced significantly in terms of data accuracy, and automatic data collection and storage. This has enabled robust continuous monitoring of wide range of phenomena, like forest vegetation dynamics. Therefore, close-range sensors, such as Phenocams (Richardson et al., 2018) and terrestrial laser scanners (Calders et al., 2015, Campos et al., 2021) can be considered the missing link between automated ground-based observations and satellite-based imagery. Here, we present a LiDAR phenology station (LiPhe) built by the National Land Survey of Finland. The LiPhe station is a potential source of accurate ground-scale phenological time-series observations that supports comparisons and analyses between satellite-derived spectral observations and tree-level phenomena in boreal forests. The LiPhe station was installed in February 2020 at a traditional 111-year-old (since 1910) Finnish research forest (Hyytiälä, 61°51´N, 24°17´E). The LiPhe station comprises a Riegl VZ-2000 scanner (RIEGL Measurement Systems, Horn, Austria), which has provided a long-term TLS time series (1.5 years) with high spatial (cm-level) and temporal (hour-level) resolution observations. Additional information on tree growth is provided by 50-point dendrometers that measure tree growth at micrometer-level at a 15 min temporal resolution. More technical details about LiPhe station can be found at Campos et al. (2021). The main features of the LiPhe station data that supports its use as a complementary ground reference source for satellite-derived spectral observations are the high-frequency data acquisitions combined with robust, illumination invariant observations at any time of the year. These properties allow the LiPhe station to detect phenological changes at an individual tree-level over an area of about four hectares. The phenological dates can be estimated from the time-series data collected with the LiPhe station by analyzing the quantitative spatial (canopy volumetric changes) and radiometric (TLS reflectance response) variations in the data over time. These variations and their magnitudes can be further cross-correlated with other ground observations, such as continuous weather parameters and the site’s species structure, thus, to explain the main phenomena driving the phenological changes.

In this work, we aim to evaluate the temporal accuracy of phenological dates estimations from the LiPhe station data and to assess their usability in calibrating large-scale phenological observations in boreal forests. In this regard, we compare different phenological date estimates derived from the LiPhe station and optical satellite imagery acquired over the same study area based on machine learning methods. Machine learning has been shown to have a great potentiality for modeling time-series datasets and detecting phenological changes (Zeng et al., 2020). A viable approach for phenological phase detection is to build a statistical model for each of the key phenological dates as a regression dependent on a number of spectral, meteorological, and possibly special characteristics. State-of the art statistical models, such as multiple linear regression, principal component regression, and Random Forest regression, were evaluated based on their predictive performance, with the best model selected for further applications (Czernec et al., 2018). Then, the most influential factors driving the phenological dates can be distinguished using feature selection. And, statistical distribution of the predicted phenological dates can be build using resampling techniques (e.g.bootstrap) for each of the phenological dates with the best performing statistical model (Czernecki et al., 2018, Ge et al., 2021). These comparisons will provide new insights into forest structural dynamics and related physical changes leading to improved interpretation of optical satellite observations of boreal forests.

REFERENCES

Berra, E. F., Gaulton, R. (2021). Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics. Forest Ecology and Management, 480, 118663.

Calders, K., Schenkels, T., Bartholomeus, H., Armston, J., Verbesselt, J., Herold, M. (2015). Monitoring spring phenology with high temporal resolution terrestrial LiDAR measurements. Agricultural and Forest Meteorology, 203, 158-168.

Campos, M. B., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J. and Puttonen, E. (2021). A LongTerm Terrestrial Laser Scanning Measurement Station to Continuously Monitor Structural and Phenological Dynamics of Boreal Forest Canopy. Front. Plant Sci., 11, 606752.

Czernecki, B., Nowosad, J. & Jabłońska, K. (2018). Machine learning modeling of plant phenology based on coupling satellite and gridded meteorological dataset. Int J Biometeorol 62, 1297–1309. https://doi.org/10.1007/s00484-018-1534-2

Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A., & Schwartz, M. D. (2007). Shifting plant phenology in response to global change. Trends in ecology & evolution, 22(7), 357-365.

Ge, H.; Ma, F.; Li, Z.; Tan, Z.; Du, C. (2021). Improved Accuracy of Phenological Detection in Rice Breeding by Using Ensemble Models of Machine Learning Based on UAV-RGB Imagery. Remote Sens., 13, 2678. https://doi.org/10.3390/rs13142678

Richardson, A.D., Hufkens, K., Milliman, T., Frolking, S., (2018). Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing. Scientific Reports,8, 5679

Zeng, L.; Wardlow, B.D.; Xiang, D.; Hu, S.; Li, D. (2020). A review of vegetation phenological metrics extraction using time-series,multispectral satellite data. Remote Sens. Environ., 237, 111511.

Pl@ntNet is an existing smartphone and web-based application that allows identifying plant species on close up images. Pl@ntNet has found various uses by citizens learning about species but also by experts in fields such as agro-ecology, education, and land and park management. Available in 36 languages and in 200+ countries, about 200.000 to 400.000 users use Pl@ntNet each day.

Pl@ntNet provides a set of generic functionalities but also services tailored to specific needs. In this presentation we report on the creation of a new project within Pl@ntNet on recognizing cultivated crops on geo-tagged photos. This application is fed by data and photos coming from the European Union’s Land Use and Coverage Area frame Survey (LUCAS). During five tri-annual LUCAS campaigns from 2006 to 2018, nearly 800.000 ‘cover’ photos were collected. The LUCAS cover photos have not been previously published but after anonymization will be released in 2022. Out of these, 330.000 provide a European coverage of (close-up) photos of crops. The protocol for these photos specified that “the picture should be taken at a close distance, so that the structure of leaves can be clearly seen, as well as flowers or fruits”. This enables an opportunity to use authoritative data to improve citizen science tools as they should be valuable for computer vision based applications such as Pl@ntNet.

A total of 215 crop species is included in the European crops project. In a first step, a the current Pl@ntNet deep learning algorithm is used for a forward inference on the LUCAS cover photos to identify crops and ingest those photos into the European crops project with a classification probability >0.8. In a second step, the LUCAS legend and the Pl@ntNet species lists have been matched and aligned. In a third step, this allows the LUCAS cover photos to be used as training data to improve the Pl@ntNet deep learning algorithm . The performance of the identification will be illustrated as a function of crop type and phenology. Issues relevant for the image classification task, such as pictures taken of recently emerged crops or taken after the crop was harvested, scaling issues related to close-up photos vs. field photos, and the need for increased capacity to generalize, will also be highlighted.

Finally, we will discuss various application contexts, detailing three uses cases associated to efficient in-situ data gathering for EO applications, potential citizen science activities related to the Farm to Fork strategy (e.g. educational activities on food and health), as well as methodological developments in the context of evidence reporting mechanisms for the Common Agricultural Policy.

In situ measurements of vegetation structural variables such as plant area index (PAI), leaf area index (LAI), and the fraction of vegetation cover (FCOVER) are needed in agricultural and forest monitoring, as well as for validating satellite products used in a range of downstream applications. Because periodic field campaigns are unable to adequately characterise temporal dynamics, a variety of automated in situ measurement approaches have been developed in recent years.

In this contribution, we investigate automated digital hemispherical photography (DHP) and wireless quantum sensor networks deployed under Component 2 of the Copernicus Ground Based Observations for Validation (GBOV) service. The primary objective is to develop and distribute robust in situ methods and datasets for the purposes of satellite product validation.

A mixture of automated DHP systems and wireless quantum sensor networks were installed at four sites covering deciduous broadleaf forest (Hainich National Park, Germany), Mediterranean vineyard vegetation (Valencia Anchor Station, Spain), wet eucalypt forest (Tumbarumba SuperSite, Australia), and tropical woody savanna (Litchfield SuperSite, Australia). At each site, manual field data collection (including DHP and LI-COR LAI-2000/2200C measurements) was carried out throughout the growing season, enabling us to benchmark the automated systems against established and accepted in situ measurement techniques.

We present findings from each of the field installations, including site-specific deployment considerations, data processing and filtering methods, and benchmarking results. We demonstrate that the automated DHP systems and wireless quantum sensors can provide rich temporal characterisation of vegetation structure, whilst demonstrating good correspondence to traditional measurement approaches (for PAI, initial results show r^2 = 0.78 to 0.91 and RMSE = 0.37 to 0.65). Perspectives on upscaling temporally continuous but spatially limited in situ data, which is a key requirement for validating moderate spatial resolution satellite products, are also discussed.

EO data can improve the timely measurement of agricultural productivity in support of efforts to evaluate and target productivity-enhancing interventions by providing critical information required to stabilize markets, mitigate food supply crises, and mobilize humanitarian assistance. Access to EO, data processing infrastructure as well capacity to develop methods, have improved substantially. However, these are still lacking in smallholder systems that form a large percentage of agriculture in sub-Saharan Africa, where these data are even more critical due to the high dependency on agriculture for livelihood. Recent advances in Machine Learning (ML) and cloud computing, increased access to satellite data offer a new promise but the lack of ground truth labels (particularly crop- type) for training and validation remains one of the biggest impediments to advanced applications of ML for mapping smallholder agricultural systems. This not only limits the development of system-specific models but also limits testing of models developed elsewhere that could result in filling huge data gaps needed to produce products that can support early warning information for decision-making in agriculture and food security.

The project “Helmets Labeling Crops” is a partnership between international and institutions based in 5 African countries creating a publicly accessible unprecedented labeled dataset in an efficient, cost-effective, equitable, participatory approach that will ensure quality and have direct transformational impacts funded by the Meridian Institute under the Lacuna Fund.

This presentation will summarize 1) the partnership and its uniqueness, 2) the main approach to data collection “the Helmets-street level surveys done with GoPro Cameras mounted on motorbike helmets'', 3) our recently developed Street2Sat framework for obtaining large data sets of geo-referenced crop type labels. With over a million images collected so far in Kenya, Uganda, France, and the United States. Using preliminary data from Kenya, we present promising results from this approach and identify the future to improve the method before operational use in 5 countries.

Climate change is shifting natural phenocycles and, combined with ongoing human-induced disturbances, is putting pressure on forest ecosystems through increased frequencies of fires, droughts, degradation events and pests. Current observation technologies like the Eddy covariance technique allow monitoring these effects in terms of forest functioning. However, capacities to monitor the impacts on and changes in forest and vegetation structure, especially vertical structure, remain limited and constrain our physical understanding for using dense remote sensing time series tracking forest dynamics and disturbances.

In our presentation we will first examine current challenges in forest structure monitoring, with focus on support of functional monitoring at local scales to support upscaling of forest structure and change assessments via airborne and satellite sensors. Then, a range of prototype projects will be presented that demonstrate solutions to these challenges using a combination of different novel near-sensing techniques across a range of sites and conditions. This will lead to requirements and specifications for a refined observation strategy and underpin StrucChangeNet that will be introduced with the aims to fill current gaps in systematic and dynamic structural monitoring.

A key feature of StrucChangeNet is the implementation of recent developments in LiDAR and Internet of Things (IoT) technology. Airborne LiDAR (ALS), Terrestrial Laser Scanning (TLS) or Unoccupied Aerial Vehicles Laser SCanning (UAV-LS) have demonstrated the capabilities to measure forest structural attributes like above-ground biomass, leaf area and leaf area density profiles along with precise localisation and trait-retrieval of individual trees. StrucChangeNet will make use of TLS, UAV-LS, as well as new monitoring LiDAR systems to capture these variables and their temporal changes down to the individual tree. Additionally, recently developed passive sensors based on IoT technology that measure multi-spectral canopy transmittance will be employed to assess canopy structural and biochemical properties. This setup will produce rich datastreams for canopy near-real time monitoring. The initial setup of ten globally distributed sites will extend the capacities of existing monitoring sites, with a focus on Eddy covariance equipped sites such as those of the ICOS, TERN and NEON networks. In particular the LiDAR data streams will also be relevant for the recently proposed GEOTREES initiative for forest aboveground biomass estimation.

MetOp-SG is the follow-on to the current, first generation series of MetOp satellites, which is now established as a cornerstone of the global network of meteorological satellites. MetOp-SG is required to ensure the continuity of these essential meteorological observations, improve the accuracy / resolution of the measurements, and also to add new measurements / missions.

The overall MetOp-SG Space Segment architecture consists of two series of satellites (Satellite A and Satellite B), each carrying different suites of instruments and operating in a LEO polar orbit identical to that of MetOp. The planned launches of the first of each series of satellites are scheduled in 2024 and 2025, respectively.

The MetOp-SG Programme is being implemented in collaboration with EUMETSAT. ESA develops the prototype MetOp-SG satellites A and B (including associated instruments) and procures, on behalf of EUMETSAT, the recurrent satellites (and associated instruments). EUMETSAT is responsible for the overall mission, funds the recurrent satellites, develops the ground segment, procures the launch and LEOP services and performs the satellites operations as part of its EPS-SG Programme.

The ESA MetOp Second Generation (MetOp-SG) Programme was approved at the ESA Council Meeting at Ministerial level in Naples in November 2012. The EUMETSAT Polar System – Second Generation (EPS-SG) Programme was fully approved on 24 June 2015.

The Phase C/D was kicked-off in November 2015 with Airbus Defence and Space SAS, France as Prime contractor for the Satellite A series, and with Airbus Space and Defence GmbH, Germany as Prime contractor for the Satellite B series. The Satellite A CDR was passed in 2018 and the Satellite B CDR in 2019. The Satellite A QR was passed in July 2021 and the Satellite B QR is planned for May 2022.

The scope of this paper is to provide an up-to-date overview of the MetOp-SG Programme development status, including the description of the Space Segment architecture, satellites configuration and key features and characteristics, orbit and space to ground communications link.

This paper also provides the up-to-date status of the Satellites PFM A and B AIT campaigns that were initiated in 2019 and 2020 respectively.

The MetOp-SG instruments status are described in separate papers.

METimage is a cross-purpose, medium resolution, multi-spectral optical imaging radiometer for meteorological applications onboard the MetOp-SG satellites, fulfilling the VII mission of the EPS-SG programme. The primary objective of the VII mission is to provide high quality imagery data for global and regional weather forecast and climate monitoring.
METimage measures the solar backscattered radiation and thermal radiance emitted by the Earth in 20 spectral bands from 443 nm to 13.345 µm.
We provide an overview over the instrument design and the main subsystems consisting of the calibration assemblies, the scan and de-rotation assemblies, the telescope, and the cryogenic subsystem. The latter includes the assemblies for spectral band and channel separation as well as the three focal plane assemblies.
We discuss the main design drivers and present the expected performance.

The Infrared Atmospheric Sounding Interferometer New Generation (IASI-NG) is a key payload element of the second generation of European meteorological polar-orbit satellites (METOP SG) dedicated to operational meteorology, oceanography, atmospheric chemistry, and climate monitoring.
CNES (Centre National d ’Etudes Spatiales) is in charge of IASI-NG programme based on an instrument concept proposed and developed by Airbus Defence and Space. It will continue and improve the IASI mission in the next decades (2020-2040) in the field of operational meteorology, climate monitoring, and characterization of atmospheric composition related to climate, atmospheric chemistry and environment. The performance objective is mainly a spectral resolution and a radiometric error divided by two compared with the IASI first generation ones.
The measurement technique is based on wide field Fourier Transform Spectrometer (operating in the 3.5 - 15.5 µm spectral range) based on an innovative Mertz compensated interferometer to manage the so-called self-apodization effect and the associated spectral resolution degradation.
Environment qualification and performance tests on flight sub-systems has been completed successfully. This includes the performance vacuum tests on the complete Focal Plane and Cooler Assembly and the performance vacuum tests on the assembly made of the aligned flight Interferometer with its Laser Metrology. The obtained results show the complete end to end processing of the acquired interferograms taking into account all metrology data.
An exhaustive microvibration campaign has also been performed on EM Instrument and allowed to have a fine assessment of the instrument sensitivity and to correct unforeseen parasitic effects. Conducted EMC qualification and first functional verifications with representative software were also done on EM Instrument.
The flight model Instrument is now fully integrated and aligned and is currently under mechanical qualification. The final thermal vacuum test will start the first trimester of 2022 after functional test verification.
We present here the synthesis of the main milestone achieved on EM Instrument and flight sub-systems, as well as the progress and available results on complete flight model Instrument.

Sentinel-5 is a hyperspectral imaging instrument that will be embarked on the MetOp Second Generation satellites (MetOp-SG) with the fundamental objective of monitoring atmospheric composition from low Earth orbit. The instrument consists of five imaging spectrometers that sense the solar spectral radiance backscattered by the earth atmosphere in eight defined spectral bands in the range from UV (270nm) to SWIR (2385nm). Processing of Sentinel-5 data will allow obtaining the distribution of important atmospheric constituents such as ozone, methane, nitrogen dioxide, sulphur dioxide, etc. on a global daily basis and at finer spatial resolution than its predecessor, the GOME-2 instrument embarked on the first generation of MetOp satellites. Sentinel-5 is part of the Space Component of the Copernicus program, a joint initiative by ESA, EUMETSAT and the European Commission. This paper presents an overall description of the Sentinel-5 instrument, its calibration and data processing.

The EUMETSAT Polar System – Second Generation (EPS-SG) will further improve observational inputs to Numerical Weather Prediction models in continuation and enhancement of the EUMETSAT Polar System (EPS) first generation service that have been exploited by EUMETSAT since 2006. Starting in 2024/2025, the EPS-SG System will deploy three successive pairs of Metop-SG A and Metop-SG B satellites equipped with complementary instruments to provide 21 years of service. The satellites will fly together on the same mid-morning polar orbit as the current Metop satellites in-orbit and will expand by 20+ years the climate data records initiated with EPS. Metop-SG A is an atmosphere sounding and imaging satellite equipped with a suite of microwave and hyperspectral infrared instruments (MWS and IASI-NG) and two advanced optical imagers (METimage and 3MI). Metop-SG A also carries the Copernicus Sentinel-5 spectrometer for measurements of trace gases. Metop-SG B is a microwave imaging satellite delivering radar observations of ocean surface wind and soil moisture (SCA) and passive microwave observations of precipitation and ice clouds (MWI and ICI). Metop-SG B also carries a receiver supporting the ARGOS localisation and data collection mission. Both satellites carry a Global Navigation Satellite System (GNSS) radio-occultation instrument (RO) for limb sounding of temperature and humidity at high vertical resolution. The EPS-SG system is developed in cooperation with the European Space Agency (ESA), Centre National d’Etudes Spatiales (CNES) and Deutsches Zentrum für Luft- und Raumfahrt (DLR). EUMETSAT procures all launch services, develops the full ground infrastructure and integrates and validates the full system. EUMETSAT then operates and exploits the system and develops additional products using new algorithms. EPS-SG is Europe’s contribution to the Joint Polar System (JPS) shared with the National Oceanic and Atmospheric Administration (NOAA) of United States. The presentation will provide a short overview of the programme, its achievements, current status and outlook.

EnMAP, the Environmental Mapping and Analysis Program, is the spaceborne German hyperspectral satellite mission. On behalf of the Geman Space Agency at DLR, the satellite is being developed by OHB system AG and the ground segment is being provided by DLR. Science lead institute is GFZ Potsdam.

As of writing, the satellite has now successfully finished its environmental test campaign and is being prepared to be launched in April 2022 with a Falcon 9 from Florida. In this talk, we give an overview on the current status of the mission, its capabilites and hopefully deliver first results of the satellite's first days in orbit. We will also give an outlook on the planning for the commissioning phase, the operational phase and collaborations.

The launch of the spaceborne imaging spectroscopy mission EnMAP (Environmental Mapping and Analysis Program; www.enmap.org) is scheduled for April 2022.

The presentation will detail the status and planning of EnMAP operations. The status covers on the one hand the realized system to perform operations and on the other hand the results of the Launch and Early Orbit Phase (LEOP) (0.5 months) and, in particular, the first insights of the Commissioning Phase (CP). The planning covers the complete activities of the CP (5.5 months) and the subsequent routine phase (54 months) with the provision of quantitative imaging spectroscopic measurements substantially improving remote sensing standard products and allowing advantageous user-driven information products to be established.

The objective of EnMAP is to measure, derive, and analyze quantitative diagnostic parameters describing key processes on the Earth’s surface focusing on issues related to soil and geology, agriculture, forestry, urban areas, aquatic systems, ecosystem transitions and associated science.

The spectral range of EnMAP covers 420 nm to 2450 nm based on a prism-based dual-spectrometer with a spectral sampling distance between 4.8 nm and 8.2 nm for the VNIR (Visible and Near Infrared; 450 nm to 1000 nm) and between 7.4 nm and 12.0 nm for the SWIR (Shortwave Infrared; 900 nm to 2450 nm). An on-board doped Spectralon sphere enables a spectral accuracy of better than 0.5 nm in VNIR and 1.0 nm in SWIR. The target signal-to-noise ratio (SNR) is 500:1 at 495 nm and 150:1 at 2200 nm (at reference radiance level representing 30% surface albedo, 30° Sun zenith angle, ground at sea level, and 40 km visibility with rural atmosphere). The signal is fed into two parallel amplifiers with different gains for each of the two detectors to have a large dynamic range. Sun calibration measurements with an on-board full-aperture diffuser enable a radiometric accuracy of better than 5%. Additional measurements, e.g. for non-linearity and closed shutter measurements for subtraction of dark signal, complement the calibration. Each detector array has 1000 valid pixels in spatial direction and, with a geometric resolution 30 m x 30 m, a swath width (across-track) of 30 km is realized. A swath length (along-track) of 5000 km, split to several observations, is reached per day. The repeat cycle of 398 revolutions in 27 days combined with an across-track tilt capability of 30° enables a target revisit time of less than 4 days. And each region is viewable under an out-of-nadir angle of at most 5°. The local time of descending node is 11:00.

The satellite, which is realized by OHB System AG, will be operated by the ground segment. DLR’s Earth Observation Center (EOC) together with the German Space Operations Center (GSOC) are responsible for operations. Mission management is covered by DLR’s Space Agency. Control and command of the satellite based on flight operations procedures using real-time and dumped data is performed via S-band ground stations for telemetry and telecommand data in Weilheim (Neustrelitz as backup) and in addition Inuvik, O’Higgins, and Svalbard for non-nominal operations. Proposals, observations, and associated research are presented by an interactive map supporting the establishment of a world-wide user network. In case of tasking conflicts, issued observations are prioritized not only according to static information like the underlying priority of the request, but also based on historical and predicted cloud coverage information taking satellite constraints such as power and storage into account. All information is incorporated into the mission timeline immediately on reception and feedback to users on planned observations is provided whenever the planning state of an observation changes. Required orbit maneuvers, for orbit maintenance and collision avoidance, and contacts with X-band ground stations for instrument data reception in Neustrelitz (Inuvik as backup) are also considered in the mission timeline and as such transmitted to the satellite during S-band passes. Together with orbit and attitude data of the satellite, image products from received instrument data during X-band passes are fully-automatically generated at three processing levels, and long-term archived in tiles of 30 km x 30 km. A catalogue allows users to search and browse all products based on the standardized protocols CSW (catalogue service for the web) and WMS (web mapping service). Because of the necessary various processing options, each product is specifically generated for each order and delivered using SFTP (secure file transfer protocol) to the scientists.

Level 1B products are corrected to Top-of-Atmosphere (TOA) radiances including defective pixel flagging, non-linearity correction, dark signal (and digital offset) correction, gain matching, straylight correction, radiometric/spectral referencing, radiometric calibration, and defective pixel interpolation. Level 1C products are orthorectified to a user selected map projection and resampling model. The physical sensor model is applied by the method of direct georeferencing with a correction of sensor interior orientation, satellite motion, light aberration and refraction, and terrain related distortions from raw imagery. The products have a geolocation accuracy of 30 m with respect to a reference image based on selected Sentinel-2 Level 1C products having an absolute geolocation accuracy of 12.5 m. Level 2A products are compensated for atmospheric effects with separate algorithms for land and water applications. For the land case the units are expressed as remote sensing, namely Bottom-of-Atmosphere (BOA), reflectances. For the water case the units are normalized water leaving remote sensing reflectance or subsurface irradiance reflectance based on user selection. A pixel classification (e.g. land-water-background, cloud) is performed and aerosol optical thickness, columnar water vapor, and adjacency corrections are treated accordingly. At all processing levels per-pixel quality information and rich metadata are appended online.

Offline quality control, e.g. based on pseudo invariant calibration sites (PICS), maintenance and fine tuning of the image processing chain, and calibrations of the instrument during operations complete the ground segment. The independent validation of products, e.g. based on already established calibration/validation procedures, sites, networks, and products of other missions, is performed by the science segment led by the GFZ German Research Centre for Geosciences. All elements of the mission are characterized and calibrated, technically verified and operationally validated until launch.

EnMAP will be launched by a Falcon 9 of SpaceX from Florida, USA. The subsequent LEOP covers the first contact with satellite after separation, setting up telemetry and telecommand communications, continuous monitoring of health status, checkout and configuration of all platform functions, e.g. achievement of nominal power and thermal configuration, activation and calibration of sensors and actuators, e.g. for attitude and orbit determination and control, and acquisition of required orbital parameters. The subsequent CP covers the activation of the instrument data storage and all payload functions including the first image acquisition, downlink, and processing. The focus is first on radiometric, second on spectral in-flight calibration using all on-board equipment and taking pre-flight characterization and calibration into account, and in parallel on geometric characterization using Earth observations. The results lead to the optimization of the radiometric and geometric processors at first and then to the atmospheric processors. These activities are iterated and complemented by continuous instrument monitoring and quality control. Finally, based on end-to-end experiences the user interfaces from observation planning to product delivery and the complete processing chains are fine-tuned. The objective of the CP is to put space and ground segment into nominal routine operations with detailed in-orbit performance analyses of on-board and on-ground functionalities resulting in product approval for users which is expected for October 2022. The subsequent routine phase keeps the mission working in nominal operations based on ground procedures and by appropriately handling non-nominal operations, if required. All elements are supervised, the satellite is kept in the required orbit, data are acquired and dumped according to the requests and following the planned mission timeline, and products are processed and delivered to users. The offered operational services are complemented by a service team offering expert advice on the exploitation of EnMAP.

EnMAP operations are planned to be continued until April 2027.

We present the measurement concepts and results of the pre-flight characterization and calibration measurements of the EnMAP HyperSpectral Imager (HSI). The final measurement campaign of the instrument was performed in 2020 prior to the integration of the instrument onto the platform. Based on specific design of the HSI the measurement concept was designed to be performed in air with subsequent corrections applied for in vacuum performance estimation. The measurement concepts and setups are demonstrated to be of excellent quality with the obtained accuracies exceeding the required values significantly. Thus, the choice of ambient conditions for calibration and the design of the optical setups and equipment are confirmed. We show that the as built HSI exceeds the expectations in almost all important parameters such as SNR, NEdL, MTF and distortions indicating that we can expect very good data quality in flight. Calibration coefficients for radiometry, geometry and spectral registration were obtained at high quality allowing for a very good reference point for the initial in orbit calibrations during the commissioning phase. The respective on-board devices and concepts for spectral (spectral signature-based multipoint registration) and radiometric calibration (full aperture diffuser) have been validated and characterized and will allow to transfer and maintain the calibration of the HSI to the final mission environment. With a planned launch date for EnMAP in April 2022 we hope to be able to present first light from the mission to confirm the on-ground results.

The Environmental Mapping and Analysis Program (EnMAP) is a spaceborne German hyperspectral satellite mission that aims at monitoring and characterizing the Earth’s environment on a global scale. EnMAP core themes are environmental changes, ecosystem responses to human activities, and management of natural resources. In 2021 major milestones were achieved in the sensor and satellite preparation which is by end-2021 in the final acceptance review and pre-launch phase, with a launch window opening April 2022 (Fischer et al., ESA LPS 2022). Accordingly, the mission science support shifted from science development to pre-launch and launch support.

The EnMAP science preparation program has been run for more than a decade to support industrial and mission development, and scientific exploitation of the data by the user community. The program is led by the German Research Center for Geosciences (GFZ) Potsdam supported by several partners and is funded within the German Earth observation program by the DLR Space Agency with resources from the German Federal Ministry for Economic Affairs and Energy (BMWi). In 2020 a new 3+1-year project phase started during which specific activities are performed at the GFZ Potsdam together with the four project partners Humboldt-University (HU) Berlin, Alfred-Wegener Institute (AWI) Bremerhaven, Ludwig Maximilian University (LMU) Munich, and University Greifswald. These activities focus on the preparation for the scientific exploitation of the data by the user community as well as mission support during the commissioning phase and the start of the nominal phase, supported by the EnMAP Science Advisory Group.

In this presentation, we aim at providing an update of the current science preparation activities performed at GFZ. This includes an update of the data product validation activities focusing on an independent validation of the EnMAP radiance and reflectance products. For smooth and efficient validation especially during the commissioning phase, a semi-automatic processing chain is being developed (EnVAL), which streamlines the validation sites and in-situ data management as well as the validation tasks and report generation. Also, an update on new resources in the online learning initiative HYPERedu will be presented. In particular, the first Massive Open Online Course (MOOC) on the basics of imaging spectroscopy titled ‘Beyond the Visible – Introduction to Hyperspectral Remote Sensing’ was successfully opened in November 2021. An update will be further provided on the status of algorithms included in the EnMAP-Box related to data pre-processing and derivation of geological and soil mapping. It includes the EnMAP processing tool (EnPT) that is developed as an alternative to the processing chain of the EnMAP ground segment and provides free and open-source features to process EnMAP Level-1B data to Level-2A bottom-of-atmosphere (BOA) reflectance, and the EnMAP geological Mapper (EnGeoMap) and Soil Mapper (EnSoMap) for users in bare Earth and Geosciences applications. Finally, a background mission plan is developed as mission internal to fully exploit the resources of the satellite in terms of functionalities and/or capacities when there are resources available after all user requests have been processed. It can be used to generate time series databases interesting for the user community and anticipate future user needs, or to prototype and validate new mission strategies, such as large mosaicking demonstrations and/or synergies with other hyperspectral missions.

The quantification of agricultural traits is essential to evaluate seasonal dynamics of crops, in particular to assess the efficiency of management measures or the effects of changing climatic conditions. Spaceborne imaging spectroscopy data from recently launched or upcoming missions allows the spatiotemporally-explicit monitoring of crop traits, such as leaf area index (LAI), leaf chlorophyll content (Cab), leaf dry matter content (Cm), leaf carotenoid content (Cxc) or leaf water content (Cw), as well as upscaled canopy-level variables and average leaf inclination angle (ALIA). The unprecedented high-dimensional data streams call for efficient, fast and easy-to-use evaluation tools to obtain key agricultural information over large and heterogeneous cultivated areas. In this respect, the Agricultural Applications (Agri-Apps) have been developed within the freely available EnMAP-box 3.9, which is provided within in the framework of the German Environmental Mapping Analysis Program (EnMAP) mission. The Agri-Apps specifically provide three main retrieval tools, which employ different estimation strategies: (1) empirical algorithms based on parametric regressions, such as the Analyze Spectral Integral (ASI), (2) physically based models, using radiative transfer models (RTM), such as the Plant Water Retrieval (PWR) tool, and (3) hybrid tools, which combine machine learning (ML) regression algorithms with RTMs, such as the hybrid ANN tool. The ASI tool (1) computes integral indices from continuum removed spectra and combines these into three-band rasters of Cxc, Cab and Cw. These three different leaf biochemical constituents then are quantified applying specific calibration functions and are displayed as RGB-images. The PWR tool (2) uses an efficient algorithm to quantitatively extract canopy water content information directly from single hyperspectral signatures or from spectroscopic images applying the Beer-Lambert law. The hybrid ANN tool (3) uses an artificial neural network (ANN) algorithm, which is trained over a simulated data base generated by the PROSAIL model.
The objective of the present study was to test the three tools regarding their suitability, retrieval performance and practical applicability for the upcoming EnMAP mission. To obtain reliable product evaluations, in situ data of crop traits (winter wheat, winter barley and corn) were collected at the Munich-North-Isar (MNI) and Irlbach test sites in Bavaria, Germany, during the growing seasons of 2017, 2018, 2020 and 2021. To demonstrate the dynamics of the crop growth cycle, a series of crop trait simulations was generated with the ASI, PWR and the hybrid ANN tools over both test sites. Thereby, none of the tools was specifically calibrated for the sites, so that the transferability and general applicability of the retrieval algorithms could also be evaluated. Accuracy of the algorithms was evaluated against in situ reference data, leading to best root mean square errors (RMSE) of 8 µg/cm² for Cab and RMSE = 1.0 m²/m² for LAI retrieval (hybrid ANN tool), RMSE = 0.03 cm for Cw using the PWR tool, and RMSE = 0.01 cm for canopy Cw using the ASI tool. Though the latter failed for Cm and Cab retrievals, most of the results suggest a good to very high retrieval performance. In summary, the hybrid ANN tool outperformed the two others which may be attributed to its property to combine physics-awareness with the capabilities of learning algorithms. Second, mapping capabilities of the three tools are demonstrated on a time-series from 2021 consisting of five images from the spaceborne DLR Earth Sensing Imaging Spectrometer (DESIS), of three acquisitions by the PRecursore IperSpettrale della Missione Applicativa) (PRISMA) of the Italian Space Agency (ASI) and of one airborne Acquisition by the The Airborne Visible-Infrared Imaging Spectrometer - Next Generation (AVIRIS_NG). By combining data from all three instruments, it was possible to compile one of the first adequately dense time-series of (predominantly) spaceborne hyperspectral scenes over agricultural land. Mapping provided high fidelity of the tools. The hybrid ANN tool is specifically demonstrated on the AVIRIS-NG scene acquired in the context of the ESA CHIME campaigns 2021 over the Irlbach site. Figure 1 shows the resulting maps over the agricultural area on 30 May, 2021. In general, the map shows plausible estimates for the observed phase of the season, with crop growth patterns well reflected by the estimated traits. The intra-field distributions are relatively narrow and spatially consistent, which can be seen as an indirect measure for the accuracy of the retrieval tool. Overall, our results show that the Agri-Apps of the EnMAP-Box and in particular the hybrid ANN tool will be suited to efficiently process data from the EnMAP satellite mission in a quick and automated, user-friendly, transferable and generally applicable way. The Agri-Apps of the EnMAP-Box are ready to provide highly relevant agricultural products from spaceborne hyperspectral data as soon as EnMAP data becomes available from 2022 onwards.

Figure 1: Estimates of LAI, Cm, Cab and ALIA from AVIRIS-NG airborne data using the hybrid ANN tool of the EnMAP-Box Agri-Apps, Irlbach test site, Germany.

The German Environmental Mapping and Analysis Program (EnMAP) is an imaging spectroscopy satellite mission aiming at monitoring and characterizing the Earth’s environment on a global scale. As part of the EnMAP mission preparation the EnMAP-Box 3 has been developed as a free and open source (FOSS) plug-in for QGIS that is designed to process imaging spectroscopy data in a GIS environment. The main development goals are (i) advanced processing of hyperspectral remote sensing data by offering state-of-the-art applications and (ii) enhanced visualization and exploration of multi-band remote sensing data and spectral libraries in a GIS environment. Therefore, the algorithms provided in the EnMAP-Box 3 will also be of high value for other multi- and hyperspectral EO missions. The Python-based plug-in bridges and combines efficiently all advantages of QGIS (e.g. for visualization, vector data processing), packages like GDAL (for data IO or working with virtual raster files) and abundant libraries for Python (e.g. scikit-learn for EO data classification and or PyQtGraph for fast and interactive chart drawing). It consists of a (i) graphical user interface (GUI) for hyperspectral data visualization and spectral library management, (ii) a set of advanced general and application-oriented algorithms, and (iii) a high-level application programming interface (EnMAP API). The EnMAP-Box can be started from QGIS or stand-alone, and is registered in the QGIS plug-in repository.
The EnMAP-Box is a QGIS plugin with a separate GUI. Typical tasks like raster or vector file management and layer styling follow the same principles and look-and-feel, known from QGIS. They are extended by more specific requirements for imaging spectroscopy data such as spectral library integration. In contrast to QGIS and other GIS software that have a single map view, the EnMAP-Box follows the concept of having multiple, linkable views on the data sources at the same time next to each other. Currently, we offer map views for visualizing raster and vector data as well as spectral views for visualizing image pixel profiles and existing spectral libraries entries, for building new spectral libraries, or for performing spectral processing with spectra. Views can interact in various ways with each other, for example (i) the spatial position and the scale of map views can be synchronized, (ii) the selected image pixel profile of a map view can be visualized in a spectral view together with other image and library spectra, or (iii) the location of previously collected image profiles can be visualized as a point layer inside a map view. Besides such data management and visualization functionality, the EnMAP-Box provides the typical yet often more elaborated interactive tools for data exploration and preparation. Here, easy-to-use workflows for machine learning classification and regression and a raster image calculator, which offers the full flexibility of NumPy operations, shall be mentioned. Moreover, domain specific interactive applications (for agriculture, geology, soil or forest science and hydrology) have been contributed by several project partners as part of the mission preparation activities. These include e.g. radiative transfer models for forward/backward simulation of leaf and vegetation canopy reflectance, suites for analyzing soil and geology spectra, or for mapping water constituents.
EnMAP-Box algorithms are developed based on the QGIS processing framework and can be used from the GUI, stand-alone Python scripts and the command line. The EnMAP-Box API enables convenient raster data IO for memory efficient block-wise image processing. The available algorithms can be used inside the QGIS Model Designer to build complex workflows covering i) machine learning (i.e image classification, regression and unmixing) and accuracy assessment, ii) various spectral resampling options, iii) spatial and spectral filtering and more.
In summary, the EnMAP-Box 3 is a powerful FOSS plugin for QGIS with a long list of available image processing and analysis tools and applications. Its full strength will be needed, when more spaceborne imaging spectroscopy data becomes available over the coming years. The Toolbox is constantly improved and extended and is a key element of the HYPERedu learning platform for imaging spectroscopy.
We present the concept of the EnMAP-Box 3 together with two typical application examples that highlight its user-friendly yet powerful algorithms.

1. Monitoring Intertidal and coast
With the establishment of the National Park Schleswig-Holstein Wadden Sea, being a UNESCO World Heritage since 2011, the monitoring of important environmental parameters became necessary for the verification of the status and the planning of further development. Finally, a coordinated monitoring program could be agreed upon with the Wadden Sea riparian states The Netherlands, Germany and Denmark. The roughly 80 environmental parameters include sediment and seagrass abundance, both parameters that have to be collected for numerous other guidelines and have repercussions on the other biotopes. Sediment type, currents and exposure times are important factors for the biology and productivity of intertidal flat areas.
Seagrasses are the only flowering plants in the European Wadden Sea. They occur in the intertidal zones and constitute an important part of the food web due to their high productivity. Besides serving as a food source for migrating geese, seagrasses also provide a habitat for a variety of other species. In the North Frisian Wadden Sea, where the largest seagrass stocks can be found, seagrass meadows have been regularly monitored since 1994 using aerial surveys. After a significant decline in the 1930s, monitoring results have shown a steady increase in stock size until 2020. In almost all other areas of the world, however, seagrass is declining.
One main goal of the current work is to provide comparable measurements from remote and the ground. Different techniques are used, each showing advantages and disadvantages such as spatial inaccuracy (airborne), spatial coverage (ground based), individual interpretation (airborne, ground based) or spectral similarity of surfaces and atmospheric influences (satellite data). Thus, the comparison of the different data sets is difficult.

2. Satellite Acquisitions
Methods for detecting different habitats in intertidal flat areas based on satellite data have been developed since many years. They are currently applied in test phase for the operational monitoring of sediment and seagrass meadows. The distribution of sediment types - sand, mixed and muddy sediments - provide valuable information about potential distribution of benthic organisms. The changes of sediment and morphologic structures provide information about the adjustment of the system to external influences, such as climate change. For the detection of sediments, linear spectral unmixing is used and for the identification different vegetation indices are applied. Spectral and textural features are used within decision trees to classify and assess the different habitats. Water coverage is permanently changing on intertidal flats and therefore, the influence of water coverage as well as different degree of wetness has to be taken into account from the spectral signal of the target surfaces. Data availability is limited due to pre-conditions of low tide and low cloud coverage, but has improved since the launch of Sentinel-2 A and B. Satellite data are used in parallel to long-term monitoring programs since several years in order to assess the differences of the acquisition techniques, so that time series can be continued - and adjusted - for future programmes.

3. Verification methods for Seagrass

a. Airborne: Mapping by observation
For more than 30 years, an airborne low altitude survey of the Wadden Sea with observers has been carried out for rapid assessment. The procedure of airborne mapping provides maps of seagrass and green algae three times a year. The technique leads to a strong simplification of the observed occurrences, size and position inaccuracies, especially on outer tidal flats with low structural features.
Today's satellite image analyses are qualitatively much more accurate than the airborne mapping, but the new remote sensing method cannot distinguish spectrally between green algae and seagrass stands or provide indications of their species diversity. So far, the three annual aerial surveys are still used to determine the maximum spread - the characteristic value for the long-term population development - and intra-annual development over time.
b. Ground truth existing: References by estimation
In addition to airborne mapping, surveys of seagrass cover on the bottom is conducted. Due to the size of the area, only 1/6 of the full SH Wadden Sea area can be mapped within one year. Besides the surrounding of the seagrass meadows along the 5% coverage limit, transects crossing the meadows provide a state of the varying stand density, during which vital parameters, among others, are also observed. The data are used for verification of both aerial observations and satellite image analyses.
In addition, specific transects are captured that are used to optimize image classification or transform image classes into real values to be reported for monitoring. For this transects, photos are taken in different viewing angles and directions for later visual inspection.
c. Ground truth evolution
The assessments performed in the field are often subjective and can lead to inaccurate results due to the difficult conditions in the field.
The data collection during a transect survey leads up to 9 km through silty mudflats under often variable, sometimes harsh weather and light conditions. The coverage estimates are assessed by the mappers, yet the low tide periods are too short to make repeated observations for verification. Therefore, a measurement independent of the observers is desired.
In order to support ground truth field mapping, an automated method was developed using the RGB control images taken during a transect survey. For this, a series of images is shot in a top-down view, covering the area of the transect point. These are then split by a segmentation algorithm (SLIC). A neural network was trained to classify the resulting sub images into either three seagrass or several background categories. The seagrass segments are subjected to three independent methods to determine the coverage: a ‘simple’ method assigning the different seagrass classes a flat coverage percentage; the Green Leaf Index (GLI), a vegetation index utilizing the characteristics of light reflected by vegetation without having access to infra-red; and ‘Otsu’, an algorithm to differentiate between light and dark areas. The interpretation of the photos has to cope for the same problems as the remote acquired data: Each of these methods have different advantages and disadvantages in the presence of disruptive factors like water coverage, reflections, or unfavourable light conditions. Finally, the resulting relative coverage area of the transect point is given by the mean of the result of all contributing control images.
d. Measurements by AI (Ground truth and Drones)
In the future, moving to drones for image taking might increase efficiency, since it would be possible to cover larger areas in less time. It remains to be tested if the required image quality can be reliably produced in the often harsh weather conditions of the Wadden Sea, and if the proposed methods produce credible results even in the absence of parameters that can only be measured by an observer on the ground.

4. Future Reporting including EO
A crucial question in the use of remote sensing for legally relevant information is the transformation of image classification into real values - combined with information on the size of the uncertainty. In order to assess the transferability of assessment from image to image, the absolute reference provided by the photo interpretation as ground reference is an important step forward.
The combination of the various monitoring methods into a new permanent monitoring system and the development of a largely automated data flow from the satellite image to the reporting product is our mid-term goal for a reliable monitoring of the unique Wadden Sea and its habitats.


Bibliography
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Kohlus, Jörn; Stelzer,Kerstin; Müller, Gabriele; Smollich, Susan (2020): Mapping seagrass (Zostera) by remote sensing in the Schleswig-Holstein Wadden Sea, In: Estuarine, Coastal and Shelf Science; Spezial Issue: Asmus,Ragnhild; Schueckel, Ulrike; Eskildsen,Kai; Ricklefs, Klaus; Garthe, Stephan: From single ecological interactions to holistic assessments of coastal habitats in the Wadden Sea.
https://doi.org/10.1016/j.ecss.2020.106699
Müller, G, Stelzer, K., Smollich, S., Gade, M., Adolph, W., Melchionna, S., Kemme, L., Geißler, J., Millat, G., Reimers, H-C., Kohlus, J., Eskildsen, K. (2016): Remotely sensing the German Wadden Sea - a new approach to address national and international environmental legislation, In: Environmental Monitoring and Assessment, 188(10), 1-17; DOI 10.1007/s10661-016-5591-x. The article is available electronically
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Key information derived from multi-source optical imagery for supporting coastal territories management
Konrad Rolland, Anaïs Teissonnier, Anne Colliez, Emile Naiken
Collecte Localisation Satellite, PIGMA, région Nouvelle Aquitaine, DINAMIS

Coastal areas have been developing continuously for the past 40 years in terms of urban planning, demographics and economics; their attractiveness will continue in light of the latest prospective studies. A fragile, highly attractive area that drives the economy, the coastline is a geographical space where specific planning and management policies are deployed.
The imperatives of territorial recomposition and reconversion, whether driven by a logic of adaptation to the risks of climate change or a response to social and environmental changes, force us to question new models for thinking about and inventing a more resilient coastline. In a context of strong residential, recreational and tourist demands, of global changes in society, of increasing climatic and environmental challenges, and in order to succeed in the transition, it is necessary to rely on a detailed knowledge of developments in order to anticipate future changes and risks.
Within this framework, CLS has established a multi-date (1980s to 2020), accurate, reliable and homogeneous large-scale land use/Land cover database for the Nouvelle Aquitaine region (84,000 km² and a coastline of over 1,000 km). This complex and innovative project covers the entire spectrum of land observation, from the acquisition of image data to the analysis of the maps produced. The objective is to understand the signals of change in the territory and to anticipate future changes.
To build up this base, we researched, processed, and combined different image sources (panchromatic and infrared aerial images, but also VHR satellite images) but also DTM. We defined a production methodology in line with the needs of the territory and compatible with the timeframe. This solution used a mixed approach between automatic data and image processing and photointerpretation. It also made it possible to produce, for example, the latest 2020 land use/land cover databases based on low-cost satellite sources in a few months with reliability levels > 90%.
The quality and temporality (5 dates) of the land use/ land cover data allow us to establish scenarios of the state of the territory in 10, 20 or 50 years according to climate change models.
For this conference, we will present the methodology implemented but also the main results of the project to demonstrate how multi-source land observation data can be used in combination with other business data to understand and manage the development of a coastal territory.

Coastal marine environments, being invaluable ecosystems and host to many species, are under increasing pressure caused by anthropogenic impacts such as, among others, growing economic use, coastline changes and recreational activities. A continuous monitoring of those environments is of key importance for the identification of natural and manmade hazards, for an understanding of oceanic and atmospheric coastal processes, and eventually for a sustainable use of those vulnerable areas.

The joint Sino-European project “Remote Sensing of Changing Coastal Marine Environments” (ReSCCoME), as part of ESA’s DRAGON 5 Programme, addresses research and development activities that focus on the way, in which the rapidly increasing amount of high-resolution EO data can be used for the surveillance of marine coastal environments, and how EO sensors can detect and quantify processes and phenomena that are crucial for the local fauna and flora, for coastal residents and local authorities. We will present results from three ongoing ReSCCoME research activities, during which synthetic aperture radar (SAR) data are being used for different monitoring purposes in vulnerable costal marine environments.

Within the first research activity a classification scheme for sediments and habitats on intertidal flats in the German Wadden Sea was developed, whose feature set consists of the Freeman-Durden (F) and Cloude-Pottier (C) decomposition components, the Double-Bounce Eigenvalue Relative Difference (D) parameter, and two parameters derived from elements of the Kennaugh matrix (K). These feature sets are used as input for a Random Forst (RF) classification, and hence, the classification scheme is abbreviated FCDK-RF. Fully polarimetric SAR data acquired at L-, C- and X-band were used, and a comparison of the classification results with reference data obtained from optical data and field campaigns revealed that the FCDK feature set has good potential for the identification of sandy and mixed sediments on intertidal flats, even when they merge into, or mix with, each other, and for the detection of bivalve beds. A simplified FCDK-RF scheme for the use of dual-co-pol SAR data showed lower accuracies in the discrimination of mixed and sandy surfaces, but was well suited for the detection of bivalve beds.

The second research activity focuses on the impact of large offshore wind farms on the local and regional wind climate, with special emphasis on the wind farm wake effect. The wake region on the downstream side of a wind farm is typically characterized by reduced wind speeds and increased turbulence intensities lasting for tens of km. Within the coastal zone, wake effects can be mixed with other effects, e.g., horizontal wind speed gradients caused by the roughness transition between land and sea. Based on a large archive of wind maps retrieved from Sentinel-1 A/B SAR-C and Envisat ASAR scenes of the North Sea and the China Seas, sector-wise mean wind speeds were analyzed upstream vs. downstream of large offshore wind farms. SAR acquisitions before and after the wind farm construction were analyzed separately in order to quantify the velocity deficits caused by wind farms. A correction for horizontal wind speed gradients was applied to take the ‘background’ wind speed variability into account. Further, a correlation was found between the velocity deficits and the wind farm capacity per km², with larger wind farms leading to more pronounced velocity deficits.

Finally, the third research activity aims at different statistical analyses based on visual inspections of SAR data with respect to imprints of marine oil pollution. More than 2000 Sentinel-1 SAR-C and Envisat ASAR scenes of the Western Java Sea were analyzed and the ‘normalized oil pollution’ density was defined, which takes into account the SAR image coverage and local wind speed, both of which may affect the total number of oil spills observed at a certain location. This density was highest along the main shipping routes and at major oil production sites. Approximately half of the spills were smaller than 1 km², though spills of more than six times that size were also found. Since visual observations are always subjective, results from independent operators examining the same set of SAR images were compared and showed good qualitative agreement. The observed quantitative differences became smaller, when only SAR images acquired at higher wind speeds were considered, indicating that a confusion with biogenic slicks and low-wind areas is the main source of errors in oil pollution detection.

Inter-tidal zones globally are currently decreasing due factors including coastal development and sea-level rise that have put increasing pressures on these fragile environments (Murray et al, 2019). Such zones have implications for natural processes including bio-diversity, blue-carbon and coastal flood protection as well as for human processes including blue-economies, port-authorities and ecosystem services. Often inter-tidal areas are under-studied due to the inherent difficulties and danger of directly accessing them and the expense of existing mapping techniques. This talk will show how time-averaged inter-tidal bathymetry can be estimated by the Temporal Waterline (TWL) method and used to routinely monitor these zones.
Research at the UK National Oceanography Centre (NOC) (Bell et al, 2016; Bird et al 2018) has led to the development of a novel method for estimating bathymetry across the intertidal zone known as the Temporal Waterline Method (TWL). TWL processing, like spatial waterline methods requires a time-series of images spanning the tidal range and an estimate of the water-level at the time of each image. As well as being an active research topic, methods based on the use of fixed position X-Band Radar have been developed into a commercial service delivered by Marlan Maritime Technologies Ltd. Recent work at NOC has further developed the TWL methodology to exploit SAR (Synthetic Aperture Radar) satellite data, unlocking the potential for new (potentially global) intertidal zone monitoring solutions. Initially the UK tide-gauge archive was used to estimate the water-levels but this only allows processing of areas within 10 to 20 km of the tide-gauge. The most recent work has been to extend the method to use outputs from the FES2014 global tidal model, so that the TWL method can be applied globally.
The talk will present the methodology and show several UK case studies at both local and regional scales with examples of validation against LiDAR surveys. The potential for the routine monitoring of regional areas will be shown. The limitations and areas for improvement will also be discussed.

References:
Bell, Paul S.; Bird, Cai O.; Plater, Andrew J. 2016 A temporal waterline approach to mapping intertidal areas using X-band marine radar. Coastal Engineering, 107. 84-101. https://doi.org/10.1016/j.coastaleng.2015.09.009
Bird, Cai O.; Bell, Paul S.; Plater, Andrew J. 2017 Application of marine radar to monitoring seasonal and event-based changes in intertidal morphology. Geomorphology, 285. 1-15. https://doi.org/10.1016/j.geomorph.2017.02.002
Murray, Nicholas J.; Phinn, Stuart R.; DeWitt, Michael; Ferrari, Renata; Johnston, Renee; Lyons, Mitchell B.; Clinton, Nicholas; Thau, David; Fuller, Richard A. 2019 The global distribution and trajectory of tidal flats. Nature 565, no. 7738. 222-225.
https://www.nature.com/articles/s41586-018-0805-8?unique_ID=636808896127280679

Green macroalgae blooms have been persistently affecting the coasts of Brittany (France) since the 1970’s, causing losses of income to the fishing and touristic sectors. Macroalgae typically proliferate in confined coastal waters when nitrogen inputs, carried by rivers, exceed the assimilative capacity of the ecosystem. The current and tides uproot the macroalgae, leaving them stranded on beaches, where their decomposition might cause serious threats to animal and human’s health. Since 2002, the French Algae Technology and Innovation Center (CEVA) has been monitoring macroalgae surfaces on beaches at 95 sites using aerial photography. Along with this monitoring effort, the French government launched in 2010 anti-algae plans, with the aim of reducing nitrogen inputs and ultimately containing green macroalgae blooms frequency and severity.

Long-term estimates of macroalgae surfaces are however not available, limiting the understanding of temporal trends in green tides and the efficiency of macroalgae bloom reduction measures. Using freely and openly available Landsat imagery archives over 35 years (1984-2019), we automatically detected and quantified green macroalgae surfaces at four highly affected sites in Northern Brittany (Schreyers et al., 2021). Mean macroalgae coverage were characterized at annual and monthly scales. We demonstrate important interannual and seasonal fluctuations in macroalgae surfaces. Over the studied period, green macroalgae blooms did not show a decrease in extent at three out of the four studied sites, despite an observed decrease in nitrogen concentrations for the rivers draining the study sites.
In addition to this long-term trend analysis, we explored the potential of Sentinel-2 imagery for macroalgae surface detection and surface quantification. Sentinel-2 provides higher-spatial resolution (10 to 20 m) as well as more frequent observations than the Landsat sensors. Given the high temporal variability and persistent cloud coverage in Brittany, increasing the number of imagery available for detection can improve the accuracy of monitoring. We compare Sentinel-2 MSI derived macroalgae surfaces with i) Landsat-8 OLI estimates and ii) the aerial photography estimates from CEVA. We ultimately discuss the advantages and limitations of satellite-based monitoring of macroalgae proliferations and potential applications to other affected systems, for example in the Caribbean Sea, West Africa and the Gulf of Mexico where Sargassum bloom events have intensified since 2011.

References:
Louise Schreyers, Tim van Emmerik, Lauren Biermann, and Yves-François Le Lay. 2021. "Spotting Green Tides over Brittany from Space: Three Decades of Monitoring with Landsat Imagery" Remote Sensing 13, no. 8: 1408. https://doi.org/10.3390/rs13081408

Geospatial Information Authority of Japan (GSI) has nationwide monitored surface changes by conducting interferometric SAR (InSAR) analysis. L-band InSAR is advantageous for measuring surface displacements in Japan because high coherence can be achieved even in mountainous areas with vegetation and steep slope. This is why Advanced Land Observing Satellite-2 (ALOS-2) satellite, whose operation started in 2014 by Japan Aerospace Exploration Agency (JAXA), is suitable for our ground deformation monitoring. We have processed the ALOS-2 data to monitor crustal deformation in Japan, and have detected surface displacements associated with earthquakes, volcanic activities, and land subsidence.
Volcano monitoring is one of our main missions. There are 111 active volcanoes in Japan, and hazardous eruptions have repeatedly occurred historically; such as the 2014 Ontake eruption in Japan that led to 58 casualties. Therefore, it is desired to continuously monitor volcanic activities and detect anomalous signals to mitigate volcanic hazard. InSAR is a powerful tool to monitor volcanic activity, while it often suffers from atmosphere-related noises etc. and likely prevents from detecting anomalies. It is crucial for volcano observation to overcome the shortcoming of conventional InSAR–based monitoring.
In order to observe spatio-temporal development of surface displacements that slowly proceed over medium to long term on volcanoes, GSI has developed a software for InSAR time series analysis (GSITSA) in which small baseline subset (SBAS) algorithm is implemented. The analysis can improve a signal-to-noise ratio by substantially reducing atmospheric noises, and thus enables us to monitor temporal evolution of slow deformation. With the background, we have conducted the InSAR time series analysis using more than 20 SAR images acquired for the period from 2014 to 2021. The time series analyses have successfully unveiled surface displacements with a few cm/year or slower at several active volcanoes, which can be hardly identified by conventional InSAR analysis. The striking point is that the time series analysis using L-band data of ALOS-2, unlike C-band, can map surface displacement over volcanoes due to high coherence even in mountainous areas. The information on spatio-temporal variation of surface displacements can be a good indicator to know a degree of volcanic activity, and thus the ALOS-2 InSAR time series analysis is about to be one of major tools for evaluation of volcanic activity in Japan. In this presentation, we mainly report our volcano monitoring by ALOS-2 InSAR time series analysis, and further demonstrates effectiveness of L-band in comparison to C-band.

The lack of regular, area-wide observations of snow mass (snow water equivalent, SWE) is a main gap in monitoring of the global cryosphere. Repeat-pass differential SAR interferometry (RP-InSAR) offers a well-defined approach for mapping SWE at high spatial resolution by measuring the path delay of the radar signal propagating through a dry snowpack. In C-band and lower radar frequencies the absorption and scattering losses in dry seasonal snow are small so that the change in the InSAR phase delay of the signal reflected from the snow/ground interface is directly related to the snow mass that accumulated during the repeat-pass time span. A critical issue for routine application of RP-InSAR is the temporal decorrelation caused by changes in the complex backscatter signal. In C-band comparatively moderate snowfall amounts may already cause complete decorrelation whereas this effect is of less concern in L-band. The use of L-band RP-InSAR for SWE mapping has by now been limited to case studies because of the lack of regular repeat observations over snow-covered land surfaces. In anticipation of the enhanced repeat observation capabilities of upcoming L-band SAR systems, such as ROSE-L, we conducted field campaigns in two Alpine test sites in order to consolidate the SP-InSAR retrieval tools and evaluate the product performance. The use of C-band RP-InSAR for SWE retrievals was as well studied. Among the topics addressed are the effects of snowfall intensity on temporal decorrelation, the impacts of topography and land cover type on the RP-InSAR phase and resulting SWE, and possible effects of snow structural properties on the observed signal.
In winter 2019-2020 the studies on SAR SWE retrievals focussed on L-band and C-band RP-InSAR applications in the Upper Engadin region in Switzerland, based on repeat-pass SAR time series acquired by ALOS PALSAR-2, respectively Sentinel-1. ALOS PALSAR-2 strip map mode data of different tracks were acquired in 14, respectively 28 day repeat intervals, starting from snow free conditions in October 2019 until the beginning of the main melting period in March 2020. On days of the PALSAR overflights snow and soil parameters were measured at several locations in snow pits and along transects. Continuous time series on the temporal evolution of snow accumulation, melt events and meteorological parameters are available from automatic weather stations. In case of dry snow, the coherence of the PALSAR data is preserved also in case of snowfall, although intensive snowfall events cause a substantial decrease in coherence. SWE retrievals based on 14-day PALSAR data show for dry snow cases good agreement with insitu snow measurements. However, a continuous time series throughout winter could not be obtained because the 2019/20 PALSAR data set suffers from lack of continuity, comprising data from 4 different tracks, and also because on some acquisition dates the coherence was affected by transient melt. Limitations in the spatial coverage are imposed by the steep topography causing large gaps due to layover and foreshortening, calling for the acquisition of near-coincident data from ascending and descending passes. The Sentinel-1 6-day RP-InSAR data show suitable coherence during stable dry snow conditions also in case of light snowfall, but melt events and moderate and intensive snowfall causes complete decorrelation.
During March 2021 an experimental airborne campaign was carried out in the high Alpine test site Wörgetal/Kühtai near Innsbruck, addressing two complementary approaches for SWE measurements: (i) exploring the measurement concept of a geostationary C-band SAR mission for retrieval of dense SWE time series; (ii) consolidating the assessment of the RP-InSAR based SWE retrieval method and performance in support of mission preparation for ROSE-L and Sentinel-1 NG. The activities were performed by DLR and ENVEO within the ESA project SARSimHT-NG. Here we provide a first account on the studies related to objective (ii), as the airborne data enable the direct comparison of dual frequency (C- and L-band) polarimetric measurements. Within the period 2nd to 19th March 2021 multiple C- and L-Band SAR data were acquired F-SAR flights on 7 days spanning two snow fall events of about 10 cm and 40 cm mean fresh snow depth. On days of the F-SAR overflights vertical profiles of physical snow parameters were measured in snow pits at different locations as well as snow depths along transects.
In the presentation we report on the impact of snowfall and other environmental parameters on C- and L-band InSAR coherence over time periods ranging from several hours to days, and report on the performance of the InSAR SWE retrievals performed over the test sites Wörgetal/Kühtai and Engadin. The campaign activities and the presented results are of relevance for preparing snow monitoring activities with current and upcoming L-Band SAR missions including SAOCOM A/B, NISAR and especially the Copernicus Expansion Mission ROSE-L. Furthermore, the investigations are of relevance for exploring the combined use of C- and L-Band SAR data for monitoring main snowpack parameters.

Rice is a staple cereal crop in Asia, and the continent accounts for about 90% of global rice production and consumption. Rice-planted area map is important parameter to estimate rice production for food security or economy, and also to quantify the carbon, water cycle or methane emission via paddy fields. Rice is mainly cultivated in the rainy season, Synthetic Aperture Radar (SAR) is therefore a robust tool because it penetrates cloud cover. Recently, machine learning has been widely used in many land cover related researches and distinct results were reported, however, limitation is that it needs a large amount of training data, it is normally time and cost consuming task. In this research, we utilized the combination of unsupervised and supervised classification to efficiently produce the training data. Training data were generated from the k-means classification results for the sampled regions, then a random forest classifier was applied to ALOS-2 PALSAR-2 ScanSAR data to identify rice-planted area in Southeast Asian countries including Cambodia, Laos PDR, Thailand, and Vietnam. It is also difficult to identify rice-planted area in this region since there are high variations in rice phenology. In order to compensate for the variations, we used time-series metrics of calculated from SAR data. Classification models were fine-tuned for the target countries, and most of the models had an accuracy of 0.9 or better. Independent verification through visual interpretation using very high resolution images (VHRs) on Google Earth also showed a high degree of consistency with the classification results. The developed paddy field maps showed high accuracy in most countries and regions. In addition to these verifications, resulted map was compared with other rice planted area maps developed by Vietnam National Space Center(VNSC) and CNES/CESBiO under the collaboration of the VNSC’s 2019 Committee on Earth Observation Satellite (CEOS) chair initiative on rice monitoring. VNSC developed rice map for the Vietnam using time series Sentinal-1 data, and CNES/CESBiO developed rice map also using time series Sentinal-1 for four countries including Cambodia, Laos PDR, Thailand, and Vietnam under the ESA GEORICE project. Comparison results showed good consistency between these products by visual interpretation. However, further quantitative comparison or verification using in-situ data and national statistics, as well as application to other regions, seasons and years, is necessary to confirm the effectiveness of proposed methodology.

Agricultural production consumes the largest share of the world's water resources, using up to 90% of available freshwater. As demand for agricultural products is estimated to increases in the future, the accompanying intensification of production will put even more pressure on available water resources. This development makes the sector even more vulnerable to the increasing impacts of climate change on hydrological conditions and demands an even more efficient water use. Detailed knowledge of the spatial and temporal state of soil moisture, which is a key parameter in plant nutrition, agricultural production and environmental research, can help addressing these challenges. While in-situ measurement methods can be used for local monitoring, they are unsuitable for regional and even global application. In this regard, high resolution surface soil moisture data for regional and local monitoring (down to precision farming level) are still lacking at a global scale. Here, the increasing spatial and temporal resolution of current and future Synthetic Aperture Radar (SAR) satellite missions (e.g. Sentinel-1, ALOS-2/4, NISAR, ROSE-L) can help to overcome this problem. Nevertheless, the SAR missions come with individual limitations regarding soil moisture estimation. While the C-band SAR mission Sentinel-1 provides high temporal and spatial resolution, it has reduced sensitivity to soil characteristics under certain vegetation coverage due to its short wavelength. On the other hand, L-band SAR missions like ALOS-2 better penetrate covering vegetation, while its temporal and spatial resolution is reduced compared to C-band SAR missions.
Using low pass filtering as well as vegetational detrending, we developed an algorithm using high-resolution Sentinel-1 SAR timeseries for estimating soil moisture based on a change detection approach, so called alpha approximation (Balenzano et al. 2011). Tested and validated over the Rur catchment, comprising a diverse cropping structure and located in the federal state of North-Rhine Westphalia in the West of Germany, it showed encouraging results with a mean R² of 0.46 and an unbiased RMSE (uRMSE) of 5.84 %. Nevertheless, especially during the growing season, the estimated soil moisture shows a bias compared to the in-situ measured soil moisture for some crops. The reason for this is the changing sensitivity of the C-band backscattering signal to a uniform change in soil moisture occurring during the growing period. To overcome this problem, L-band ALOS-2 data is used, being less affected by vegetation cover and more sensitive to changes in soil moisture. By combining both timeseries, the high spatial and temporal resolution of C-band Sentinel-1 is used to fill the gaps between the sparse ALOS-2 recordings, while benefiting from the lower vegetation sensitivity of L-band. In this regard, this study evaluates different methods for assembling both microwave frequency bands, e.g. using L-band ALOS-2 soil moisture estimations as boundary conditions for C-band Sentinel-1 soil moisture estimation or matching changes in C-band backscattering signal to the co-located changes of L-band backscattering signal. The resulting algorithm for soil moisture estimation will be integrated within an automated workflow, using a cloud-computing platform (e.g. Google Earth Engine, CODE-DE). This enables fast processing without the use of local computational infrastructure. It will be validated over an Apulian test site, reflecting the diverse agricultural landscape of the Mediterranean region.

With the increasing deployments of CubeSat and SmallSats by both Government and Commercial entities, there is a need for innovative sensors, techniques and applications. These solutions have to be compact, low power, and with improved efficiencies. Rapid progress has been made in innovative methods and sensors for detection of UV/Visible/Infrared radiation for applications in Earth Remote Sensing and other commercial areas.

Since the community has graduated from amateur experiments in the universities to building highly capable CubeSats, it’s time to look into possible science applications of these platforms. There is always a question of calibration traceability of large amount of data acquired from these CubeSat and SmallSat missions. Few of the missions are able to carry calibration hardware on board and produce calibrated data. However, some of these platforms are really compact with no space for any calibration hardware. One of the examples is the use of other large missions as transfer radiometer for earth imaging CubeSat constellations. We also have to look at other options like vicarious methods and other innovative techniques to acquire scientifically meaningful data.

The session is a high-level forum bringing together scientists and technologists involved in the research, design, and development of CubeSats for Earth Remote Sensing applications. This session comprises invited presentations by scientists presenting the data from past and present missions and show science traceability by comparing them with data from large missions. For example data from TEMPEST-D, HARP, TROPICS will be presented with their approach to calibration and validation.

In total there will be total of five presentations, including the overview and four missions presenting their latest data with their calibration approach.

The HyTI (Hyperspectral Thermal Imager) mission, funded by NASA’s Earth Science Technology Office InVEST (In-Space Validation of Earth Science Technologies) program, will demonstrate how high spectral and spatial long-wave infrared image data can be acquired from a 6U CubeSat platform. The mission will use a spatially modulated interferometric imaging technique to produce spectro-radiometrically calibrated image cubes, with 25 channels between 8 to 10.7 microns, at 13 wavenumber resolution), at a ground sample distance of approximately 60 m. The HyTI performance model indicates narrow band NEdTs of less than0 .3 K. The small form factor of HyTI is made possible via the use of a no-moving-parts Fabry-Perot interferometer, and JPL’s cryogenically-cooled (NEdT requirement can be met at dark current associated with an FPA temperature of 68 K) HOT-BIRD FPA technology. Launch is scheduled for summer 2022. The value of HyTI to Earth scientists will be demonstrated via on-board processing of the raw instrument data to generate L1 and L2 products, with a focus on rapid delivery of data regarding volcanic degassing, and land surface temperature.

HyTI uses JPL's T2SLS ‘HOT-BIRD’ focal plane array. T2SLS detectors exhibit high levels of temporal stability with respect to both gain and offset, making them an ideal candidate for HyTI, as the 6U form-factor left no room for an onboard radiometric calibration mechanism. Rather, prior to launch, the HyTI instrument will be calibrated by deriving look-up tables relating target radiance and sensor response for a suite of FPA integration times and temperatures. On-orbit, data will be calibrated to spectral radiance using these gain LUTs. It is anticipated that radiometric offset (obtained via deep space look) will be updated each orbit.

During operations the HyTI calibration will be validated using three sources: i) occasional Lunar imaging events, ii) vicarious calibration with Landsat TIIRS and Terra ASTER data sets, and iii) direct validation using the Jet Propulsion laboratory’s Lake Tahoe and Salton Sea calibration sites. This will be important, as HyTI will process from L0 to L1 on-orbit, and so the calibration must be validated (and if required, updated) in a timely manner (as most L0 data will not be transmitted to ground or archived).

In this presentation we will provide an overview of the HyTI measurement approach, the onboard data reduction and calibration approach and the spacecraft design.

The 3U Hyper-Angular Rainbow Polarimeter (HARP) Cubesat carries a compact hyper-angular imaging polarimeter (size of a small loaf of bread) aimed at the multiwavelength polarized imaging of Earth’s atmosphere from different viewing angle perspectives. The HARP system consists of a wide field of view lens, followed by a polarization optimized Philips prism, and three imaging sensors. Each sensor is furnished with a linear polarizer at a particular orientation, and a stripe filter that simultaneously select the sampled wavelengths, and the along track viewing angle for each pushbroom imager. HARP started data collection in April 2020 from the ISS orbit and is the first Hyper-Angular imaging polarimeter in space. The HARP payload produces pushbroom images at four wavelengths (440, 550, 670 and 870nm) with up to 60 viewing angles at 670 nm and up to 20 along track angles for the other three wavelengths. HARP swath consists of 94 degs in the cross track direction, allowing for a very wide coverage around the globe, and +/-57 degs in the along track direction, providing wide scattering angle sampling for aerosol and cloud particle retrieval. The HARP satellite is still active on orbit and so far have produce a large collection of scenes providing an unprecedented demonstration of the hyperangular retrieval of cloud and aerosol properties from space.

The HARP sensor was radiometrically and polarimetrically calibrated at the ground, and its post launch radiometric calibration have been intercompared with other satellite sensor including MODIS, VIIRS and ABI showing excellent results over its first year of operation. Multiple ground targets have been selected for calibration, including the high altitude lake Titicaca, in the border between Peru and Bolivia, which allow for polarized sunglint measurements with little atmospheric interference. A first cut in HARP’s post-launch polarimetric calibration has also being assessed by comparing the HARP measurements with multi-angle surface and atmospheric models. Results from this intercomparison will be discussed as part of this presentation.

In terms of level 2 performance, the Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm has being used for the detailed retrieval of aerosol and surface properties using HARP CubeSat data. GRASP has been applied to multiple HARP scenes producing retrievals of dust, smoke, pollution and other aerosol components, including the measurements of aerosol optical depth, real and complex refractive indices, particle sphericity, single scattering albedo, etc. These retrievals will be presented and discussed in detail showing dust transport from Africa, forest fire smoke, etc. HARP has also performed the first ever hyper-angular retrieval of cloud microphysical properties using cloudbow measurements. Different than previous retrievals, the hyperangular measurements from HARP allow for cloudbow measurements with pixel resolution rather than using a composite of a large area as it has been performed by the POLDER instrument.

This presentation will discuss the performance of the HARP sensor in space tracked by intercomparisons with other satellites and general ground-based data sets. The HARP payload is a precursor to the HARP-2 polarimeter that will fly on the NASA PACE mission to collect global data on aerosol and cloud particles, which will also be introduced as part of this talk.

Temporal Experiment for Storms and Tropical Systems – Demonstration (TEMPEST-D) is a nearly 3-year NASA mission to demonstrate global observations from a multi-frequency microwave sensor deployed on a 6U CubeSat platform. TEMPEST was proposed in 2013 as an Earth Venture Instrument-2 to perform high temporal resolution observations of rapidly evolving storms using a constellation of five identical CubeSats with microwave sensors in a single orbital plane, providing 7-minute temporal sampling of rapidly-developing convective activity over 30 minutes. To demonstrate necessary capability to successfully operate the TEMPEST constellation, NASA’s Earth Venture Technology program funded the production, deployment and operation of a TEMPEST-D, a multi-frequency microwave radiometer on a 6U CubeSat, which was successfully delivered for launch less than 2 years after PDR.

TEMPEST-D was deployed from the ISS into low Earth orbit on July 13, 2018, and observed the Earth’s atmosphere nearly continuously until it re-entered on June 21, 2021. TEMPEST-D performed the first global Earth observations from a multi-frequency microwave radiometer on a CubeSat. The TEMPEST-D mission substantially exceeded expectations in terms of data quality, stability, consistency and mission duration. TEMPEST-D data were validated through inter-calibration with existing scientific and operational microwave sensors measuring at similar frequencies, including 4 MHS sensors on NOAA-19, MetOp-A, -B and -C, as well as GPM/GMI. These validation results showed that TEMPEST had comparable or better performance to much larger operational sensors in terms of instrument noise, calibration accuracy, precision and stability throughout the nearly 3-year mission.

TEMPEST-D performed detailed observations of the microphysics of hurricanes, typhoons and tropical cyclones during three consecutive hurricane seasons. Simultaneous observations by TEMPEST-D and JPL’s RainCube weather radar demonstrated physical consistency and well-correlated passive and active microwave measurements of severe weather from the two CubeSats. Quantitative precipitation estimates retrieved from TEMPEST-D data are highly correlated with standard ground radar precipitation products. TEMPEST-D also performed along-track scanning measurements constituting the first space-borne demonstration of “hyperspectral” microwave sounding observations to retrieve the height of the planetary boundary layer.

The stability, accuracy and reliability of the TEMPEST-D instrument aboard a 6U CubeSat opens a breadth of possibilities for future science missions to substantially improve the temporal resolution of cloud and precipitation observations. Together, the TEMPEST-D and RainCube CubeSat missions demonstrated the necessary technology and scientific potential to deploy coordinated constellations of small satellites with heterogeneous microwave sensors to improve understanding of microphysical processes of both clouds and precipitation.

The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission will provide nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. TROPICS will provide rapid-refresh microwave measurements (median refresh rate of approximately 50 minutes for the baseline mission) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. The TROPICS comstellation mission comprises six CubeSats in three low-Earth orbital planes. Each CubeSat will host a high performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles. TROPICS spatial resolution and measurement sensitivity is comparable with current state-of-the-art observing platforms. Launches for the TROPICS constellation mission are planned in 2022. NASA’s Earth System Science Pathfinder (ESSP) Program Office approved the separate TROPICS Pathfinder mission, which launched on June 30, 2021, in advance of the TROPICS constellation mission as a technology demonstration and risk reduction effort. The TROPICS Pathfinder mission has provided an opportunity to checkout and optimize all mission elements prior to the primary constellation mission. This presentation will describe the instrument checkout and calibration/validation plans and progress for the TROPICS Pathfinder mission and will include first light mission results and comparisons with current operational instruments, such as the Advanced Technology Microwave Sounder (ATMS). We will also discuss plans for the constellation mission, including recent activities to improve the data latency for near-real-time forecasting applications.

SigNals Of Opportunity: P-band Investigation (SNOOPI) will be the first on-orbit demonstration of remote sensing using Signals of Opportunity (SoOp) in P-band (240-380 MHz). P-band SoOp has the potential for spaceborne remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE), two variables identified as priorities in NASA’s 2017-2027 Decadal Survey for Earth Science and Applications from Space. P-band is needed to penetrate through dense vegetation to sense RZSM. The longer wavelength of P-band also increases the unwrapping interval for phase observations, which may enable a new measurement of SWE.

SNOOPI is a technology validation mission with three specific goals to test important assumptions about P-band reflectometry form orbit. First, to collect data from orbit and validate the signal scattering model, most importantly the assumption of a coherent signal. Second, to investigate possible effects of RFI on the measurements, given the prevalence of other sources in these frequencies. Third, to demonstrate robustness to uncertainty in the source location and signal strength. The SoOp observable will be used to estimate complex reflection coefficient over varied topographical land surface conditions and will be compared with forward models driven by in-situ data.

In support of the mission, analysis have been performed on key instrument and mission parameters. Evaluation of the orbital coverage of the spacecraft based on launch date are used to create instrument recording schedules. Priorities for these recordings are overpasses of SMAP Calibration/Validation (cal/val) sites, arcs of data over winter snow-covered regions and tracks on the eastern Contiguous United States (CONUS). Results from an SoOp retrieval analytical model are verified using a bit-level signal and instrument simulator. A ground-based station is designed (soon to be deployed) to monitor the noncooperative sources, in order to reduce risk due to uncertainty in knowledge of the broadcast power, spectrum shape, and orbital position of the transmitter source. SNOOPI is scheduled for delivery in August-2022.