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.