Carbon, water and energy fluxes between the land and the atmosphere strongly depend on the functioning of ecosystems. Yet, understanding, characterizing and predicting the role of land on climate is intrinsically challenging, not only because of the complexity of the processes involved, but also by the large heterogeneity of the land surface itself. This spatial variation results from local climatological, topographical and edaphic conditions, but also by the great diversity in vascular plants, in how they are organized in distinctive ecosystems, in how these are structured within the landscape, and how these landscapes have been shaped and altered by humans for millennia.
The Ecosystem Function from Earth Observation group aims at rethinking how we quantify and map ecosystem functional properties from space by synergistically exploiting the increasing diversity of complementary satellite data streams that are currently available. This involves redesigning how we combine these signals together by integrating process-based understanding and data-driven approaches. A key focus is on exploring the complexity and diversity of terrestrial ecosystems, and how their specific functional properties affect land-atmosphere interactions. Topics explored under this umbrella include: (i) improving estimations of carbon, water and energy fluxes; (ii) studying the role of biodiversity (specifically functional diversity) to improve ecosystem resilience; (iii) exploring the biophysical effects of land use and land management on climate.
Joint project ITMS Integrated Greenhouse Gas Monitoring System for Germany - Module B (demonstration phase) Duration: 08/2022 - 07/2026 PI/Co-PIs: Gregory Duveiller
ITMS-QS II -TORCH
Joint project ITMS Integrated Greenhouse Gas Monitoring System for Germany - Characterization of uncertainty for high-resolution AI-based biogenic CO2 fluxes Duration: 12/2023 - 11/2026 PI/Co-PIs: Martin Jung
Earth system models are the basis for understanding and projecting climate change. Despite progress in the field, the models’ ability to simulate both global and regional Earth system responses is limited by the representation of physical and biological small-scale processes. The EU-funded USMILE project will use machine learning to improve modelling and understanding of the Earth system.
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The mission of Open-Earth-Monitor is to accelerate uptake of environmental information to guide current and future users in research, decision-making and citizens toward the most sustainable solutions.
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The objective of the Sen4GPP project is to improve our knowledge on the spatio-temporal variations of GPP associated with terrestrial ecosystems by a synergistic exploitation of the complementary information (Land Surface Temperature – LST, Solar-Induced Fluorescence – SIF, Fraction of Absorbed Photosynthetically Active Radiation – FAPAR and land cover) provided by the Sentinel missions (Sentinel-2, Sentinel-3 and Sentinel-5P) at multiple spatial and temporal resolutions, as well as other Earth Observation and in situ data.
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Dunn, R. J. H.; Aldred, F.; Gobron, N.; Miller, J. B.; Willett, K. M.; Ades, M.; Adler, R. R.; Allan, P.; Allan, R.; Anderson, J.et al.; Argüez, A.; Arosio, C.; Augustine, J. A.; Azorin-Molina, C.; Barichivich, J.; Beck, H. E.; Becker, A.; Bellouin, N.; Benedetti, A.; Berry, D. I.; Blenkinsop, S.; Bock, O.; Bodin, X.; Bosilovich, M. G.; Boucher, O.; Buehler, S. A.; Calmettes, B.; Carrea, L.; Castia, L.; Christiansen, H. H.; Christy, J. R.; Chung, E.-S.; Coldewey-Egbers, M.; Cooper, O. R.; Cornes, R. C.; Covey, C.; Cretaux, J.-F.; Crotwell, M.; Davis, S. M.; de Jeu, R. A. M.; Degenstein, D.; Delaloye, R.; Girolamo, L. D.; Donat, M. G.; Dorigo, W. A.; Durre, I.; Dutton, G. S.; Duveiller, G.; et, a.; Zotta, R. M.: Global Climate. Bulletin of the American Meteorological Society 102 (8), pp. 11 - 142 (2023)
Nath, S.; Gudmundsson, L.; Schwaab, J.; Duveiller, G.; De Hertog, S. J.; Guo, S.; Havermann, F.; Luo, F.; Manola, I.; Pongratz, J.et al.; Seneviratne, S. I.; Schleussner, C. F.; Thiery, W.; Lejeune, Q.: TIMBER v0.1: a conceptual framework for emulating temperature responses to tree cover change. Geoscientific Model Development 16 (14), pp. 4283 - 4313 (2023)
Pacheco-Labrador, J.; de Bello, F.; Migliavacca, M.; Ma, X.; Carvalhais, N.; Wirth, C.: A generalizable normalization for assessing plant functional diversity metrics across scales from remote sensing. Methods in Ecology and Evolution 14 (8), pp. 2123 - 2136 (2023)
