2026-10-07, 14:45–15:00 (Europe/Madrid), Rooms 12+14
Gross Primary Productivity (GPP) is an essential variable to monitor carbon uptake by terrestrial ecosystems. Mapping GPP is thus of great interest to establish estimates of the global carbon budget, especially when this needs to be done at regional scale such as with the 'REgional Carbon Cycle Assessment and Processes' (RECCAP) initiative coordinated by the Global Carbon Project. While GPP is not directly measurable from space by remote sensing, sun-induced chlorophyll fluorescence (SIF) can, and it has been shown to correlate well with GPP. SIF is a weak signal that can be retrieved from hyperspectral remote sensing and which is emitted by the photosynthetic machinery itself. The downside is that current and past satellite instruments from which SIF can be derived have a very coarse spatial resolution (5 km at best) and are usually very noisy due to the subtle nature of the signal. In this use case, we have thus developed some tools to improve the situation. First, we collaborated to derive a new retrieval method leveraging on machine learning to reduce the noise. Second, we adapted a spatial enhancement methodology enabling users to increase the spatial resolution by combining other remote sensing variables with a simple modelling framework of their choice. This was done on the Copernicus Data Space Ecosystem (CDSE) using the OpenEO framework, allowing users to design their own downscaling approach and applying it lazily to their zones of interest. To compare with in-site GPP estimations from eddy covariance flux towers, we further designed a methodology to characterize the heterogeneity around these towers based on data cubes of Sentinel-2 data.
More details on this OEMC use case can be found in the GEO Knowledge Hub under this link: https://gkhub.earthobservations.org/packages/qz6qd-rk354
Open-Earth-Monitor Cyberinfrastructure (Grant agreement ID: 101059548), Other
Gregory Duveiller holds a PhD in agronomical science and biological engineering from the Université catholique of Louvain (UCLouvain), Belgium. After his PhD, he spent 10 years working at the European Commission Joint Research Centre (JRC), in Ispra, Italy. He has specialized in developing methods to combine different satellite remote sensing data streams to better monitor and understand land processes, including crop yield monitoring, land cover change and land-atmosphere interactions. Since 2021 he is a project group leader at the Max Planck Institute for Biogeochemistry in Jena, Germany. His main research aims at improving our understanding of the role of terrestrial ecosystems in the Earth System by using data-driven yet process-based thinking applied to satellite Earth Observation data. 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.