Getting SIF closer to GPP: developing tools towards reaching such goal (Gregory Duveiller)

The Open-Earth Monitor Global Workshop 2026 "Next-Generation EO & GeoAI Built on Openness and Distributed Data" 7-9 October 2026, Barcelona, Spain https://earthmonitor.org/global-workshop-2026/ 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.