High-resolution upscaling of ecosystem carbon fluxes using Sentinel-2 and explainable AI

High-resolution estimates of ecosystem carbon dioxide exchange are essential for interpreting atmospheric CO₂ observations and quantifying natural and anthropogenic carbon budgets across spatial scales. Most state-of-the-art data-driven biosphere models rely on MODIS or VIIRS products at 500 m resolution to upscale eddy-covariance flux measurements, despite the strong spatial heterogeneity of many landscapes and the limited representativeness of individual flux towers. Recent advances in satellite remote sensing, particularly the Sentinel-2 constellation, enable data-driven upscaling of ecosystem carbon fluxes at 10 m resolution and offer new opportunities to better align reference measurements with model inputs.

In this contribution, we present a novel explainable machine-learning framework that combines Sentinel-2 observations with meteorological data to predict net ecosystem exchange, gross primary productivity, and ecosystem respiration across a wide range of ecosystem types in Europe, including different crop species. A key methodological aspect is the explicit alignment of eddy-covariance footprint estimates with high-resolution Sentinel-2 data, which improves model training under non-independent and spatially heterogeneous reference data conditions typical of European landscapes.

We demonstrate that this footprint-aware upscaling strategy leads to improved flux estimates and more robust spatial predictions. Using explainable AI techniques, we further analyse feature contributions and extract ecosystem-specific temperature dependencies of photosynthesis and respiration, enhancing process understanding beyond purely predictive performance. Finally, we show how the resulting models can be applied to generate spatially explicit CO₂ flux maps from urban to continental scales while accounting for the representativeness of individual flux towers and reducing extrapolation artefacts.