Transforming GPP Estimation in Terrestrial Ecosystems using Remote Sensing and Transformers

Gross Primary Productivity (GPP) is a critical measure of carbon uptake by terrestrial vegetation, essential for understanding the global carbon cycle and developing climate mitigation strategies. This study introduces a novel approach to estimating annual GPP in Europe and North America using a deep tabular model that integrates remote sensing data from Google Earth Engine with the FT-Transformer architecture. We utilized transfer learning to pre-train the model on extensive MOD17A2H/A3H (MOD17) data and fine-tuned it with FLUXNET station data, addressing the scarcity of in-situ measurements. Our results demonstrate that the proposed model provides more accurate GPP estimates compared to traditional MOD17 values, reflecting a closer alignment with FLUXNET observations. This enhanced accuracy highlights the model’s ability to capture complex ecological and climatic interactions, offering a promising tool for advancing our understanding of terrestrial carbon dynamics. The full code repository is available
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