{"ID":2834630,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.01917","arxiv_id":"2512.01917","title":"A Footprint-Aware, High-Resolution Approach for Carbon Flux Prediction Across Diverse Ecosystems","abstract":"Natural climate solutions (NCS) offer an approach to mitigating carbon dioxide (CO2) emissions. However, monitoring the carbon drawdown of ecosystems over large geographic areas remains challenging. Eddy-flux covariance towers provide ground truth for predictive 'upscaling' models derived from satellite products, but many satellites now produce measurements on spatial scales smaller than a flux tower's footprint. We introduce Footprint-Aware Regression (FAR), a first-of-its-kind, deep-learning framework that simultaneously predicts spatial footprints and pixel-level (30 m scale) estimates of carbon flux. FAR is trained on our AMERI-FAR25 dataset which combines 439 site years of tower data with corresponding Landsat scenes. Our model produces high-resolution predictions and achieves R2 = 0.78 when predicting monthly net ecosystem exchange on test sites from a variety of ecosystems.","short_abstract":"Natural climate solutions (NCS) offer an approach to mitigating carbon dioxide (CO2) emissions. However, monitoring the carbon drawdown of ecosystems over large geographic areas remains challenging. Eddy-flux covariance towers provide ground truth for predictive 'upscaling' models derived from satellite products, but m...","url_abs":"https://arxiv.org/abs/2512.01917","url_pdf":"https://arxiv.org/pdf/2512.01917v1","authors":"[\"Jacob Searcy\",\"Anish Dulal\",\"Scott Bridgham\",\"Ashley Cordes\",\"Lillian Aoki\",\"Brendan Bohannan\",\"Qing Zhu\",\"Lucas C. R. Silva\"]","published":"2025-12-01T17:34:41Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}