A Footprint-Aware, High-Resolution Approach for Carbon Flux Prediction Across Diverse Ecosystems

תקציר מקורי באנגליתarXiv:2512.01917v2 Announce Type: replace Abstract: Eddy-covariance (EC) flux towers provide in situ measurements of $CO_2$ flux and serve as the ground-truth data for predictive `upscaling' models derived from satellite products. However, many satellites now resolve spatial scales smaller than an EC tower's footprint. We show theoretically that upscaling models trained on high-resolution data in heterogeneous landscapes must account for an EC tower's footprint to avoid bias in pixel-level predictors. To address this problem, we introduce Footprint-Aware Regression (FAR), a deep-learning framework that simultaneously predicts spatial footprints and pixel-level estimates of $CO_2$ flux, and show it yields unbiased pixel-level predictions given sufficient training data. We demonstrate FAR on