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.
@article{arxiv.2512.01917,
title = {A Footprint-Aware, High-Resolution Approach for Carbon Flux Prediction Across Diverse Ecosystems},
author = {Jacob Searcy and Anish Dulal and Scott Bridgham and Ashley Cordes and Lillian Aoki and Brendan Bohannan and Qing Zhu and Lucas C. R. Silva},
journal= {arXiv preprint arXiv:2512.01917},
year = {2025}
}