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

• URL: http://arxiv.org/abs/2512.01917v1
• Date: Mon, 01 Dec 2025 17:34:41 GMT
• Title: A Footprint-Aware, High-Resolution Approach for Carbon Flux Prediction Across Diverse Ecosystems
• Authors: Jacob Searcy, Anish Dulal, Scott Bridgham, Ashley Cordes, Lillian Aoki, Brendan Bohannan, Qing Zhu, Lucas C. R. Silva,
• Abstract summary: Footprint-Aware Regression (FAR) is a first-of-its-kind, deep-learning framework that simultaneously predicts spatial footprints and pixel-level (30 m scale) estimates of carbon flux.<n>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.
• Score: 8.658824757360104
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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