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.

翻译：自然气候解决方案为缓解二氧化碳排放提供了一种途径。然而，在大范围地理区域内监测生态系统的碳吸收仍具挑战性。涡动协方差通量塔为基于卫星产品构建的预测性‘升尺度’模型提供了地面真值，但许多卫星当前观测的空间尺度小于通量塔的足迹范围。我们提出了足迹感知回归，这是一种首创的深度学习框架，能够同时预测空间足迹和像素级（30米尺度）的碳通量估计值。该模型基于我们构建的AMERI-FAR25数据集进行训练，该数据集整合了439个站点年的塔基数据与相应的Landsat影像。我们的模型能够生成高分辨率预测结果，在测试多种生态系统的月净生态系统交换量时达到了R2 = 0.78的预测精度。