用深度学习精准预测不同生态系统的碳通量，精度达0.78。
A Footprint-Aware, High-Resolution Approach for Carbon Flux Prediction Across Diverse Ecosystems
- 基于遥感与塔观测数据，联合建模碳通量空间分布
- 30米分辨率下月度碳通量预测R²达0.78
- 适合需要高精度碳汇评估的研究与政策制定
自然气候解决方案（NCS）为减缓二氧化碳排放提供了途径。然而，在大范围地理区域内监测生态系统碳汇仍具挑战性。涡流相关塔提供了用于推演卫星产品预测模型的地面实况数据，但许多卫星如今的测量尺度已小于通量塔的足迹范围。本文提出首个基于深度学习的足迹感知回归框架（FAR），可同时预测空间足迹与像素级（30米尺度）的碳通量。FAR在包含439个站点年份的AMERI-FAR25数据集上训练，该数据集融合了塔观测数据与对应陆地卫星影像。模型生成高分辨率预测，在多种生态系统测试站点上的月度净生态系统交换预测达到R²=0.78。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。