Rapid changes in terrestrial carbon dioxide uptake captured in near-real time from a geostationary satellite: The ALIVE framework

作者：Danielle Losos, Sadegh Ranjbar, Sophie Hoffman, Ryan Abernathey, Ankur R. Desai, Jason A. Otkin, Helin Zhang, Youngryel Ryu, Paul C. Stoy · 发表于：Remote Sensing of Environment · 年份：2025 · DOI：10.1016/j.rse.2025.114759 · 被引用次数：6 · 研究领域：Atmospheric and Environmental Gas Dynamics、Meteorological Phenomena and Simulations、Climate variability and models

The terrestrial carbon cycle responds to human activity, ecosystem dynamics, and weather and climate variability including extreme events. Satellite remote sensing has transformed our ability to estimate ecosystem carbon dioxide uptake, the gross primary productivity (GPP), with increasing accuracy and spatial resolution. Many aspects of terrestrial carbon cycling happen quickly on sub-daily or daily scales. These dynamics may not be captured at the temporal scales of typical remote sensing products from polar orbiting satellites – often multiple days or longer. Imagers onboard geostationary satellites measure the Earth system at “hypertemporal” time scales of minutes or less and often have the spectral capabilities to estimate GPP and other surface-atmosphere fluxes using established approaches. Here, we use observations and data products from the Advanced Baseline Imager (ABI) on the Geostationary Environmental Operational Satellite – R Series (GOES-R) to create ALIVE GPP ( A dvanced Baseline Imager L ive I maging of V egetated E cosystems), a GPP product that provides open data on the native five-minute basis of GOES-R CONUS scenes with latency under one day. Our machine learning model, trained on GPP estimates from 111 eddy covariance flux towers with 276 site-years of data spanning tropical to boreal ecosystems, captures up to 70 % of the observed variability when 20 % of tower sites are withheld, with R 2 values of 0.78 (0.82) when aggregating to daily (weekly) periods....