Using Geostationary Satellite Observations and Machine Learning Models to Estimate Ecosystem Carbon Uptake and Respiration at Half Hourly Time Steps at Eddy Covariance Sites.

Polar‐orbiting satellites have significantly improved our understanding of the terrestrial carbon cycle, yet they are not designed to observe sub‐daily dynamics that can provide unique insight into carbon cycle processes. Geostationary satellites offer remote sensing capabilities at temporal resolutions of 5‐min, or even less. This study explores the use of geostationary satellite data acquired by the Geostationary Operational Environmental Satellite—R Series (GOES‐R) to estimate terrestrial gross primary productivity (GPP) and ecosystem respiration (RECO) using machine learning. We collected and processed data from 126 AmeriFlux eddy covariance towers in the Contiguous United States synchronized with imagery from the GOES‐R Advanced Baseline Imager (ABI) from 2017 to 2022 to develop ML models and assess their performance. Tree‐based ensemble regressions showed promising performance for predicting GPP (R2 of 0.70 ± 0.11 and RMSE of 4.04 ± 1.65 μmol m−2 s−1) and RECO (R2 of 0.77 ± 0.10 and RMSE of 0.90 ± 0.49 μmol m−2 s−1) on a half‐hourly time step using GOES‐R surface products and top‐of‐atmosphere observations. Our findings align with global efforts to utilize geostationary satellites to improve carbon flux estimation and provide insight into how to estimate terrestrial carbon dioxide fluxes in near‐real time. Plain Language Summary: Fighting climate change requires an understanding of how ecosystems absorb and release carbon dioxide. While most Earth‐orbiting satellites provide limited snapshots, this study explores how more frequent imagery—every 5 min—from geostationary satellites, also known as weather satellites, can be used to estimate ecosystem carbon dioxide flux. By combining this data with machine learning techniques, we successfully estimated carbon uptake and release at over 100 US sites at half‐hourly intervals. This paves the way for near‐real‐time global monitoring of carbon exchange, offering a powerful tool for scientists and policymakers tackling climate change. Key Points: Advanced Baseline Imager (ABI) observations can estimate sub‐daily ecosystem carbon uptake and respiration at 126 AmeriFlux sitesIntegration of top‐of‐atmosphere products enhances the accuracy of monitoring land surface functionsABI observations can fill eddy covariance data gaps: up to 1 week (high accuracy), 6 months (low accuracy) [ABSTRACT FROM AUTHOR]