Estimating terrestrial carbon dioxide and water vapor fluxes from geostationary satellites in near-real time: the ALIVE framework

The terrestrial carbon and water cycles can change rapidly in response to extreme events, human management, meteorological drivers, and more. Many established remote sensing-based estimates of terrestrial ecosystem function are not designed to observe such rapid changes. To address this shortcoming, we developed a machine learning (ML) approach trained on eddy covariance measurements to estimate terrestrial carbon dioxide and water vapor fluxes at five-minute intervals using geostationary (“weather”) satellite observations from the Advanced Baseline Imager on the GOES-R satellite series. Our approach, which we call ALIVE (Advanced Baseline Imager Live Imaging of Vegetated Ecosystems), creates a ‘hypertemporal’ view of terrestrial ecosystem functioning over the diurnal cycle and in near-real time. We will describe the training and testing procedure used to develop ALIVE with a focus on ML model skill and uncertainty estimation. We then compare ALIVE against the historical MODIS eight-day gross primary productivity (GPP) product to demonstrate how ALIVE can estimate the carbon cycle consequences of land management and extreme events to lead to an improved understanding of the carbon and water cycles. Comparisons during seasonal transitions and in response to extreme events including continental-scale wildfire smoke across North America in 2023 highlight the importance of rapid observations of land surface function. ALIVE is currently limited to the Conterminous United States (CONUS) and surrounding areas. We discuss the steps necessary to expand it, namely the availability of eddy covariance observations across the Americas and opportunities to apply the ALIVE framework to similar geostationary satellites worldwide like the new Meteosat-12 (formerly MTG-I1) which observes Africa, Europe, and parts of western Asia and is currently scheduled to become operational in Spring 2024. We also discuss opportunities to combine ALIVE estimates with other carbon and water cycle products in a remote sensing data fusion framework to help observe terrestrial ecosystems ‘everywhere, all the time’. By quantifying the carbon and water cycle across all of the time scales over which they vary, we hope to improve the ability of satellite remote sensing to understand our changing planet.