Half-Hourly Estimates of Ecosystem Carbon Dioxide Uptake from Space: Using GOES-R Observations and Ameriflux Data to Create a Real-Time GPP Product

Eddy covariance measurements are often used to infer gross primary productivity (GPP) on half-hourly time scales at the ecosystem scale. Remote sensing has revolutionized our ability to infer GPP across the globe at longer time scales of multiple days to years. We can learn more about the processes that control carbon fluxes by studying them at higher temporal resolutions, yet sub-daily estimates of GPP haven’t been available from space, until now. We describe an approach to estimate GPP in real-time across the Western Hemisphere using data from the Advanced Baseline Imager (ABI) on the Geostationary Observational Environmental Satellites - R Series (GOES-R). We coupled a well-established algorithm for inferring surface downwelling shortwave radiation (P) with estimates of the near infrared reflectance of vegetation (NIRv) from the new ABI surface reflectance product to create an NIRvP product. NIRvP is strongly related to GPP, and we used a model following Khan et al. (2022) to estimate GPP from NIRvP for different vegetation types. The resulting model creates a continuous estimate of GPP from the terrestrial surface that can be fused with other remotely sensed data products, applied to other geostationary satellite observations, and used to infer the impacts of disturbances and other rapid changes to carbon cycling.