Evaluating Satellite-Based FluxSat Gross Primary Production Trends with Eddy Covariance Data and Extending the FluxSat Climate Data record

Gross Primary Production (GPP) is the amount of carbon dioxide (CO₂) plants fix through photosynthesis. The ability to detect global and regional temporal variations in GPP, including trends, is critical to our understanding of the carbon cycle in a changing climate. FluxSat is a globally gridded satellite-based terrestrial GPP dataset produced using a neural network model trained with reflectance data from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites and photosynthetically active radiation at the top of the atmosphere, calibrated to ground-based eddy covariance (EC) data, from 2000-present. We compare GPP trends for FluxSat to collocated EC sites and find that FluxSat is able to capture the sign of the ground-based GPP trends, although it underestimates the magnitude of these trends. Additionally, we find that FluxSat more closely reproduces GPP trends at EC sites that have collected data for longer time periods. In future updates to FluxSat, we intend to extend this data record using the same machine-learning modeling approach, both forward in time with the Visible Infrared Imaging Radiometer Suite (VIIRS) and backward in time using the Advanced Very High Resolution Radiometer (AVHRR) reflectances back to 1981 to create a homogenized long-term GPP record spanning over 40 years.