UC Berkeley

Accurate estimation of gross primary production (GPP), the amount of carbon absorbed by plants via photosynthesis, is of great importance for understanding ecosystem functions, carbon cycling, and climate-carbon feedbacks. Remote sensing has been widely used to quantify GPP at regional to global scales. However, polar-orbiting satellites (e.g., Landsat, Sentinel, Terra, Aqua, Suomi NPP, JPSS, OCO-2) lack the capability to examine the diurnal cycles of GPP because they observe the Earth's surface at the same time of day. The Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), launched in June 2018, observes the land surface temperature (LST) at different times of day with high spatial resolution (70 m × 70 m) from the International Space Station (ISS). Here, we made use of ECOSTRESS data to predict instantaneous GPP with high spatial resolution for different times of day using a data-driven approach based on machine learning. The predictive GPP model used instantaneous ECOSTRESS LST observations along with the daily enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), land cover type from the National Land Cover Database (NCLD), and instantaneous meteorological data from the ERA5 reanalysis dataset. Our model estimated instantaneous GPP across 56 flux tower sites fairly well (R2 = 0.88, Root Mean Squared Error (RMSE) = 2.42 μmol CO2 m−2 s−1). The instantaneous GPP estimates driven by ECOSTRESS LST captured the diurnal variations of tower GPP for different biomes. We then produced multiple high resolution ECOSTRESS GPP maps for the central and northern California. We found distinct changes in GPP at different times of day (e.g., higher in late morning, peak around noon, approaching zero at dusk), and clear differences in productivity across landscapes (e.g., savannas, croplands, grasslands, and forests) for different times of day. ECOSTRESS GPP also captured the seasonal variations in the diurnal cycling of photosynthesis. This study demonstrates the feasibility of using ECOSTRESS data for producing instantaneous GPP (i.e., GPP for the acquisition time of the ECOSTRESS data) for different times of day. The ECOSTRESS GPP can shed light on how plant photosynthesis and water use vary over the course of the diurnal cycle and inform agricultural management and future improvement of terrestrial biosphere/land surface models.