Global Gross Primary Productivity Estimation Using Passive Microwave Observations From China's Fengyun‐3D Satellite

Abstract In this study, we present the development and validation of a microwave‐based global gross primary productivity (GPP) estimation method, EDVI‐GPP, using the microwave emissivity difference vegetation index (EDVI) retrieved from the China's Fengyun‐3D satellite for the period 2020–2022. The global EDVI‐GPP model incorporates the effect of diffuse radiation from clouds on GPP and utilizes the Shuffled Complex Evolution‐University of Arizona (SCE‐UA) algorithm to tune its key parameters of light use efficiency and the seasonality of the vegetation water content index. In situ GPP measurements from 163 eddy covariance flux sites covering 10 major ecosystem types around the globe are used to calibrate and validate the model performance. EDVI‐GPP provides daily temporal resolution GPP estimations applicable under both clear and cloudy skies. At 8‐day temporal resolution, the coefficients of determination between EDVI‐GPP and in situ GPP measurements ( R 2 = 0.51) during 2020–2022 are comparable to several mainstream GPP products (FLUXCOM‐GPP, MODIS‐GPP, GLASS‐GPP, PML‐GPP, and GOSIF‐GPP), with a reduced bias of −0.46 gC m −2 day −1 . On a global scale, the annual‐averaged EDVI‐GPP exhibits high spatial consistency ( R = 0.71–0.74) with the compared GPP products. The EDVI‐GPP model quantifies the mean global GPP as 123.77 ± 1.33 Pg C yr −1 from 2020 to 2022, which is in close agreement with other published estimates. By using the three‐cornered hat (TCH) method to evaluate GPP uncertainty, we find that EDVI‐GPP exhibits a large uncertainty in agricultural areas. This research incorporates microwave‐derived variables into daily GPP estimation on a global scale, providing a less cloud‐affected and reliable measurement.