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

Abstract In this study, we present the development and validation of a microwave‐based regional gross primary productivity (GPP) estimation method, EDVI‐GPP, using the Emissivity Difference Vegetation Index (EDVI) retrieved from the China's Fengyun‐3B satellite over East Asia for the period 2016–2018. Given the common issue of cloud cover contamination in optical remote sensing, microwave remote sensing is explored as a viable alternative due to its ability to penetrate clouds. Our approach is substantiated with in situ GPP measurements from 18 eddy covariance flux sites and comparative analysis against four satellite‐derived GPP products. At a daily scale, EDVI‐GPP demonstrated proficiency in capturing day‐to‐day variations of GPP on a regional scale, exhibiting a strong correlation with in situ measurements. When extended to an 8‐day temporal resolution, EDVI‐GPP correlations ( R 2 = 0.51) are comparable to MODIS‐GPP ( R 2 = 0.59), FLUXCOM‐GPP ( R 2 = 0.66), GLASS‐GPP ( R 2 = 0.53), and VODCA2‐GPP ( R 2 = 0.13), with a reduced bias of −0.84 gC/m 2 /day. Notably, under moderate to heavy cloud cover, the method maintained superior performance, suggesting resilience to cloud interference. On a regional scale, EDVI‐GPP exhibited spatial consistency and high spatiotemporal correlation with the compared GPP products ( R = 0.69–0.83). Such robust correlations lay the groundwork for the method's application across broader geographical extents. The annual averaged EDVI‐GPP of China was 6.00 Pg C yr −1 , which was in close agreement with other published estimates and thereby supported China's carbon peak and carbon neutrality objectives. This research marks a pioneering effort to incorporate microwave‐derived variables into daily GPP estimation on a regional scale, with potential for global application, providing a less cloud‐affected and reliable measurement.