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 (R² = 0.51) are comparable to MODIS-GPP (R² = 0.59), FLUXCOM-GPP (R² = 0.66), GLASS-GPP (R² = 0.53), and VODCA2-GPP (R² = 0.13), with a reduced bias of −0.84 gC/m²/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.