Spatiotemporal Evolution of XCO2 in East Asia (2016–2024) Across Different Climate Zones Based on GOSAT and OCO-2 Data Fusion

Although satellite sensors provide global observations, factors such as cloud interference and narrow swath widths frequently result in partial data gaps which constrain the continuous spatiotemporal analysis of the column-averaged dry air mole fraction of CO2 (XCO2). To address this challenge, this study develops a novel multi-stage fusion framework that integrates GOSAT and OCO-2 data using inverse error variance weighting and a dynamic bias correction technique, generating a seamless monthly XCO2 dataset for East Asia (2016–2024). Validation against TCCON measurements (RMSE = 1.22 ppm; R2 = 0.96) and WDCGG data (RMSE = 2.85 ppm; R2 = 0.76) demonstrates the high accuracy of the product. The results show that the growth rate consistently exceeds 2.2 ppm/year, with clear seasonal patterns characterized by spring maxima and summer minima. Spatially, the locus of rapid growth has shifted toward central and western China, reflecting patterns of regional economic development, while substantial concentrations still persist in the industrialized regions of eastern China, Japan, and South Korea. This study provides new insights into regional atmospheric CO2 dynamics and emphasizes the efficacy of dynamic bias correction in data fusion.