Estimation of High Spatial Resolution CO2 Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite Data

The increase in the carbon dioxide (CO2) concentration is a major driver of global warming, presenting significant challenges to ecosystems and human societies. Satellite remote sensing technology can monitor the continuous spatial variation of the atmospheric CO2 column concentration (XCO2), but its global application is limited by the narrow observational swath. To address this, this study effectively integrates XCO2 data retrieved from the GOSAT and OCO-2 satellites using atmospheric profile adjustment and spatial grid integration techniques. Based on this, a multi-machine learning ensemble algorithm (MLE) was developed, which successfully estimated the spatially continuous XCO2 concentration in China from 2010 to 2022 (ChinaXCO2-MLE). The results indicate that, compared to individual satellite observations, the integration of multi-source satellite XCO2 data significantly improves the spatiotemporal coverage. The overall R2 of the MLE model was 0.97, with an RMSE of 0.87 ppmv, outperforming single machine learning models. The ChinaXCO2-MLE shows good consistency with the observational records from two background stations in China, with R2 values of 0.93 and 0.78, and corresponding RMSEs of 1.00 ppmv and 1.32 ppmv. This study also reveals the seasonal and regional variations in China’s XCO2 concentration: the highest concentration occurs in spring, the lowest concentration occurs in northern regions during summer, and the lowest concentration occurs in southern regions during autumn. From 2010 to 2022, the XCO2 concentration continued to rise, but the growth rate has slowed due to the implementation of air pollution prevention and energy conservation policies. The spatially continuous XCO2 data provide a more comprehensive understanding of carbon variation and offer a valuable reference for achieving China’s carbon neutrality goals.