XCO2 Data Full-Coverage Mapping in China Based on Random Forest Models - Ruizhi Chen; Zhongting Wang; Chunyan Zhou; Ruijie Zhang; Huizhen Xie; Huayou Li (Remote Sensing)
Carbon dioxide (CO2) is a key driver of global climate change. Since the Industrial Revolution, the rapid rise in atmospheric CO2 levels has significantly intensified global warming and climate-related issues. To accurately and promptly monitor changes in CO2 concentrations and to support the development of climate policies, this study proposes a method based on random forest models to generate a continuous monthly dataset of CO2 column concentration (XCO2) across the entire Chinese region from 2004 to 2023. The study integrates XCO2 satellite observations from SCIAMACHY, GOSAT, OCO-2, and GF-5B, alongside nighttime light remote sensing data, meteorological parameters, vegetation indices, and CO2 profile data. Using the random forest algorithm, a complex relationship model was established between XCO2 concentrations and various environmental variables. The goal of this model is to provide XCO2 estimates with enhanced spatial coverage and accuracy. The XCO2 concentrations predicted by the model show a high level of consistency with satellite observations, achieving a correlation coefficient (R-value) of 0.9959 and a root mean square error (RMSE) of 1.1631 ppm. This indicates that the model offers strong predictive accuracy and generalization ability. Additionally, ground-based validation further confirmed the model’s effectiveness, with a correlation coefficient (R-value) of 0.956 when compared with TCCON site observation data.