High-Coverage Reconstruction of XCO₂ Using Multisource Satellite Remote Sensing Data in Beijing-Tianjin-Hebei Region

The extreme climate caused by global warming has had a great impact on the earth's ecology. As the main greenhouse gas, atmospheric CO₂ concentration change and its spatial distribution are among the main uncertain factors in climate change assessment. Remote sensing satellites can obtain changes in CO₂ concentration in the global atmosphere. However, some problems (e.g., low time resolution and incomplete coverage) caused by the satellite observation mode and clouds/aerosols still exist. By analyzing sources of atmospheric CO₂ and various factors affecting the spatial distribution of CO₂, this study used multisource satellite-based data and a random forest model to reconstruct the daily CO₂ column concentration (XCO₂) with full spatial coverage in the Beijing-Tianjin-Hebei region. Based on a matched data set from 1 January 2015, to 31 December 2019, the performance of the model is demonstrated by the determination coefficient (R²) = 0.96, root mean square error (RMSE) = 1.09 ppm, and mean absolute error (MAE) = 0.56 ppm. Meanwhile, the tenfold cross-validation (10-CV) results based on samples show R² = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm, and the 10-CV results based on spatial location show R² = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm. Finally, the spatially seamless mapping of daily XCO₂ concentrations from 2015 to 2019 in the Beijing-Tianjin-Hebei region was conducted using the established model. The study of the spatial distribution of XCO₂ concentration in the Beijing-Tianjin-Hebei region shows its spatial differentiation and seasonal variation characteristics. Moreover, daily XCO₂ map has the potential to monitor regional carbon emissions and evaluate emission reduction.