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.
Keywords: CO2; mapping; random forest; remote sensing; satellite.