Global 20-year XCO₂ mapping through synergy of multi-satellite observations

Global-scale, long-term, high-consistency, and high-coverage carbon dioxide (CO₂) products are crucial for understanding the dynamics of CO₂ worldwide, which are often generated by integrating multi-source satellite and reanalysis data. However, existing research generally faces several challenges, including inconsistencies among different satellites, limited accuracy of multi-source data fusion modeling, and difficulties in extrapolating beyond the modeling period due to the interannual growth trend of CO₂. To fill this gap, our study proposes a novel approach for reconstructing global column-averaged dry-air mole fraction of CO₂ (XCO₂) products of long time series (2003-2022) and high accuracy, with the combination of local Least Absolute Shrinkage and Selection Operator (LASSO) regression and de-trending methods. The proposed method corrects the differences between multi-source satellites to enhance the consistency of reconstruction results. Furthermore, it accounts for spatio-temporal heterogeneity and enhances extrapolation. Two long-term XCO₂ products are available: multi-satellite XCO₂ (MS-XCO₂, 2003-2022), which integrates data from five satellites but still exhibits spatial gaps, and MS-CAMS-XCO₂ (2003-2020), which fuses CAMS data and MS-XCO₂ to provide spatially continuous coverage. Validation results against ground stations show high accuracy for both MS-XCO₂ and MS-CAMS-XCO₂, with R² values of 0.985 and 0.989, and RMSE values of 1.08 ppm and 0.997 ppm, respectively. The reconstructed datasets reveal that the growth rate of global XCO₂ is 2.260 ppm/year, with higher CO₂ levels in the mid-latitude northern hemisphere and lower CO₂ levels in the southern hemisphere. Further analysis of the correlation between XCO₂ anomalies and net ecosystem exchange (NEE) indicates a significant negative correlation when the ecosystem is a carbon source. However, the correlation varies when the ecosystem is a carbon sink, with a significant negative correlation observed when vegetation is in good condition. This study generates two datasets of global long-term high-precision XCO₂, providing valuable data for understanding the global carbon cycle.