Global daily seamless XCO2 Mapping (2016–2020): Spatio-temporal trends and variations during wildfire events

Carbon dioxide (CO2) is a dominant greenhouse gas and has a considerable effect on climate change. Satellite remote sensing is commonly used to acquire atmospheric CO2 concentrations. However, the limited spatial coverage of a single satellite makes the obtainment of full-coverage CO2 data difficult. In this study, a daily dataset of global seamless column-averaged dry-air mole fractions of CO2 (XCO2) was generated with a high spatial resolution of 0.1° from 2016 to 2020, by using a stacking machine learning method. The proposed XCO2 dataset shows a satisfactory performance, with a root mean square error (RMSE) of 0.9697 ppm and correlation coefficient (R) of 0.9868 in the 10-fold cross validation. The spatial validation reveals good generalization ability, with continent-by-continent validation results showing an R greater than 0.93. The proposed dataset reports high consistency and accuracy in the ground-based validation, with an RMSE of 1.0855 ppm. Out of 24 stations, 22 demonstrate a precision of R greater than 0.95. In comparison with two XCO2 model simulations, our reconstructions show a better consistency with ground observations. Spatial analyses at continent, national, and Chinese provincial levels, and temporal trends at daily, monthly, seasonal, and annual scales, are provided. Furthermore, benefitting from the daily temporal resolution, two typical examples of wildfire events, namely the Fort McMurray wildfire and the Blue Cut Fire, are evaluated. Our dataset can effectively capture fine-scale XCO2 variations and has the potential to characterize carbon sources and sinks. The dataset can be obtained freely at https://zenodo.org/records/15191247.