Global estimates of gap-free and fine-scale CO concentrations during 2014-2020 from satellite and reanalysis data.

Carbon dioxide (CO) is a crucial greenhouse gas with substantial effects on climate change. Satellite-based remote sensing is a commonly used approach to detect CO with high precision but often suffers from extensive spatial gaps. Thus, the limited availability of data makes global carbon stocktaking challenging. In this paper, a global gap-free column-averaged dry-air mole fraction of CO (XCO) dataset with a high spatial resolution of 0.1° from 2014 to 2020 is generated by the deep learning-based multisource data fusion, including satellite and reanalyzed XCO products, satellite vegetation index data, and meteorological data. Results indicate a high accuracy for 10-fold cross-validation (R = 0.959 and RMSE = 1.068 ppm) and ground-based validation (R = 0.964 and RMSE = 1.010 ppm). Our dataset has the advantages of high accuracy and fine spatial resolution compared with the XCO reanalysis data as well as that generated from other studies. Based on the dataset, our analysis reveals interesting findings regarding the spatiotemporal pattern of CO over the globe and the national-level growth rates of CO. This gap-free and fine-scale dataset has the potential to provide support for understanding the global carbon cycle and making carbon reduction policy, and it can be freely accessed at https://doi.org/10.5281/zenodo.7721945.