Generating daily high-resolution and full-coverage XCO across China from 2015 to 2020 based on OCO-2 and CAMS data.

China has set a goal to achieve carbon neutrality by 2060, and satellite remote sensing allows for acquiring large-range and high-resolution carbon dioxide (CO) data, which can aid in achieving this goal. However, satellite-derived column-averaged dry-air mole fraction of CO (XCO) products often suffer from substantial spatial gaps due to the impacts of narrow swath and clouds. Here, this paper generates daily full-coverage XCO data at a high spatial resolution of 0.1° over China during 2015-2020, by fusing satellite observations and reanalysis data in a deep neural network (DNN) framework. Specifically, DNN constructs the relationships between Orbiting Carbon Observatory-2 satellite XCO retrievals, Copernicus Atmosphere Monitoring Service (CAMS) XCO reanalysis data, and environmental factors. Then, daily full-coverage XCO data can be generated based on CAMS XCO and environmental factors. Results show that a satisfactory performance is reported in multiform validations, with RMSE and R of 0.99 ppm and 0.963 in terms of the sample-based cross-validation, respectively. The independent in-situ validation also indicates high consistency (R = 0.866 and RMSE = 1.71 ppm) between XCO estimates and ground measurements. Based on the generated dataset, spatial and seasonal distributions of XCO across China are investigated, and a growth rate of 2.71 ppm/yr is found from 2015 to 2020. This paper generates long time series of full-coverage XCO data, which helps promote our understanding of carbon cycling. The dataset is available from https://doi.org/10.5281/zenodo.7793917.