Synergy of Multiple-Satellite Measurements to Fill the Gap of Global XCO2

Accurate monitoring of atmospheric carbon dioxide (CO₂) concentrations is essential for understanding the global carbon cycle. Satellite remote sensing enables global-scale CO₂ monitoring, providing column-averaged dry air mole fractions of CO₂ (XCO₂). Current missions such as the Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse gases Observing SATellite (GOSAT) series provide high-quality XCO₂ but have many observation gaps because of narrow swath widths or cloud interference. To address this, we first propose a machine learning (ML) approach to estimate daily XCO₂ (ML XCO₂) at a 0.25$°$ resolution using OCO-2, Sentinel-5 Precursor TROPOspheric Monitoring Instrument, and ERA5 reanalysis data from May 2018 to December 2023. Validation against the Total Carbon Column Observing Network measurements shows high agreement, with an R² of 0.95, a root mean square error of 1.05 ppm, and a mean absolute error of 0.78 ppm. The spatiotemporal variations in ML XCO₂ are generally consistent with OCO-2, GOSAT, and the Copernicus Atmospheric Monitoring Service (CAMS) XCO₂. Notably, during the Coronavirus disease 2019 (COVID-19) pandemic in 2020, ML XCO₂ estimates were more consistent with OCO-2 and GOSAT XCO₂ than CAMS XCO₂, which was overestimated owing to unadjusted COVID-19-related CO₂ emission reductions. The annual mean CO₂ growth rates from ML XCO₂ (2.065–2.735 ppm/year, 2019–2023) also agree with estimates from OCO-2 and the National Oceanic and Atmospheric Administration surface measurements, indicating the robustness of our approach. Our study demonstrates that the synergy between multiple-satellite measurements enhances the spatial coverage of XCO₂ and improves our understanding of the global carbon cycle.