Deriving Full‐Coverage and Fine‐Scale XCO₂ Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker Output

Abstract

Abstract Due to the coarse spatial resolution, the column‐averaged dry‐air mole fraction of CO 2 (XCO 2 ) data from the CarbonTracker may be inadequate to reflect the spatial heterogeneity of XCO 2 . We developed a machine learning model to fill the data gaps in the Orbiting Carbon Observatory 2 satellite retrievals across China during 2015–2018, with cross‐validation R 2 = 0.95 and RMSE = 0.91 ppm. Based on the gap‐filled data set, the multiyear average XCO 2 was the highest in East China (405.71 ± 3.72 ppm) and the lowest in Northwest China (403.99 ± 3.47 ppm). At the national level, the multiyear seasonal XCO 2 varied from 402.54 ± 3.95 ppm in summer to 406.28 ± 3.19 ppm in spring. While the XCO 2 kept increasing, the rate of increase declined from 3.23 to 2.10 ppm/year. The machine learning approach is feasible for downscaling and calibrating the CarbonTracker XCO 2 data. The full‐coverage and fine‐scale XCO 2 data set is expected to advance our understanding of the carbon cycles.