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

Due to the coarse spatial resolution, the column-averaged dry-air mole fraction of CO2 (XCO2) data from the CarbonTracker may be inadequate to reflect the spatial heterogeneity of XCO2. 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 XCO2 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 XCO2 varied from 402.54 +/- 3.95 ppm in summer to 406.28 +/- 3.19 ppm in spring. While the XCO2 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 XCO2 data. The full-coverage and fine-scale XCO2 data set is expected to advance our understanding of the carbon cycles.

Automated summary

Due to coarse spatial resolution, the CarbonTracker's XCO2 data fails to capture the spatial heterogeneity of XCO2. In this study, a machine learning model was developed to fill the data gaps in the Orbiting Carbon Observatory 2 satellite retrievals in China, yielding high cross-validation results. The filled data set revealed regional variations in XCO2, with the highest average in East China and the lowest in Northwest China.