Quantification of the distribution of the CO ₂ dry-air mixing ratio (XCO ₂ ) is crucial for understanding the carbon cycle. However, clouds and aerosols in the line of light create spectral interference with CO ₂ signals. This interference can result in a low yield of XCO ₂ retrievals, thus limiting the application of these valuable satellite data. In this study, we developed an innovative methodology to obtain XCO ₂ maps of high spatial and temporal resolution using satellite data. The method first interpolates the spatial properties using an empirical Bayesian kriging (EBK) algorithm. Then, the temporal properties are modulated based on a CO ₂ curve database that was constructed using temporal contours and transfer learning techniques. We applied this method to obtain spatiotemporal XCO ₂ maps over mainland China using the Orbiting Carbon Observatory 2 (OCO-2) data product OCO-2_L2_Lite_FP 9r for the period from January 1 to December 31, 2019. The correlation coefficient (

$R^{2}$

) was 0.8056, and the average absolute prediction error [root-mean-square error (RMSE)] was 0.9951. In the research area of mainland China, the vacancy validation strategy was adopted and yielded

$R^{2}$

and RMSE of 0.8230 and 0.9746, respectively. We used the 2018–2019 ground-based data from four Total Carbon Column Observing Network (TCCON) sites in Europe and 2016 Hefei sites in mainland China to evaluate the performance of this new mapping method, respectively. Also, we obtained

$R^{2}$

of 0.8690 and the RMSE of 0.9056 in Europe and

$R^{2}$

of 0.8473 and the RMSE of 0.7026 in mainland China, proving the robustness and high precision of our method. This mapping technique is capable of filling the spatiotemporal gaps of satellite measurements with the high accuracy and resolution needed for its scientific application; thus, it has the potential to augment the scientific returns of satellite missions (e.g., USA OCO-2 Japan GOSAT and Chinese TanSat).

Tongtong Sun

Publications