## 3 Experimental Result and Accuracy Evaluation
### Evaluation of Model Performance  
The accuracy of the CNN-STK model was first evaluated based on a completely random independent validation dataset consisting of 51,054 samples. The model exhibited satisfactory performance, achieving an \(R^{2}\) of 0.936, an \(RMSE\) of 1.300 ppm, and an \(MAE\) of 0.946 ppm, as illustrated in Figure 4a. Then, to validate the efficacy of this model, we compared it against a single CNN model, an RF model, and an RF-STK model, all trained and evaluated using the same data sets (Figure 4b-d). The accuracies of the comparative models are inferior to that of the CNN-STK model, suggesting that the CNN-STK model is better at accurately reconstructing full-coverage XCO2 data. Meanwhile, both the CNN-STK and RF-STK models demonstrated higher accuracies compared to their respective non-STK counterparts, indicating that the spatiotemporal Kriging method effectively optimizes the error distribution by considering the spatiotemporal autocorrelation of residuals, thereby improving prediction accuracy. In particular, the RF-STK model demonstrates a more pronounced improvement in accuracy through the spatiotemporal Kriging method. The CNN model can probably comprehensively learn the spatial relationships and features from sample points and surrounding auxiliary data, while, in contrast, the RF model faces challenges in capturing spatial relationships between data points. Consequently, the spatiotemporal Kriging method exhibits a notable application advantage in addressing the residuals of the RF model.  
Due to the latitudinal and seasonal variations in XCO2 data, we systematically categorized all data points based on latitudes and seasons, respectively. Then, the accuracy of each category was validated to assess the overall stability of the model. As illustrated in Table 2, the model exhibits higher accuracy in the low latitude region (I) compared to the mid-latitude regions (II-V), as evidenced by higher \(R^{2}\) and lower \(RMSE\) and \(MAE\). The accuracy in summer (VII) is slightly lower than that in other seasons, probably attributable to the hot and rainy climate during the summer in the study area, resulting in fewer valid data points acquired by satellites. Nevertheless, there is no strong fluctuation in the accuracies across different latitude zones and seasons, indicating the robustness of the CNN-STK model, with an average \(R^{2}\) of 0.942 across latitudes and 0.931 across seasons.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Latitude (\({}^{\circ}\)N)** & \(R^{2}\) & \(RMSE\) & \(MAE\) \\ \hline I & [Min \({}^{\star}\), 20] & 0.963 & 0.903 & 0.670 \\ II & [20, 30] & 0.944 & 1.133 & 0.822 \\ III & [30, 40] & 0.932 & 1.295 & 0.953 \\ IV & [40, 50] & 0.937 & 1.303 & 0.964 \\ V & [50, Max \({}^{\star}\)] & 0.934 & 1.487 & 1.101 \\ \hline  & **Season** & \(R^{2}\) & \(RMSE\) & \(MAE\) \\ \hline VI & Spring (Mar, Apr, May) & 0.939 & 1.129 & 0.824 \\ VII & Summer (Jun, Jul, Aug) & 0.920 & 1.540 & 1.169 \\ VIII & Autumn (Sep, Oct, Nov) & 0.932 & 1.235 & 0.911 \\ IX & Winter (Dec, Jan, Feb) & 0.933 & 1.170 & 0.845 \\ \hline \hline \end{tabular} \({}^{\star}\)Min\({}^{\star}\) and “Max” refer to the minimum and maximum latitude of China.  
\end{table}
Table 2: Accuracy validation results of CNN-STK model by latitude and by season.  
Figure 3: Spatiotemporal maps of monthly XCO2 over China at 0.25\({}^{\circ}\) grid-scale from 2015 to 2020. (Each period of XCO2 data from January 2015 to December 2020 is listed sequentially in order from left to right within each row and top to bottom across rows.).