## 5 Conclusions  
This study introduces a novel approach to transforming discrete satellite observations into continuous spatiotemporal datasets. Specifically, we developed a CNN-STK model to leverage the strengths of deep learning and geostatistical method, reconstructing a monthly spatiotemporal XCO2 dataset of China at 0.25\({}^{\circ}\) grid-scale from 2015 to 2020 based on single-satellite retrievals. It provides a new workflow to obtain comprehensive, objective, and reliable regional estimates of atmospheric CO\({}_{2}\) concentrations, which is particularly beneficial for countries and regions lacking effective terrestrial CO\({}_{2}\) observations. We conducted multiple experiments to assess the CNN-STK XCO2 dataset. Firstly, we evaluated the CNN-STK model using an independent validation dataset and comparative experiments, demonstrating its high precision with an \(R^{2}\) of 0.936, an \(RMSE\) of 1.3 ppm, and an \(MAE\) of 0.946 ppm. Besides, the integrated approach proved effective. Secondly, we categorized interpolation results by latitude and season, revealing minimal fluctuations in precision, indicating its robustness. Finally, we compared the CNN-STK XCO2 dataset with model-simulated CAMS XCO2 dataset and TCCON ground-based observations. The experimental results show coherence between CNN-STK XCO2 and CAMS XCO2 trends, as well as the validation results against TCCON XCO2, demonstrate an \(R^{2}\) of 0.954, \(RMSE\) of 0.898 ppm, and \(MAE\) of 0.741 ppm, underscoring the reliability of CNN-STK dataset. We further extensively discussed the spatial distribution of the accuracy of the CNN-STK XCO2 dataset across different seasons and hot-spot/cold-spot regions, along with its uncertainties and influencing factors. The error distributions in spring, autumn, and winter exhibit similarities. The northern region of China has relatively more XCO2 observations with uniform distribution, resulting in generally higher accuracy compared to the southern region. High errors and uncertainties are concentrated in the Qinghai-Tibet Plateau, Sichuan Basin, and the northeastern region. Summer is the period of high uncertainty for the CNN-STK XCO2 dataset. Additionally, the accuracies of cold-spot regions are lower than that of hot-spot areas and regions with no significant clustering characteristics, indicating potential large biogenic disturbances in areas where XCO2 low values cluster, such as forest carbon sink absorption. Furthermore, we assessed that nighttime light data exerted the greatest influence on CNN-STK XCO2, followed by CH\({}_{4}\) column-mean molar fraction, total column ozone, total column water, and surface solar radiation. Figure 12: Spatial distributions of CNN-STK XCO2, ODIAC CO\({}_{2}\), and EDGAR CO\({}_{2}\) in 2015. **Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/rs16132433/s1](https://www.mdpi.com/article/10.3390/rs16132433/s1)]([https://www.mdpi.com/article/10.3390/rs16132433/s1](https://www.mdpi.com/article/10.3390/rs16132433/s1)), Figure S1. The XCO2 data acquired by OCO-2 at daily, weekly, monthly, and yearly time scales; Figure S2. Histogram of residual frequency distribution of CNN model; Figure S3. The spatial empirical variogram function and temporal empirical variogram function of the residuals (red points), and their corresponding fitted variogram function models (blue lines); Figure S4. The empirical spatiotemporal variogram functions for residuals and its fitted variogram function model; Figure S5. Locations of Heefi site and Xianghe site (a). Circular geographical regions centered on each site, with diameters of 1\({}^{\circ}\), 3\({}^{\circ}\), and 5\({}^{\circ}\). The land cover data was obtained from GlobeLand30 dataset ([[http://globallcover.com/](http://globallcover.com/)]([http://globallcover.com/](http://globallcover.com/)) (accessed on 9 November 2023)), and the blank area in the diagrams is the oceanic area (b); Figure S6.