## 4 Discussion
### Advantages and Limitations  
In recent years, carbon observation satellite data has made significant contributions to the global-scale monitoring of atmospheric CO\({}_{2}\) concentrations, but its spatial and temporal discontinuities have become the main reason for restricting the wide application. In response to this situation, some scholars have explored different spatiotemporal reconstruction methods for XCO2 and obtained better accuracy results [20; 27; 42]. Compared with the machine learning models constructed by [PERSON] [24], [PERSON] [25], and [PERSON] [26], with \(R^{2}\) ranging from 0.61 to 0.91, the CNN model utilized in this study performs better. Deep learning models typically outperform machine learning models in tasks with large amounts of training samples and can automatically learn features without the need for feature engineering before modeling. This study has a considerable sample size and possesses the prerequisites for exploring the application of deep learning models. Moreover, we tried to integrate the CNN model with the spatiotemporal Kriging method, aiming to leverage the advantages of these two types of models, i.e., after getting the predictions from the CNN model, we further refined the model errors using the spatiotemporal Kriging method, which led to a more accurate interpolation result. To the best of our knowledge, this attempt to effectively integrate a deep learning model with a geostatistical method is new and has never been reported. Compared to single deep learning models constructed by [PERSON] [27] and [PERSON] [28] (accuracy validations based on TCCON are: \(R^{2}\) = 0.87; \(RMSE\) = 0.90 ppm and \(MAE\) = 0.74 ppm, respectively), this study also has higher accuracy, which not only indicates that the model constructed in this paper is reliable, but also demonstrates that the strategy of optimizing the residuals using spatiotemporal Kriging method is effective. It is worth mentioning that all of their experiments introduced existing XCO2 datasets from model simulations as a priori knowledge or constraints in modeling, which may cause unnecessary biases. In contrast, we only used XCO2 satellite data for modeling and interpolation and then validated it with model simulations, which effectively avoids this problem and ensures the model results are independent. In addition, although the experiment used the strategy of randomly selecting 20% of the sample points as an independent validation dataset to evaluate the model accuracy, we used different randomly selected training and validation datasets several times in constructing the CNN and spatiotemporal Kriging models, and all of them obtained stable accuracy results. However, the CNN-STK model proposed in this study also has some limitations. For instance, the density of valid data points significantly affects interpolation precision when using the spatiotemporal Kriging method. Future research aiming for finer-scale analysis should consider integrating more satellite data sources, such as GOSAT, OCO-3, TanSat, etc., to enhance data density and improve the reliability of geostatistical methods. Secondly, we directly used the cropping method for CNN model samples provided by the previous study, wherein the surrounding imagery of each satellite observation point was cropped into an independent sample. However, the appropriate sample size should be determined through corresponding comparative experiments. To be specific, larger samples may be  
Figure 11: Feature importance ranking for XCO2. able to provide richer features and thus improve the model accuracy, but it is also possible to affect the model accuracy because of the spatiotemporal heterogeneity. Furthermore, current studies often use XCO2 as auxiliary data for modeling anthropogenic carbon emissions of interest or computing enhancement and anomaly indicators to characterize the spatial distribution of anthropogenic carbon emissions. Exploration of its potential practical significance remains insufficient.