# XCO\({}_{2}\) Super-Resolution Reconstruction Based on Spatial Extreme Random Trees
## 4 Conclusions  
As CO\({}_{2}\) is a major greenhouse gas, excessive CO\({}_{2}\) emissions are a significant contributor to global warming. In this study, we introduced a novel spatial downscaling model named SExtraTrees, aimed at addressing the research gap in reconstructing high-resolution XCO\({}_{2}\) from the perspective of spatial heterogeneity. By considering nonlinear relationships and spatial characteristics, SExtraTrees further enhances the accuracy and robustness of high-resolution XCO\({}_{2}\) reconstruction, thereby improving the model's generalizability. Based on XCO\({}_{2}\) data with a spatial resolution of 10 km from 2016 to 2020, we successfully reconstructed XCO\({}_{2}\) at a spatial resolution of 1 km. To meet the need for carbon emission monitoring, the spatiotemporal characteristics of XCO\({}_{2}\) were analyzed within the region of China. Based on the results of this study, the following conclusions were drawn:  
1. The SExtraTrees model proposed in this study is an effective technique for predicting national long-term time series XCO\({}_{2}\) data. The SExtraTrees model outperforms Kriging interpolation and extreme random trees models in terms of model fitting and prediction while maintaining spatial consistency with the original XCO\({}_{2}\) data and ensuring high predictive accuracy. 2. The 1 km-resolution XCO\({}_{2}\) products reconstructed using the spatial extreme random trees model provide fundamental data and technical support for regional XCO\({}_{2}\) monitoring. These predicted XCO\({}_{2}\) datasets can also serve as a reference for calibrating other low-resolution XCO\({}_{2}\) datasets. The obtained results highlight the importance of combining geographic spatial correlations with machine learning methods to achieve high precision and robustness. The spatial extreme random trees model can also be applied to the resolution reconstruction of other environmental factors. Figure 7: Spatial distribution of XCO\({}_{2}\) trends from 2016 to 2020. 3. Based on the resolution reconstruction results of the spatial extreme random trees model, XCO\({}_{2}\) exhibits significant spatial and temporal heterogeneity. From 2016 to 2020, XCO\({}_{2}\) in China shows an increasing trend each year. Nationally, the spatial distribution of XCO\({}_{2}\) aligns with China's economic development and urbanization level, with higher XCO\({}_{2}\) concentrations in the eastern regions and lower concentrations in the western regions of China. The distribution of carbon concentrations varies among different regions, mainly due to differences in the geographical environment, economic development level, and industrial structure. Higher vegetation coverage can enhance the carbon sequestration capacity. Given the spatiotemporal changes and differences in XCO\({}_{2}\), this study provides scientific references for reducing carbon emissions, which is essential for crafting efficient policies aimed at reducing carbon emissions and addressing climate change. In summary, the spatial extreme random trees model enhances the fitting capability of XCO\({}_{2}\) with complex spatial features and exhibits good generalization performance. SExtra-Trees may also be applicable to other studies related to high-resolution reconstruction of air pollutants, serving as a methodological reference for reconstructing other factors at high resolutions. It is worth noting that in other relevant studies, when establishing models, it is essential to select influential factors that are strongly correlated with the dependent variable. Through the application of the spatial extreme random trees model, we have gained deeper insights into the spatiotemporal distribution characteristics of XCO\({}_{2}\) in China, providing robust data support for future carbon emission control and climate change research. In future studies, it will be necessary to further consider the unique temporal characteristics of XCO\({}_{2}\) data.