## 2 Methodology and Data
### High Precision and Wide Range XCH4 Fusion Dataset Construction
It is still difficult to more accurately attribute the areas of high methane concentration distribution identified in the 7 km resolution XCH\({}_{4}\) images. At 1 km resolution, however, spatial attribution of enhanced methane column concentrations becomes easier. For example, a distinct methane plume appears in the region shown in zoom in Figure 9c. According to Google Earth imagery, this coordinate corresponds to the main methane emission source in the oil field, with a methane column concentration enhancement of 295.26 ppb. In contrast, the corresponding 7 km grid in Figure 9a shows an enhancement of 164.33 ppb (the average concentration of all grids within the rectangle is considered as the background methane column concentration in this region). Our calculations indicate that 85% of the enhancement in this 7 km grid is due to the plume from this major emission source, which is also consistent with the accounting metrics provided by the oil companies. However, due to the confidentiality of the data, we are also unable to provide detailed metrics in this paper. However, even with the 1 km resolution imagery, it remains challenging to attribute minor XCH\({}_{4}\) enhancements caused by facility-level emissions. We advocate for the future development of XCH\({}_{4}\) datasets with even finer spatial resolution. ## 4 Discussion

### Strengths and Weaknesses of Each Model

In this study, we utilized three models to fuse GOSAT and TROPOMI data: the linear regression model, the random forest model, and the local random forest (LRF) model. The linear regression model is favored for its simplicity, ease of interpretation, and high computational efficiency. However, it is highly sensitive to outliers and lacks the capacity to effectively describe and predict complex data relationships. In contrast, the random forest model mitigates the impact of noise and outliers by averaging the results from multiple decision trees, thereby minimizing the influence of any single outlier on the overall prediction. This enhances the model's robustness and accuracy, enabling it to

Figure 9: High-resolution and high-precision XCH\({}_{4}\) dataset for oilfield regions. **(a)** shows the low-resolution data of an area different from Figure 8 after LRF model fusion, **(b)** shows the high-resolution data of the same area, **(c)** shows a zoomed-in display of a high-emission plant area in **(b)**, and **(d)** is a satellite map corresponding to the area displayed in **(c)**. capture nonlinear relationships within the data. Consequently, we decided to discard the linear regression model. Building upon the random forest model, we incorporated an advanced method from the literature: the local random forest (LRF) model. This model adapts to spatial variability, making it particularly well-suited for addressing the spatiotemporal heterogeneity in the global distribution of XCH\({}_{4}\) data. After cross-validating with TCCON sites, we calculated the coefficient of determination (R\({}^{2}\)) and the root mean square error (RMSE) between the fused products and TCCON site data for each model. As anticipated, the LRF model exhibited the highest R\({}^{2}\) and the lowest RMSE, consistent with our expectations. Therefore, we selected the fused products from the LRF model as the high-precision, wide-coverage XCH\({}_{4}\) dataset for further research. ### Challenge and Forward

The development of high-resolution XCH\({}_{4}\) datasets through multi-source satellite fusion presents significant opportunities for advancing methane monitoring and emission control in the oil and gas industry. However, this process is fraught with several challenges. The heterogeneity of data sources--stemming from variations in spatial resolution, temporal coverage, and spectral response among different satellites--introduces considerable complexity into the data fusion process. This complexity necessitates the development of sophisticated algorithms capable of integrating diverse datasets while maintaining high accuracy and consistency.