## 2 Methodology and Data
### High Precision and Wide Range XCH4 Fusion Dataset Construction
We then performed a correlation fitting between the fused dataset and the pre-fusion TROPOMI product, as shown in Figure 5.

Figure 5 shows the scatter density plot for all data from 2019 to 2023 before and after fusion, processed with a 2\({}^{\circ}\) grid and a five-day spatial average. The overall fused values are slightly higher than the original values. The forced-through-origin fitting equation is y = 1.004x, with R\({}^{2}\) = 0.84 and RMSE = 37.116 ppb. The image and data both indicate a strong correlation between the data before and after fusion. Furthermore, we conducted a global cross-validation of our fused products against TCCON sites. However, due to the limited number of monitoring stations in China--specifically, only the 'Hefei' and 'Xianghe' sites--we are constrained by the current density of TCCON sites and are unable to directly validate the accuracy of our fused products in regions with high oil and gas emissions. As a compromise, we utilized XCH\({}_{4}\) observational data from these

Figure 3: Location of the study area (Dongying, Shandong Province, China). two Chinese sites for the year 2023. We established a 20 km buffer zone around each site and conducted cross-validation by comparing the average column concentration of the LRF-fused products within the buffer zones with the observed concentrations at the sites. Simultaneously, we employed two additional data fusion models, namely the linear regression model and the random forest model, each producing a set of fused XCH4 products, which were also cross-validated against the TCCON sites. In addition to visual comparisons, we utilized two quantitative metrics, the coefficient of determination (R2) and root mean square error (RMSE), to evaluate the effectiveness of the models. The cross-validation results are presented in Figure 6 and Table 2. Figure 6 compares the original TROPOMI XCH4 data and the XCH4 products derived from the three different fusion models against the TCCON site data. Specifically, Figure 6a-d show the cross-validation images for the original data, the linear regression model, the local random forest model, and the random forest model, respectively, along with the corresponding R2 values. Figure 4: Presentation of XCH4 dataset fused by LRF model in Shandong Province. Figure 5: Scatter Density Plot of Fused Data vs. Original TROPOMI Data, with the Black Dashed Line Representing the 1:1 Line and the Blue Dashed Line Representing the Fitted Line. Table 2 presents the RMSE values for the pre-fusion data and the fused products compared to the TCCON site data. As shown in Figure 6 and Table 2, upon calculation, our LRF model achieved an R\({}^{2}\) of 0.68, higher than the standard random forest model (0.64) and the linear regression model (0.51). The RMSE between the fused product and TCCON ground station data is 23.201 ppb, lower than the standard random forest model (29.118 ppb) and linear regression model (35.024 ppb), indicating that the LRF model's fusion is superior. Additionally, the RMSE of the product obtained by the LRF model is significantly lower than before fusion (43.409 ppb), demonstrating that data fusion effectively improves product accuracy and reduces error. However, while the fused XCH\({}_{4}\) dataset inherits both the wide coverage of TROPOMI and the high accuracy of GOSAT, its resolution still does not meet the requirements for oil field monitoring. Therefore, we further enhanced the resolution in the oil field regions based on the high-precision, wide-coverage XCH\({}_{4}\) dataset obtained. ### High-Resolution \(\Delta\)XCH\({}_{4}\) Retrieval from GF-5 in the Oil Field Area

Within the study area, we first filtered and preprocessed the GF-5 satellite images. Initially, we applied cloud masking, shadow masking, and water masking to remove