## 2 Methodology and Data
### High Precision and Wide Range XCH4 Fusion Dataset Construction
## 5 Conclusions

### Data Sources and Data Processing

In this study, XCH\({}_{4}\) data acquired from TROPOMI and GOSAT satellites between 2019 and 2023 were systematically analyzed. GOSAT data are known for their high accuracy, while TROPOMI data are known for their wide coverage. In order to combine the advantages of both, we constructed a global XCH\({}_{4}\) dataset with both high accuracy and wide coverage. Before constructing the model, we performed spatiotemporal matching of the two datasets to ensure high spatiotemporal correlation of the training data. ### Model Development and Performance Evaluation

We used a linear regression model, a random forest model, and a localized random forest model designed to effectively integrate data from GOSAT and TROPOMI. We quantitatively assessed the integration effectiveness of each model through the coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE). The results showed that the localized random forest model performed well in terms of R\({}^{2}\) value (0.68) and RMSE value (23.201 ppb) and thus was selected as the best fusion model to achieve the construction of a high-precision and wide-coverage XCH\({}_{4}\) dataset. ### High-Resolution Data Processing

For high-emission background areas (e.g., oil field areas), in order to achieve more detailed and precise satellite remote sensing purposes, proxy data from GF-5 satellites were utilized, and \(\Delta\)XCH\({}_{4}\) data were acquired by matched filtering technique, which successfully improved the resolution to 1 km. Subsequently, the lasso regression method was used to integrate the \(\Delta\)XCH\({}_{4}\) data with 1-km resolution into the fusion model of a 7-km grid, and a high-resolution and high-precision XCH\({}_{4}\) grid dataset of the oilfield area was generated. ### Significance of the Research Results

The oilfield XCH\({}_{4}\) dataset developed in this research not only achieves a high level of accuracy and resolution but also provides strong support for optimizing methane monitoring in the oilfield region and controlling domestic carbon emissions. The research results of this dataset are of great practical value in advancing China's goal of carbon peaking and carbon neutrality and have a very positive contribution to environmental protection and climate change response. Conceptualization, L.F.; Methodology, L.F.; Resources, Y.W. ; Writing--original draft, L.F.; Writing--review & editing, Y.W. ; Visualization, L.F.; Project administration, Y.D. ; Funding acquisition, Y.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are available on request from the corresponding author. * _[PERSON] et al. (2023)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Spatial and Temporal Variations of Atmospheric CH\({}_{4}\) in Monsoon Asia Detected by Satellite Observations of GOSAT and TROPOMI. _Remote Sens._**2023**, _15_, 3389. [CrossRef]
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