###### Abstract
The Sentinel-5P satellite, launched by the European Space Agency in 2017 and equipped with the tropospheric monitoring instrument (TROPOMI), provides daily global methane column concentrations for each atmospheric layer. [PERSON] and colleagues used data provided by TROPOMI to derive the latest global methane concentration trends [28]. [PERSON] and others processed and simply classified CH\({}_{4}\) data provided by the high-resolution TROPOMI instrument to detect anomalous emissions from various sources [29]. However, the spatial resolution of these spaceborne sensors is relatively coarse, limiting their application in monitoring methane emission point sources [30]. In recent years, some research teams have made significant contributions to developing XCH\({}_{4}\) products with large coverage and high-resolution characteristics, effectively integrating the features of different sensors. [PERSON] et al. developed a retrieval algorithm based on machine learning models to replace the fully physical methods, using the XCH\({}_{4}\) product from the GOSAT satellite as a benchmark [31]. This led to the development of a TROPOMI-XCH\({}_{4}\) product that possesses the accuracy characteristics of GOSAT-XCH\({}_{4}\)[32]. [PERSON] et al. integrated data from GOSAT, OCO-2/3, and reanalysis products to generate comprehensive coverage products for XCO\({}_{2}\) and XCH\({}_{4}\) products [33]. The core foundation of the spatiotemporal interpolation method is the first law of geography [34], which states that geographically proximate variables are more likely to exhibit higher correlations [35; 36; 37]. However, the distribution of methane emissions in the energy sector does not adhere to the first law of geography, as its distribution patterns are primarily driven by human activities [38; 39]. This results in biases in the fused XCH\({}_{4}\) product when revealing the distribution of methane emissions. Additionally, the differences in detection methods of various sensors may also pose challenges to product-level fusion [40; 41]. Therefore, it is imperative to establish a high-resolution, wide-range, and high-precision XCH\({}_{4}\) remote sensing dataset for the oil and gas industry [42]. Based on the concept of data fusion, this work proposes a method for high-emission areas such as oil and gas regions that integrates multi-source remote sensing data (including GOSAT, TROPOMI, and GF-5), aiming to construct an XCH\({}_{4}\) remote sensing dataset that simultaneously meets the requirements of high resolution, wide coverage, and high accuracy. Compared with previous studies, this study has achieved great improvement in two aspects: On the one hand, for the spatial and temporal heterogeneity of the global methane concentration distribution, the random forest model, which is often used in previous studies, is difficult to adapt to the spatial and temporal heterogeneity of the global methane concentration distribution. temporal heterogeneity of the global distribution of methane and affects the accuracy of the products, whereas in this study, the LRF model, which varies with time and spatial location, is used so that the fused high-precision and large-range XCH4 products can better describe the spatial and temporal heterogeneity of methane concentration. On the other hand, to address the resolution problem, this study further introduces GF-5 satellite images and adopts the matched filtering method to greatly improve the resolution of the fused XCH4 products and constructs a high-precision and high-resolution XCH4 dataset in oil and gas areas, which can effectively improve the current situation of methane concentration detection in oil and gas fields. The structure of this paper is organized as follows: Section 2 introduces the data and methods used in this study; Section 3 analyzes the experimental results; Section 4 discusses the experimental results; finally, Section 5 provides a summary of the entire work.