###### Abstract  
The (CH4) is the second-largest greenhouse gas contributing to global climate warming. As of 2022, methane emissions from the oil and gas industry amounted to 3.586 million tons, representing 13.24% of total methane emissions and ranking second among all methane emission sources. To effectively control methane emissions in oilfield regions, this study proposes a multi-source remote sensing data fusion method based on the concept of data fusion, targeting high-emission areas such as oil and gas fields. The aim is to construct an XCH4 remote sensing dataset that meets the requirements for high resolution, wide coverage, and high accuracy. Initially, XCH4 data products from the GOSAT satellite and the TROPOMI sensor are matched both spatially and temporally. Subsequently, variables such as longitude, latitude, aerosol optical depth, surface albedo, digital elevation model (DEM), and month are incorporated. Using a local random forest (LRF) model for fusion, the resulting product combines the high accuracy of GOSAT data with the wide coverage of TROPOMI data. On this basis, \(\Delta\)XCH4 is derived using GF-5. Combined with the GFEI prior emission inventory, the high-precision fusion dataset output by the LRF model is redistributed grid by grid in oilfield areas, producing a 1 km resolution XCH4 grid product, thereby constructing a high-precision, high-resolution dataset for oilfield regions. Finally, the challenges that emerged from the study were discussed and summarized, and it was envisioned that, in the future, with the advancement of satellite technology and algorithms, it would be possible to obtain more accurate and high-resolution datasets of methane concentration and apply such datasets to a wide range of fields, with the expectation that significant contributions could be made to reducing methane emissions and combating climate change. methane monitoring; data fusion; matched filter; local random forest model +
Footnote †: journal: applied sciences  
in this sector [14; 15]. However, recent studies indicate that facilities with lower but more persistent emission rates may actually be the most significant sources of methane emissions in the oil and gas industry [16; 17]. Therefore, obtaining high-resolution, wide-coverage XCH\({}_{4}\) products in emission regions to support more detailed and accurate top-down methane flux inversions is a task of significant scientific importance [18; 19; 20]. This approach enables a more precise identification and quantification of methane emissions, which is crucial for developing effective mitigation strategies and understanding the overall impact of methane on climate change [21; 22]. Currently, there are many methane monitoring satellites globally, among which GOSAT and TROPOMI are among the most widely used sensors and satellites [23]. The Greenhouse Gases Observing Satellite (GOSAT) launched by Japan has been providing accurate data since 2009. [PERSON] and colleagues used the TM5-4 DVAR inverse modeling framework to explore the use of GOSAT retrievals in inverse modeling and compared these retrieval results with those from SCIAMACHY and the NOAA [24]. [PERSON] and others estimated global surface CH\({}_{4}\) flux using GOSAT CH\({}_{4}\) data and simulated the three-dimensional distribution of global atmospheric CH\({}_{4}\) concentration based on these surface fluxes [25]. [PERSON] used a series of available GOSAT data to detect emissions from single point sources, aiming to identify methane emissions from specific locations [26]. [PERSON], [PERSON], and others reported regional monthly CH\({}_{4}\) and CO\({}_{2}\) fluxes from GOSAT column data using the ensemble Kalman filter (EnKF) and the GEOS-Chem chemical transport model and compared these posterior values with those inferred from surface mole fraction data to observe surface carbon fluxes of CO\({}_{2}\) and CH\({}_{4}\)[27].