## 1 Introduction  
Methane (CH\({}_{4}\)) is a significant greenhouse gas, second only to carbon dioxide (CO\({}_{2}\)), accounting for over 20% of the total global greenhouse gas emissions [1, 2]. The Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) indicates that methane's contribution to global warming has reached 25% [3], with a global warming potential over 20 and 100 years that is 84 and 28 times that of CO\({}_{2}\), respectively. Moreover, methane has a relatively short residence time in the atmosphere (9 \(\pm\) 1 years). Thus, reducing methane emissions is considered one of the effective ways to combat global climate change on a decadal scale. Over the past decade, atmospheric methane concentrations have increased rapidly, nearing three times pre-industrial levels [4]. This surge is primarily due to human activities, such as the extraction of fossil fuels like coal, oil, and natural gas [5; 6]. Studies on methane emissions from facilities have shown that a small number of extremely strong point sources account for a significant portion of total emissions due to equipment malfunctions or abnormal operating conditions [7; 8]. Rapidly identifying, repairing, and regularly monitoring these top emitters presents an opportunity to mitigate climate change effectively. Satellite remote sensing provides an important technical means for systematically monitoring methane point source emissions in the global oil and gas production sector. The shortwave infrared range (1600-2500 nm) detected by hyperspectral imaging spectrometers under clear-sky conditions can be used for the inversion of the atmospheric methane column concentration. For example, instruments such as the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) [9] and the Greenhouse Gases Observing Satellite (GOSAT) [10] have already achieved the inversion of the methane column concentration at global and regional scales. However, these satellite sensors have a relatively coarse spatial resolution, which leads to a lower estimation accuracy in monitoring methane point source emissions [11]. Methane point source emission monitoring requires high spatial resolution hyperspectral imaging data. Relevant application cases have demonstrated the potential of such data in methane plume remote sensing inversion. For example, [PERSON] et al. [12] used Hyperion hyperspectral data to monitor methane leakage emissions from the Aliso Canyon in California, USA. The Visible Shortwave Infrared Hyperspectral Imager (AHSI) onboard China's Gaofen-5 (GF5) and Ziyuan-1 02D (ZY1-02D) satellites has also been successfully applied to methane point source emission monitoring in the Permian Basin region of the USA [13; 14]. The GHGSat satellite from Canada [15] is specifically designed to monitor methane point sources with a spatial resolution of 25 m over a 12 \(\times\) 12 km\({}^{2}\) area (detection accuracy of 1-15%). In addition, multispectral satellites such as Landsat-8 [16], Sentinel-2A/2B [17], and WorldView-3 [18] have recently been proven to be capable of mapping super methane emission sources occurring on bright and homogeneous surfaces. However, due to their relatively coarse spectral resolution, multispectral imagers still have a much higher detection limit for methane point sources compared to hyperspectral imagers. Satellite observations of methane in the atmosphere using shortwave infrared (SWIR) solar backscattering hold unique potential for both global and point source monitoring, provided that they are combined with a fine spatial resolution and frequent revisit rates. The Sentinel-2 twin satellites are equipped with multi-band instruments capable of observing in the methane-sensitive SWIR bands, and their 20 m resolution methane data can pinpoint the locations of significant leaks (>1 t/h) [17]. However, the relatively coarse spatial resolution of these satellite sensors results in a lower accuracy for monitoring methane point sources [19; 20; 21].