XCH4 inversion

Amid global climate change, rising atmospheric methane (CH4) concentrations significantly influence the climate system, contributing to temperature increases and atmospheric chemistry changes. Accurate monitoring of these concentrations is essential to support global methane emission reduction goals, such as those outlined in the Global Methane Pledge targeting a 30% reduction by 2030. Satellite remote sensing, offering high precision and extensive spatial coverage, has become a critical tool for measuring large-scale atmospheric methane concentrations. However, traditional physical inversion models face challenges, including high computational complexity, low processing efficiency, and inadequate incorporation of spatial distribution information, limiting their effectiveness. To address these shortcomings, this study proposes a high-precision XCH4 inversion method that integrates the Convolutional Block Attention Module (CBAM) with the ResNet18 neural network (CBAM-ResNet18). By leveraging shortwave infrared spectral data from the Sentinel-5P satellite and the CAMS reanalysis dataset, this approach achieves rapid and accurate XCH4 inversion. Experimental results demonstrate that the method outperforms both conventional physical models and existing mainstream techniques in terms of inversion accuracy and computational efficiency. It achieves an error of less than 2%, meeting the stringent precision requirements for XCH4 in atmospheric remote sensing and providing a robust tool for methane monitoring.