A Machine Learning Approach for XCH4 Retrieval over China Based on TROPOMI Spectral Features

Methane is a major greenhouse gas that plays a critical role in climate change. Accurate monitoring of methane at the regional scale is essential for understanding its spatiotemporal dynamics and underlying processes. Here, a machine learning-based framework is developed to retrieve column-averaged methane (XCH4) by integrating shortwave infrared spectral information from TROPOMI. Spectral channels are first analyzed through machine learning-based feature importance analysis, and normalized spectral features are subsequently constructed. These features are then combined with auxiliary variables, including meteorological conditions, observation geometry, and surface environmental factors, to develop an XCH4 retrieval model. The GOSAT L2 XCH4 product is introduced as a high-accuracy reference observation for correcting the retrieved XCH4 results, and independent validation is conducted using observations from the TCCON site. Among the evaluated models, XGBoost demonstrates the best performance, achieving an R2 of 0.91 and an RMSE of 9.28 ppb under five-fold cross-validation. The model also maintains relatively better predictive performance compared with other models under both temporal and spatial extrapolation scenarios. Compared with the TROPOMI L2 product, the retrieved XCH4 shows improved agreement with TCCON observations, with a higher correlation (R = 0.84) and reduced errors. Based on this approach, a daily, spatially continuous XCH4 dataset over China for 2021 is generated. Analysis of the retrieved dataset reveals pronounced spatiotemporal patterns, characterized by higher XCH4 concentrations in eastern China and lower values in the western regions, as well as a distinct seasonal cycle.