Accurate retrieval of column-averaged dry-air mole fraction of methane (XCH4) in the atmosphere is important for greenhouse gas tonic shower cap emission management.Traditional XCH4 retrieval methods are complex, while machine learning can be used to model nonlinear relationships by analyzing large datasets, providing an efficient alternative.This study proposes an XGBoost algorithm-based retrieval method to improve the efficiency of atmospheric XCH4 retrieval.First, the key wavelengths affecting XCH4 retrieval were determined using a radiative transfer model.The TROPOspheric Monitoring Instrument (TROPOMI) L1B satellite data, L2 XCH4 products, and auxiliary data were matched to construct the dataset.
The dataset constructed was used to train the XGBoost model and obtain the TRO_XGB_XCH4 model.Finally, the accuracy of the baseball scoreboards for sale proposed model was evaluated using various parameter values and validated against XCH4 products and Total Carbon Column Observing Network (TCCON) ground-based observations.The results showed that the proposed TRO_XGB_XCH4 model had a tenfold cross-validation accuracy R of 0.978, a ground-based validation R of 0.749, and a temporal extension accuracy R of 0.
863.Therefore, the accuracy of the TRO_XGB_XCH4 retrieval model is comparable to that of the official TROPOMI L2 product.