Retrieval of Atmospheric XCH<sub>4</sub> via XGBoost Method Based on TROPOMI Satellite Data
Accurate retrieval of column-averaged dry-air mole fraction of methane (XCH<sub>4</sub>) in the atmosphere is important for greenhouse gas emission management. Traditional XCH<sub>4</sub> retrieval methods are complex, while machine learning can be used to model nonlinear rel...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/2073-4433/16/3/279 |
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| author | Wenhao Zhang Yao Li Bo Li Tong Li Zhengyong Wang Xiufeng Yang Yongtao Jin Lili Zhang |
| author_facet | Wenhao Zhang Yao Li Bo Li Tong Li Zhengyong Wang Xiufeng Yang Yongtao Jin Lili Zhang |
| author_sort | Wenhao Zhang |
| collection | DOAJ |
| description | Accurate retrieval of column-averaged dry-air mole fraction of methane (XCH<sub>4</sub>) in the atmosphere is important for greenhouse gas emission management. Traditional XCH<sub>4</sub> 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 XCH<sub>4</sub> retrieval. First, the key wavelengths affecting XCH<sub>4</sub> retrieval were determined using a radiative transfer model. The TROPOspheric Monitoring Instrument (TROPOMI) L1B satellite data, L2 XCH<sub>4</sub> 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_XCH<sub>4</sub> model. Finally, the accuracy of the proposed model was evaluated using various parameter values and validated against XCH<sub>4</sub> products and Total Carbon Column Observing Network (TCCON) ground-based observations. The results showed that the proposed TRO_XGB_XCH<sub>4</sub> 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_XCH<sub>4</sub> retrieval model is comparable to that of the official TROPOMI L2 product. |
| format | Article |
| id | doaj-art-0156c0b247eb4bf8b1b81a4a2de37398 |
| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj-art-0156c0b247eb4bf8b1b81a4a2de373982025-08-20T02:11:21ZengMDPI AGAtmosphere2073-44332025-02-0116327910.3390/atmos16030279Retrieval of Atmospheric XCH<sub>4</sub> via XGBoost Method Based on TROPOMI Satellite DataWenhao Zhang0Yao Li1Bo Li2Tong Li3Zhengyong Wang4Xiufeng Yang5Yongtao Jin6Lili Zhang7School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, ChinaSchool of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, ChinaSchool of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, ChinaSchool of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, ChinaSchool of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, ChinaSchool of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, ChinaSchool of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAccurate retrieval of column-averaged dry-air mole fraction of methane (XCH<sub>4</sub>) in the atmosphere is important for greenhouse gas emission management. Traditional XCH<sub>4</sub> 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 XCH<sub>4</sub> retrieval. First, the key wavelengths affecting XCH<sub>4</sub> retrieval were determined using a radiative transfer model. The TROPOspheric Monitoring Instrument (TROPOMI) L1B satellite data, L2 XCH<sub>4</sub> 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_XCH<sub>4</sub> model. Finally, the accuracy of the proposed model was evaluated using various parameter values and validated against XCH<sub>4</sub> products and Total Carbon Column Observing Network (TCCON) ground-based observations. The results showed that the proposed TRO_XGB_XCH<sub>4</sub> 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_XCH<sub>4</sub> retrieval model is comparable to that of the official TROPOMI L2 product.https://www.mdpi.com/2073-4433/16/3/279XCH<sub>4</sub> retrievalsatellite remote sensingTROPOMIradiative transfer modelXGBoost |
| spellingShingle | Wenhao Zhang Yao Li Bo Li Tong Li Zhengyong Wang Xiufeng Yang Yongtao Jin Lili Zhang Retrieval of Atmospheric XCH<sub>4</sub> via XGBoost Method Based on TROPOMI Satellite Data Atmosphere XCH<sub>4</sub> retrieval satellite remote sensing TROPOMI radiative transfer model XGBoost |
| title | Retrieval of Atmospheric XCH<sub>4</sub> via XGBoost Method Based on TROPOMI Satellite Data |
| title_full | Retrieval of Atmospheric XCH<sub>4</sub> via XGBoost Method Based on TROPOMI Satellite Data |
| title_fullStr | Retrieval of Atmospheric XCH<sub>4</sub> via XGBoost Method Based on TROPOMI Satellite Data |
| title_full_unstemmed | Retrieval of Atmospheric XCH<sub>4</sub> via XGBoost Method Based on TROPOMI Satellite Data |
| title_short | Retrieval of Atmospheric XCH<sub>4</sub> via XGBoost Method Based on TROPOMI Satellite Data |
| title_sort | retrieval of atmospheric xch sub 4 sub via xgboost method based on tropomi satellite data |
| topic | XCH<sub>4</sub> retrieval satellite remote sensing TROPOMI radiative transfer model XGBoost |
| url | https://www.mdpi.com/2073-4433/16/3/279 |
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