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| Research on Satellite Greenhouse Gas Remote Sensing Retrieval Methods Based on Machine Learning |
| SHENG Shu-li1, ZOU Ming-min1, 2*, LIU Tian-qi1, CHENG Yong-ping1, CHEN Zi-zheng1, WANG Xu-wen1 |
1. Institute of Material Science and Information Technology, Anhui University, Hefei 230601, China
2. Anhui Provincial Laboratory of Information Materials and Intelligent Perception, Anhui University, Hefei 230601, China
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Abstract Greenhouse-gas satellite remote sensing provides vital data support for climate-change research. Accurately obtaining the spatiotemporal distribution of greenhouse gas concentrations is key for effective carbon emission accounting. This study aims to develop a satellite-based greenhouse-gas retrieval model using machine-learning methods, enabling rapid and high-precision inversion of column-averaged dry-air mixing ratios (XCO2 and XCH4). First, a training dataset was constructed using an atmospheric radiative-transfer model combined with measurements from the Total Carbon Column Observing Network (TCCON). Next, a one-dimensional convolutional neural network (1D CNN) was employed as the machine-learning method. The model leveraged Adaptive Moment Estimation (Adam) and Bayesian Optimization (BO) during training. Its performance was compared to that of Random Forest (RF) and Backpropagation Neural Network (BPNN) models. Results showed that the 1D-CNN model achieved correlation coefficients of 0.953 1 for XCO2 and 0.957 3 for XCH4 on the test dataset, outperforming both RF and BPNN. Finally, high-spectral-resolution observations from the Chinese GF-5B satellite's Greenhouse gases Monitoring Instrument (GMI/GF-5B) were used to retrieve global XCO2 and XCH4 for 2022—2024. Comparisons with data from nine TCCON stations demonstrated strong agreement: the correlation coefficients exceeded 0.938 5 for XCO2 and 0.959 8 for XCH4. Overall retrieval errors were within 2 ppm for XCO2 (with 99.15% of validation samples showing errors below 1.5 ppm) and within 10 ppb for XCH4.
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Received: 2025-02-17
Accepted: 2025-06-11
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Corresponding Authors:
ZOU Ming-min
E-mail: zoumm@ahu.edu.cn
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[1] YANG Xiao-yu, WANG Zhong-ting, PAN Guang, et al（杨晓钰, 王中挺, 潘 光）. Journal of Atmospheric and Environmental Optics（大气与环境光学学报）, 2022, 17（6）: 581.
[2] Liu Yi, Lu Daren, Chen Hongbin, et al. Remote Sensing Technology and Application, 2011, 26（2）: 247.
[3] Lopez F P A, Zhou G, Jing G, et al. Remote Sensing, 2022, 14（11）: 2622.
[4] WANG Lei, HUAN Ke-wei, LIU Xiao-xi（王 磊, 宦克为, 刘小溪）. National Analytical Chemistry Bulletin（分析化学）, 2022, 50（12）: 1918.
[5] PENG Hao-jie, ZHOU Yang, HU Xiao-fei（彭豪杰, 周 杨, 胡校飞）. Journal of Remote Sensing（遥感学报）, 2023, 27（2）: 430.
[6] MIAO Yun-fei, ZOU Ming-min, SHENG Shu-li, et al（缪云飞, 邹铭敏, 盛书丽, 等）. China Environmental Science（中国环境科学）, 2023, 43(S1): 20.
[7] Li K, Bai K, Jiao P, et al. Remote Sensing of Environment, 2024, 304: 114039.
[8] LI Jing-bo, ZHANG Ying, GAI Rong-li（李静波, 张 莹, 盖荣丽）. China Environmental Science（中国环境科学）, 2023, 43（4）: 1499.
[9] Zhao Z, Xie F, Ren T, et al. Journal of Quantitative Spectroscopy and Radiative Transfer, 2022, 278: 108006.
[10] Zhang W, Wang Z, Li T, et al. Atmosphere, 2025, 16(3): 238.
[11] Huang X, Deng Z, Jiang F, et al. Geophysical Research Letters, 2024, 51(8): e2023GL107536.
[12] Gong X, Zhang Y, Fan M, et al. Atmosphere, 2024, 15(1): 118.
[13] YE Han-han, WANG Xian-hua, WU Shi-chao, et al（叶函函, 王先华, 吴时超, 等）. Journal of Atmospheric and Environmental Optics（大气与环境光学学报）, 2021, 16（3）: 231.
[14] Liu Shuanghui, Li Xiaoying, Cao Xifeng. Remote Sensing Technology and Application, 2022, 37（2）: 436.
[15] LI Yun-fei, LI Jun, HE Lin（李云飞, 李 军, 贺 霖）. Journal of Remote Sensing（遥感学报）, 2022, 26（8）: 1614.
[16] TANG Yong-sheng, CHEN Zheng-guang（唐永生, 陈争光）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2019, 41（3）: 892.
[17] Saberioon M, Gholizadeh A, Ghaznavi A, et al. Computers and Electronics in Agriculture, 2024, 227: 109494.
[18] Wu Lijun, Wang Baoxing, Zhang Lei, et al. Journal of Near Infrared Spectroscopy, 2020, 28（3）: 153.
[19] Yoshida Y, Kikuchi N, Morino I, et al. Atmospheric Measurement Techniques, 2013, 6（6）: 1533.
[20] GAO Xiao-hong, YANG Yang, ZHANG Wei, et al（高小红, 杨 扬, 张 威, 等）. Remote Sensing Technology and Application（遥感技术与应用）, 2015, 30（5）: 849.
[21] Zhao R, Liu L, Liu X, et al. Nuclear Science and Techniques, 2025, 36（2）: 29.
[22] LI Qin-qin, WANG Xian-hua, YE Han-han, et al（李勤勤, 王先华, 叶函函, 等）. Acta Optica Sinica（光学学报）, 2020, 40（6）: 26. |
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