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High-Resolution Inversion Method for the Snow Water Equivalent Based on the GF-3 Satellite and Optimized EQeau Model

文献类型：期刊论文

作者Yang, Yichen2,4; Fang, Shifeng2; Wu, Hua2; Du, Jiaqiang1,3; Wang, Xiaohu2,4; Chen, Rensheng5,6; Liu, Yongqiang7; Wang, Hao2,4
刊名REMOTE SENSING
出版日期2022-10-01
卷号14期号:19页码:20
关键词snow water equivalent GF-3 the EQeau model S3H polarization decomposition
DOI10.3390/rs14194931
通讯作者Fang, Shifeng(fangsf@igsnrr.ac.cn)
英文摘要High-resolution snow water equivalent studies are important for obtaining a clear picture of the potential of water resources in arid areas, and SAR-based sensors can achieve meter-level snow water equivalent inversion. The advanced C-band SAR satellite Gaofen-3 (GF-3) can now achieve meter-level observations of the same area within one day and has great potential for the inversion of the snow water equivalent. The EQeau model is an empirical method for snow water equivalent inversion using C-band SAR satellites, but the model has major accuracy problems. In this paper, the EQeau model is improved by using classification of underlying surface types and polarization decomposition, and the inversion of the snow water equivalent was also completed using the new data source GF-3 input model. The results found that: (1) the classification of underlying surface types can significantly improve the fit between the snow thermal resistance and the backscattering coefficient ratio; (2) the accuracy of the snow density extracted by the GF-3 satellite using the Singh-Cloude Three-Component Hybrid (S3H) decomposition is better than IDW spatial interpolation, and the overall RMSE can reach 0.005 g/cm(3); (3) the accuracy of the optimized EQeau model is significantly improved, and the overall MRE is reduced from 27.4% to 10.3%. Compared with the original model, the optimized model is superior both in terms of verification accuracy and image detail. In the future, with the combination of advanced technologies such as the Internet of Things (IoT), long, gapless, all-weather, and high-resolution snow water equivalent inversion can be achieved, which is conducive to the realization of all-weather monitoring of the regional snow water equivalent.
WOS关键词SPATIOTEMPORAL VARIABILITY ; RADAR INTERFEROMETRY ; SPATIAL-DISTRIBUTION ; SAR DATA ; DYNAMICS ; DENSITY ; RUNOFF ; COVER ; DEPTH
资助项目National Natural Science Foundation of China[41971082] ; National Key Research and Development Program of China[2019YFC1510505]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000867181100001
出版者MDPI
资助机构National Natural Science Foundation of China ; National Key Research and Development Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/185555]  
专题中国科学院地理科学与资源研究所
通讯作者Fang, Shifeng
作者单位1.Chinese Res Inst Environm Sci, State Key Lab Environm Criteria & Risk Assessment, Beijing 100012, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Chinese Res Inst Environm Sci, Inst Ecol Environm Res, Beijing 100012, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Qilian Alpine Ecol & Hydrol Res Stn, Lanzhou 730000, Peoples R China
6.Northwest Univ, Coll Urban & Environm Sci, Xian 710000, Peoples R China
7.Xinjiang Univ, Coll Resource & Environm Sci, Urumqi 830046, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Yichen,Fang, Shifeng,Wu, Hua,et al. High-Resolution Inversion Method for the Snow Water Equivalent Based on the GF-3 Satellite and Optimized EQeau Model[J]. REMOTE SENSING,2022,14(19):20.
APA Yang, Yichen.,Fang, Shifeng.,Wu, Hua.,Du, Jiaqiang.,Wang, Xiaohu.,...&Wang, Hao.(2022).High-Resolution Inversion Method for the Snow Water Equivalent Based on the GF-3 Satellite and Optimized EQeau Model.REMOTE SENSING,14(19),20.
MLA Yang, Yichen,et al."High-Resolution Inversion Method for the Snow Water Equivalent Based on the GF-3 Satellite and Optimized EQeau Model".REMOTE SENSING 14.19(2022):20.

入库方式： OAI收割

来源：地理科学与资源研究所

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