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Merging Multisatellite and Gauge Precipitation Based on Geographically Weighted Regression and Long Short-Term Memory Network

文献类型：期刊论文

作者Shen, Jianming1,2; Liu, Po3; Xia, Jun1,4; Zhao, Yanjun1,2; Dong, Yi1,2
刊名REMOTE SENSING
出版日期2022-08-01
卷号14期号:16页码:20
关键词deep learning multiple-satellite-based precipitation products gauge observation GWR LSTM
DOI10.3390/rs14163939
通讯作者Liu, Po(liupo@casm.ac.cn)
英文摘要To generate high-quality spatial precipitation estimates, merging rain gauges with a single-satellite precipitation product (SPP) is a common approach. However, a single SPP cannot capture the spatial pattern of precipitation well, and its resolution is also too low. This study proposed an integrated framework for merging multisatellite and gauge precipitation. The framework integrates the geographically weighted regression (GWR) for improving the spatial resolution of precipitation estimations and the long short-term memory (LSTM) network for improving the precipitation estimation accuracy by exploiting the spatiotemporal correlation pattern between multisatellite precipitation products and rain gauges. Specifically, the integrated framework was applied to the Han River Basin of China for generating daily precipitation estimates from the data of both rain gauges and four SPPs (TRMM_3B42, CMORPH, PERSIANN-CDR, and GPM-IMAGE) during the period of 2007-2018. The results show that the GWR-LSTM framework significantly improves the spatial resolution and accuracy of precipitation estimates (resolution of 0.05 degrees, correlation coefficient of 0.86, and Kling-Gupta efficiency of 0.6) over original SPPs (resolution of 0.25 degrees or 0.1 degrees, correlation coefficient of 0.36-0.54, Kling-Gupta efficiency of 0.30-0.52). Compared with other methods, the correlation coefficient for the whole basin is improved by approximately 4%. Especially in the lower reaches of the Han River, the correlation coefficient is improved by 15%. In addition, this study demonstrates that merging multiple-satellite and gauge precipitation is much better than merging partial products of multiple satellite with gauge observations.
WOS关键词RAINFALL ; RADAR ; SATELLITE ; PRODUCTS ; MICROWAVE ; PROJECT ; TRMM ; BIAS
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23040304] ; National Natural Science Foundation of China[41890823]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:000845305900001
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/182225]  
专题中国科学院地理科学与资源研究所
通讯作者Liu, Po
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Surveying & Mapping, Beijing 100830, Peoples R China
4.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China
推荐引用方式
GB/T 7714		 Shen, Jianming,Liu, Po,Xia, Jun,et al. Merging Multisatellite and Gauge Precipitation Based on Geographically Weighted Regression and Long Short-Term Memory Network[J]. REMOTE SENSING,2022,14(16):20.
APA Shen, Jianming,Liu, Po,Xia, Jun,Zhao, Yanjun,&Dong, Yi.(2022).Merging Multisatellite and Gauge Precipitation Based on Geographically Weighted Regression and Long Short-Term Memory Network.REMOTE SENSING,14(16),20.
MLA Shen, Jianming,et al."Merging Multisatellite and Gauge Precipitation Based on Geographically Weighted Regression and Long Short-Term Memory Network".REMOTE SENSING 14.16(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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