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, Jianming2,3; Liu, Po4; Xia, Jun1,2; Zhao, Yanjun2,3; Dong, Yi2,3 |
刊名 | REMOTE SENSING
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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 |
DOI | 10.3390/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 |
WOS记录号 | WOS:000845305900001 |
出版者 | MDPI |
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.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Surveying & Mapping, Beijing 100830, 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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