{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T19:50:45Z","timestamp":1765828245224,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T00:00:00Z","timestamp":1670025600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42077438"],"award-info":[{"award-number":["42077438"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Optimized rain gauge networks minimize their input and maintenance costs. Satellite precipitation observations are particularly susceptible to the effects of terrain elevation, vegetation, and other topographical factors, resulting in large deviations between satellite and ground-based precipitation data. Satellite precipitation observations are more inaccurate where the deviations change more drastically, indicating that rain gauge stations should be utilized at these locations. This study utilized satellite precipitation observation data to facilitate rain gauge network optimization. The deviations between ground-based precipitation data and three types of satellite precipitation observation data were used for entropy estimation. The rain gauge network in the Oujiang River Basin of China was optimally designed according to the principle of maximum joint entropy. Two optimization schemes of culling and supplementing 40 existing sites and 35 virtual sites were explored. First, the optimization and ranking of the rain gauge station network showed good stability and consistency. In addition, the joint entropy of deviation was larger than that of ground-based precipitation data alone, leading to a higher degree of discrimination between rain gauge stations and enabling the use of deviation data instead of ground-based precipitation data to assist network optimization, with more reasonable and interpretable results.<\/jats:p>","DOI":"10.3390\/rs14236142","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T05:31:32Z","timestamp":1670218292000},"page":"6142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Optimal Rain Gauge Network Design Aided by Multi-Source Satellite Precipitation Observation"],"prefix":"10.3390","volume":"14","author":[{"given":"Helong","family":"Wang","sequence":"first","affiliation":[{"name":"College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China"},{"name":"Zhejiang Institute of Hydraulics & Estuary (Zhejiang Institute of Marine Planning and Design), Hangzhou 310020, China"}]},{"given":"Wenlong","family":"Chen","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Planning and Design Group, Nanjing 210019, China"}]},{"given":"Zukang","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 210098, China"}]},{"given":"Yueping","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Dingtao","family":"Shen","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis and Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China"},{"name":"College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1002\/2017RG000574","article-title":"A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons","volume":"56","author":"Sun","year":"2018","journal-title":"Rev. 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