{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:05:12Z","timestamp":1787029512149,"version":"build-2736575974"},"reference-count":58,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"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":["51879067"],"award-info":[{"award-number":["51879067"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["B200204038"],"award-info":[{"award-number":["B200204038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019slkj-B1"],"award-info":[{"award-number":["2019slkj-B1"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["51879067"],"award-info":[{"award-number":["51879067"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["B200204038"],"award-info":[{"award-number":["B200204038"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2019slkj-B1"],"award-info":[{"award-number":["2019slkj-B1"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hydraulic Science and Technology Plan Foundation of Shaanxi Province","award":["51879067"],"award-info":[{"award-number":["51879067"]}]},{"name":"Hydraulic Science and Technology Plan Foundation of Shaanxi Province","award":["B200204038"],"award-info":[{"award-number":["B200204038"]}]},{"name":"Hydraulic Science and Technology Plan Foundation of Shaanxi Province","award":["2019slkj-B1"],"award-info":[{"award-number":["2019slkj-B1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Integrated Multisatellite Retrievals for GPM (IMERG) is designed to derive precipitation by merging data from all the passive microwave (PMW) and infrared (IR) sensors. While the input source errors originating from the PMW and IR sensors are important, their structure, characteristics, and algorithm improvement remain unclear. Our study utilized a four-component error decomposition (4CED) method and a systematic and random error decomposition method to evaluate the detectability of IMERG dataset and identify the precipitation errors based on the multi-sensors. The 30 min data from 30 precipitation stations in the Tunxi Watershed were used to evaluate the IMERG data from 2018 to 2020. The input source includes five types of PMW sensors and IR instruments. The results show that the sample ratio for IR (Morph, IR + Morph, and IR only) is much higher than that for PMW (AMSR2, SSMIS, GMI, MHS, and ATMS), with a ratio of 72.8% for IR sources and a ratio of 27.2% for PMW sources. The high false ratio of the IR sensor leads to poor detectability performance of the false alarm ratio (FAR, 0.5854), critical success index (CSI, 0.3014), and Brier score (BS, 0.1126). As for the 4CED, Morph and Morph + IR have a large magnitude of high total bias (TB), hit overestimate bias (HOB), hit underestimate bias (HUB), false bias (FB), and miss bias (MB), which is related to the prediction ability and sample size. In addition, systematic error is the prominent component for AMSR2, SSMIS, GMI, and Morph + IR, indicating some inherent error (retrieval algorithm) that needs to be removed. These findings can support improving the retrieval algorithm and reducing errors in the IMERG dataset.<\/jats:p>","DOI":"10.3390\/rs15061710","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T07:46:43Z","timestamp":1679471203000},"page":"1710","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Evaluation and Error Decomposition of IMERG Product Based on Multiple Satellite Sensors"],"prefix":"10.3390","volume":"15","author":[{"given":"Yunping","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China"},{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5288-9372","authenticated-orcid":false,"given":"Ke","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China"},{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"},{"name":"Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China"},{"name":"CMA-HHU Joint Laboratory for HydroMeteorological Studies, Hohai University, Nanjing 210098, China"},{"name":"Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 210098, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0058-9332","authenticated-orcid":false,"given":"Andras","family":"Bardossy","sequence":"additional","affiliation":[{"name":"Institute for Water and Environmental System Modeling, University of Stuttgart, 70569 Stuttgart, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoji","family":"Shen","sequence":"additional","affiliation":[{"name":"Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujia","family":"Cheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China"},{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,22]]},"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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