{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T15:10:17Z","timestamp":1792163417625,"version":"4.4.0"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T00:00:00Z","timestamp":1640217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Research Fund of National Earth Observation Data Center","award":["NODAOP2020021"],"award-info":[{"award-number":["NODAOP2020021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multiple source satellite datasets, including the Gaofen (GF) series and Zhuhai-1 hyperspectral, are provided to detect and monitor the floods. Considering the complexity of land cover changes within the flooded areas and the different characteristics of the multi-source remote sensing dataset, we proposed a new coarse-to-fine framework for detecting floods at a large scale. Firstly, the coarse results of the water body were generated by the binary segmentation of GF-3 SAR, the water indexes of GF-1\/6 multispectral, and Zhuhai-1 hyperspectral images. Secondly, the fine results were achieved by the deep neural networks with noisy-label learning. More specifically, the Unet with the T-revision is adopted as the noisy label learning method. The results demonstrated the reliability and accuracy of water mapping retrieved by the noisy learning method. Finally, the differences in flooding patterns in different regions were also revealed. We presented examples of Poyang Lake to show the results of our framework. The rapid and robust flood monitoring method proposed is of great practical significance to the dynamic monitoring of flood situations and the quantitative assessment of flood disasters based on multiple Chinese satellite datasets.<\/jats:p>","DOI":"10.3390\/rs14010051","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T21:40:21Z","timestamp":1640295621000},"page":"51","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Flood Detection Using Multiple Chinese Satellite Datasets during 2020 China Summer Floods"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5902-7126","authenticated-orcid":false,"given":"Lianchong","family":"Zhang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junshi","family":"Xia","sequence":"additional","affiliation":[{"name":"Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo 103-0027, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.isprsjprs.2005.02.002","article-title":"Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards","volume":"59","author":"Tralli","year":"2005","journal-title":"ISPRS J. 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