{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T16:24:47Z","timestamp":1782577487957,"version":"3.54.5"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,10]],"date-time":"2020-01-10T00:00:00Z","timestamp":1578614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hunan Provincial Natural Science Foundation of China","award":["No.2018JJ3348"],"award-info":[{"award-number":["No.2018JJ3348"]}]},{"name":"Research Foundation of Education Bureau of Hunan Province, China","award":["No.17C0952"],"award-info":[{"award-number":["No.17C0952"]}]},{"name":"Foundation of China Scholarship Council","award":["No.201806725009"],"award-info":[{"award-number":["No.201806725009"]}]},{"name":"Construction Program for First-Class Disciplines (Geography) of Hunan Province, China","award":["No.-"],"award-info":[{"award-number":["No.-"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurately quantifying water inundation dynamics in terms of both spatial distributions and temporal variability is essential for water resources management. Currently, the water map is usually derived from synthetic aperture radar (SAR) data with the support of auxiliary datasets, using thresholding methods and followed by morphological operations to further refine the results. However, auxiliary datasets may lose efficacy on large plain areas, whilst the parameters of morphological operations are hard to be decided in different situations. Here, a heuristic and automatic water extraction (HAWE) method is proposed to extract the water map from Sentinel-1 SAR data. In the HAWE, we integrate tile-based thresholding and the active contour model, in which the former provides a convincing initial water map used as a heuristic input, and the latter refines the initial map by using image gradient information. The proposed approach was tested on the Dongting Lake plain (China) by comparing the extracted water map with the reference data derived from the Sentinel-2 dataset. For the two selected test sites, the overall accuracy of water classification is between 94.90% and 97.21% whilst the Kappa coefficient is within the range of 0.89 and 0.94. For the entire study area, the overall accuracy is between 94.32% and 96.7% and the Kappa coefficient ranges from 0.80 to 0.90. The results show that the proposed method is capable of extracting water inundations with satisfying accuracy.<\/jats:p>","DOI":"10.3390\/rs12020243","type":"journal-article","created":{"date-parts":[[2020,1,10]],"date-time":"2020-01-10T10:20:29Z","timestamp":1578651629000},"page":"243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas"],"prefix":"10.3390","volume":"12","author":[{"given":"Shunshi","family":"Hu","sequence":"first","affiliation":[{"name":"College of Resources and Environmental Sciences, Hunan Normal University, Changsha 410081, China"},{"name":"Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxin","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Resources and Environmental Sciences, Hunan Normal University, Changsha 410081, China"},{"name":"Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6116-3194","authenticated-orcid":false,"given":"Jinchang","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510640, China"},{"name":"Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G11XW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huimin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510640, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an 710048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Hong","sequence":"additional","affiliation":[{"name":"Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089-2560, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1126\/science.aau3842","article-title":"Measuring Earth\u2019s rivers","volume":"361","author":"Palmer","year":"2018","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"303","DOI":"10.5194\/nhess-9-303-2009","article-title":"Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data","volume":"9","author":"Martinis","year":"2009","journal-title":"Nat. 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