{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:07:21Z","timestamp":1774894041193,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,1]],"date-time":"2018-02-01T00:00:00Z","timestamp":1517443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Reliable information about the spatial distribution of surface waters is critically important in various scientific disciplines. Synthetic Aperture Radar (SAR) is an effective way to detect floods and monitor water bodies over large areas. Sentinel-1 is a new available SAR and its spatial resolution and short temporal baselines have the potential to facilitate the monitoring of surface water changes, which are dynamic in space and time. While several methods and tools for flood detection and surface water extraction already exist, they often comprise a significant manual user interaction and do not specifically target the exploitation of Sentinel-1 data. The existing methods commonly rely on thresholding at the level of individual pixels, ignoring the correlation among nearby pixels. Thus, in this paper, we propose a fully automatic processing chain for rapid flood and surface water mapping with smooth labeling based on Sentinel-1 amplitude data. The method is applied to three different sites submitted to recent flooding events. The quantitative evaluation shows relevant results with overall accuracies of more than 98% and F-measure values ranging from 0.64 to 0.92. These results are encouraging and the first step to proposing operational image chain processing to help end-users quickly map flooding events or surface waters.<\/jats:p>","DOI":"10.3390\/rs10020217","type":"journal-article","created":{"date-parts":[[2018,2,2]],"date-time":"2018-02-02T04:20:50Z","timestamp":1517545250000},"page":"217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":195,"title":["A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery"],"prefix":"10.3390","volume":"10","author":[{"given":"Filsa","family":"Bioresita","sequence":"first","affiliation":[{"name":"Laboratoire Image, Ville, Environnement\u2014LIVE\/CNRS UMR 7362, Department of Geography, University of Strasbourg, 3 rue de l\u2019Argonne, 67000 Strasbourg, France"},{"name":"Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia"}]},{"given":"Anne","family":"Puissant","sequence":"additional","affiliation":[{"name":"Laboratoire Image, Ville, Environnement\u2014LIVE\/CNRS UMR 7362, Department of Geography, University of Strasbourg, 3 rue de l\u2019Argonne, 67000 Strasbourg, France"}]},{"given":"Andr\u00e9","family":"Stumpf","sequence":"additional","affiliation":[{"name":"\u00c9cole et Observatoire des Sciences de la Terre\u2014EOST\/CNRS UMS 830, University of Strasbourg, 67084 Strasbourg, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0426-4911","authenticated-orcid":false,"given":"Jean-Philippe","family":"Malet","sequence":"additional","affiliation":[{"name":"\u00c9cole et Observatoire des Sciences de la Terre\u2014EOST\/CNRS UMS 830, University of Strasbourg, 67084 Strasbourg, France"},{"name":"Institut de Physique du Globe de Strasbourg\u2014IPGS\/CNRS UMR 7516, University of Strasbourg, 67084 Strasbourg, France"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1023\/B:NHAZ.0000037035.65105.95","article-title":"Application of remote sensing in flood management with special reference to monsoon Asia: A review","volume":"33","author":"Sanyal","year":"2004","journal-title":"Nat. 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