{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:00:32Z","timestamp":1776182432129,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"the Horizon 2020 Program of the European Union","doi-asserted-by":"publisher","award":["776848"],"award-info":[{"award-number":["776848"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"the Horizon 2020 Program of the European Union","doi-asserted-by":"publisher","award":["GL20200161002"],"award-info":[{"award-number":["GL20200161002"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"the Horizon 2020 Program of the European Union","doi-asserted-by":"publisher","award":["2020VTA0001"],"award-info":[{"award-number":["2020VTA0001"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the MOST High-Level Foreign Expert program","award":["776848"],"award-info":[{"award-number":["776848"]}]},{"name":"the MOST High-Level Foreign Expert program","award":["GL20200161002"],"award-info":[{"award-number":["GL20200161002"]}]},{"name":"the MOST High-Level Foreign Expert program","award":["2020VTA0001"],"award-info":[{"award-number":["2020VTA0001"]}]},{"name":"the Chinese Academy of Sciences President\u2019s International Fellowship Initiative","award":["776848"],"award-info":[{"award-number":["776848"]}]},{"name":"the Chinese Academy of Sciences President\u2019s International Fellowship Initiative","award":["GL20200161002"],"award-info":[{"award-number":["GL20200161002"]}]},{"name":"the Chinese Academy of Sciences President\u2019s International Fellowship Initiative","award":["2020VTA0001"],"award-info":[{"award-number":["2020VTA0001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precise and accurate delineation of flooding areas with synthetic aperture radar (SAR) and multi-spectral (MS) data is challenging because flooded areas are inherently heterogeneous as emergent vegetation (EV) and turbid water (TW) are common. We addressed these challenges by developing and applying a new stepwise sequence of unsupervised and supervised classification methods using both SAR and MS data. The MS and SAR signatures of land and water targets in the study area were evaluated prior to the classification to identify the land and water classes that could be delineated. The delineation based on a simple thresholding method provided a satisfactory estimate of the total flooded area but did not perform well on heterogeneous surface water. To deal with the heterogeneity and fragmentation of water patches, a new unsupervised classification approach based on a combination of thresholding and segmentation (CThS) was developed. Since sandy areas and emergent vegetation could not be classified by the SAR-based unsupervised methods, supervised random forest (RF) classification was applied to a time series of SAR and co-event MS data, both combined and separated. The new stepwise approach was tested for determining the flood extent of two events in Italy. The results showed that all the classification methods applied to MS data outperformed the ones applied to SAR data. Although the supervised RF classification may lead to better accuracies, the CThS (unsupervised) method achieved precision and accuracy comparable to the RF, making it more appropriate for rapid flood mapping due to its ease of implementation.<\/jats:p>","DOI":"10.3390\/rs14153718","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T23:33:01Z","timestamp":1659569581000},"page":"3718","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Evaluation of SAR and Optical Data for Flood Delineation Using Supervised and Unsupervised Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9846-6890","authenticated-orcid":false,"given":"Fatemeh","family":"Foroughnia","sequence":"first","affiliation":[{"name":"Department of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg, 2628 CN Delft, The Netherlands"}]},{"given":"Silvia Maria","family":"Alfieri","sequence":"additional","affiliation":[{"name":"Department of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg, 2628 CN Delft, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9176-4556","authenticated-orcid":false,"given":"Massimo","family":"Menenti","sequence":"additional","affiliation":[{"name":"Department of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg, 2628 CN Delft, The Netherlands"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8655-5266","authenticated-orcid":false,"given":"Roderik","family":"Lindenbergh","sequence":"additional","affiliation":[{"name":"Department of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg, 2628 CN Delft, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1002\/(SICI)1099-1085(199708)11:10<1427::AID-HYP473>3.0.CO;2-S","article-title":"Satellite remote sensing of river inundation area, stage, and discharge: A review","volume":"11","author":"Smith","year":"1997","journal-title":"Hydrol. 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