{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T03:08:14Z","timestamp":1776136094299,"version":"3.50.1"},"reference-count":114,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:00:00Z","timestamp":1668211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19070102"],"award-info":[{"award-number":["XDA19070102"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19030203"],"award-info":[{"award-number":["XDA19030203"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["G2022055010L"],"award-info":[{"award-number":["G2022055010L"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2020VTA0001"],"award-info":[{"award-number":["2020VTA0001"]}]},{"name":"MOST High Level Foreign Expert program","award":["XDA19070102"],"award-info":[{"award-number":["XDA19070102"]}]},{"name":"MOST High Level Foreign Expert program","award":["XDA19030203"],"award-info":[{"award-number":["XDA19030203"]}]},{"name":"MOST High Level Foreign Expert program","award":["G2022055010L"],"award-info":[{"award-number":["G2022055010L"]}]},{"name":"MOST High Level Foreign Expert program","award":["2020VTA0001"],"award-info":[{"award-number":["2020VTA0001"]}]},{"name":"Chinese Academy of Sciences Pres-ident\u2019s International Fellowship Initiative","award":["XDA19070102"],"award-info":[{"award-number":["XDA19070102"]}]},{"name":"Chinese Academy of Sciences Pres-ident\u2019s International Fellowship Initiative","award":["XDA19030203"],"award-info":[{"award-number":["XDA19030203"]}]},{"name":"Chinese Academy of Sciences Pres-ident\u2019s International Fellowship Initiative","award":["G2022055010L"],"award-info":[{"award-number":["G2022055010L"]}]},{"name":"Chinese Academy of Sciences Pres-ident\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>The annual flood and the alteration in hydrological regimes are the most vital concerns in the Vietnamese Mekong Delta (VMD). Although synthetic aperture radar (SAR) Sentinel-1 imagery is widely used for water management, only a few studies have used Sentinel-1 data for mapping surface water and monitoring flood events in the VMD. This study developed an algorithm to implement (i) automatic Otsu threshold on a series of Sentinel-1 images to extract surface water and (ii) time series analyses on the derived surface water maps to detect flood water extent in near-real-time (NRT). Specifically, only cross-polarized VH was selected after an assessment of different Sentinel-1 polarizations. The dynamic Otsu thresholding algorithm was applied to identify an optimal threshold for each pre-processed Sentinel-1 VH image to separate water from non-water pixels for producing a time series of surface water maps. The derived Sentinel-1 surface water maps were visually compared with the Sentinel-2 Full Resolution Browse (FRB) and statistically examined with the Sentinel-2 Multispectral Instrument (MSI) surface water maps, which were generated by applying the Otsu threshold on the normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) images. The visual comparison showed a strong correspondence between the Sentinel-1 surface water maps and Sentinel-2 FRB images in three periods, including rice\u2019s sowing season, flood period, and rice\u2019s maturation stage. A good statistical agreement suggested that the performance of the dynamic Otsu thresholding algorithm on Sentinel-1 image time series to map surface water is effective in river areas (R2 = 0.97 and RMSE = 1.18%), while it is somewhat lower in paddy field areas (R2 = 0.88 and RMSE = 3.88%). Afterward, a flood mapping algorithm in NRT was developed by applying the change-detection-based time series analyses on the derived Sentinel-1 surface water maps. Every single pixel at the time t\u00a0 is respectively referred to its state in the water\/non-water and flooded\/non-flooded maps at the previous time t\u22121 to be classified into a flooded or non-flooded pixel. The flood mapping algorithm enables updates at each time step to generate temporal flood maps in NRT for monitoring flood water extent in large-scale areas. This study provides a tool to rapidly generate surface water and flood maps to support water management and risk reduction in the VMD. The future improvement of the current algorithm is discussed.<\/jats:p>","DOI":"10.3390\/rs14225721","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T04:24:10Z","timestamp":1668399850000},"page":"5721","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9738-6832","authenticated-orcid":false,"given":"Khuong H.","family":"Tran","sequence":"first","affiliation":[{"name":"College of Resources, Environment and Tourism, Capital Normal University, 105 West Third Ring Road North, Haidian District, Beijing 100048, China"},{"name":"College of Geospatial Information Science and Technology, Capital Normal University, 105 West Third Ring Road North, Haidian District, Beijing 100048, China"},{"name":"Southern Institute for Water Resources Planning (SIWRP), 271\/3 An Duong Vuong Street, District 5, Ho Chi Minh City 748000, Vietnam"},{"name":"Geospatial Science Center of Excellence, Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD 57007, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9176-4556","authenticated-orcid":false,"given":"Massimo","family":"Menenti","sequence":"additional","affiliation":[{"name":"College of Geospatial Information Science and Technology, Capital Normal University, 105 West Third Ring Road North, Haidian District, Beijing 100048, China"},{"name":"Department of Geoscience and Remote Sensing, Delft University of Technology, 2600 GA Delft, The Netherlands"},{"name":"State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3108-8645","authenticated-orcid":false,"given":"Li","family":"Jia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cramb, R. 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