{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T13:59:49Z","timestamp":1772114389926,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T00:00:00Z","timestamp":1658188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovation Group Project of Hubei Natural Science Foundation","award":["2019CFA019"],"award-info":[{"award-number":["2019CFA019"]}]},{"name":"Innovation Group Project of Hubei Natural Science Foundation","award":["2020BCA074"],"award-info":[{"award-number":["2020BCA074"]}]},{"name":"Hubei Provincial Key Research and Development Program","award":["2019CFA019"],"award-info":[{"award-number":["2019CFA019"]}]},{"name":"Hubei Provincial Key Research and Development Program","award":["2020BCA074"],"award-info":[{"award-number":["2020BCA074"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-temporal-resolution inundation maps play an important role in surface water monitoring, especially in lake sites where water bodies change tremendously. Synthetic Aperture Radar (SAR) that guarantees a full time-series in monitoring surface water due to its cloud-penetrating capability is preferred in practice. To date, the methods of extracting and analyzing inundation maps of lake sites have been widely discussed, but the method of extracting surface water maps refined by inundation frequency map and the distinction of inundation frequency map from different datasets have not been fully explored. In this study, we leveraged the Google Earth Engine platform to compare and evaluate the effects of a method combining a histogram-based algorithm with a temporal-filtering algorithm in order to obtain high-quality surface water maps. Both algorithms were conducted on Sentinel-1 images over Poyang Lake and Dongting Lake, the two largest lakes in China, respectively. High spatiotemporal time-series analyses of both lakes were implemented between 2017 and 2021, while the inundation frequency maps extracted from Sentinel-1 data were compared with those extracted from Landsat images. It was found that Sentinel-1 can monitor water inundation with a substantially higher accuracy, although minor differences were found between the two sites, with the overall accuracy for Poyang Lake (95.38\u201398.69%) being higher than that of Dongting Lake (95.05\u201397.5%). The minimum and maximum water areas for five years were 1232.96 km2 and 3828.36 km2 in Poyang Lake, and 624.7 km2 and 2189.17 km2 in Dongting Lake. Poyang Lake was frequently inundated with 553.03 km2 of permanent water and 3361.39 km2 of seasonal water while Dongting Lake was less frequently inundated with 320.09 km2 of permanent water and 2224.53 km2 of seasonal water. The inundation frequency maps from different data sources had R2 values higher than 0.8, but there were still significant differences between them. The overall inundation frequency values of the Sentinel-1 inundation frequency maps were lower than those of the Landsat inundation frequency maps due to the severe contamination from cloud cover in Landsat imagery, which should be paid attention in practical application.<\/jats:p>","DOI":"10.3390\/rs14143473","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T23:10:22Z","timestamp":1658272222000},"page":"3473","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Monitoring Surface Water Inundation of Poyang Lake and Dongting Lake in China Using Sentinel-1 SAR Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Zirui","family":"Wang","sequence":"first","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Chucai Honors College, Hubei University, Wuhan 430062, China"}]},{"given":"Fei","family":"Xie","sequence":"additional","affiliation":[{"name":"Hubei Institute of Land Surveying and Mapping, Wuhan 430034, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0685-4897","authenticated-orcid":false,"given":"Feng","family":"Ling","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"given":"Yun","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.4319\/lo.2006.51.5.2388","article-title":"The global abundance and size distribution of lakes, ponds, and impoundments","volume":"51","author":"Downing","year":"2006","journal-title":"Limnol. 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