{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T20:35:43Z","timestamp":1775680543521,"version":"3.50.1"},"reference-count":79,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Aberystwyth University\u2019s AberDoc Programme"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mapping the spatial and temporal dynamics of tropical herbaceous wetlands is vital for a wide range of applications. Inundated vegetation can account for over three-quarters of the total inundated area, yet widely used EO mapping approaches are limited to the detection of open water bodies. This paper presents a new wetland mapping approach, RadWet, that automatically defines open water and inundated vegetation training data using a novel mixture of radar, terrain, and optical imagery. Training data samples are then used to classify serial Sentinel-1 radar imagery using an ensemble machine learning classification routine, providing information on the spatial and temporal dynamics of inundation every 12 days at a resolution of 30 m. The approach was evaluated over the period 2017\u20132022, covering a range of conditions (dry season to wet season) for two sites: (1) the Barotseland Floodplain, Zambia (31,172 km2) and (2) the Upper Rupununi Wetlands in Guyana (11,745 km2). Good agreement was found at both sites using random stratified accuracy assessment data (n = 28,223) with a median overall accuracy of 89% in Barotseland and 80% in the Upper Rupununi, outperforming existing approaches. The results revealed fine-scale hydrological processes driving inundation patterns as well as temporal patterns in seasonal flood pulse timing and magnitude. Inundated vegetation dominated wet season wetland extent, accounting for a mean 80% of total inundation. RadWet offers a new way in which tropical wetlands can be routinely monitored and characterised. This can provide significant benefits for a range of application areas, including flood hazard management, wetland inventories, monitoring natural greenhouse gas emissions and disease vector control.<\/jats:p>","DOI":"10.3390\/rs15061705","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T06:00:01Z","timestamp":1679464801000},"page":"1705","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["RadWet: An Improved and Transferable Mapping of Open Water and Inundated Vegetation Using Sentinel-1"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5054-1548","authenticated-orcid":false,"given":"Gregory","family":"Oakes","sequence":"first","affiliation":[{"name":"Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth SY23 3DB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7928-8873","authenticated-orcid":false,"given":"Andy","family":"Hardy","sequence":"additional","affiliation":[{"name":"Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth SY23 3DB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7435-0148","authenticated-orcid":false,"given":"Pete","family":"Bunting","sequence":"additional","affiliation":[{"name":"Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth SY23 3DB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s11273-016-9487-x","article-title":"Tropical Wetlands, Climate, and Land-Use Change: Adaptation and Mitigation Opportunities","volume":"24","author":"Kolka","year":"2016","journal-title":"Wetl. 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