{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:40:35Z","timestamp":1772822435318,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T00:00:00Z","timestamp":1717804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Aberystwyth University\u2019s AberDoc Programme and Aberystwyth University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The ability to accurately map tropical wetland dynamics can significantly contribute to a number of areas, including food and water security, protection and enhancement of ecosystems, flood hazard management, and our understanding of natural greenhouse gas emissions. Yet currently, there is not a tractable solution for mapping tropical forested wetlands at high spatial and temporal resolutions at a regional scale. This means that we lack accurate and up-to-date information about some of the world\u2019s most significant wetlands, including the Amazon Basin. RadWet-L is an automated machine-learning classification technique for the mapping of both inundated forests and open water using ALOS ScanSAR data. We applied and validated RadWet-L for the Amazon Basin. The proposed method is computationally light and transferable across the range of landscape types in the Amazon Basin allowing, for the first time, regional inundation maps to be produced every 42 days at 50 m resolution over the period 2019\u20132023. Time series estimates of inundation extent from RadWet-L were significantly correlated with NASA-GFZ GRACE-FO water thickness (Pearson\u2019s r = 0.96, p &lt; 0.01), USDA G-REALM lake hight (Pearson\u2019s r between 0.63 and 0.91, p &lt; 0.01), and in situ river stage measurements (Pearson\u2019s r between 0.78 and 0.94, p &lt; 0.01). Additionally, we conducted an evaluation of 11,162 points against the input ScanSAR data revealing spatial and temporal consistency in the approach (F1 score = 0.97). Serial classifications of ALOS-2 PALSAR-2 ScanSAR data by RadWet-L can provide unique insights into the spatio-temporal inundation dynamics within the Amazon Basin. Understanding these dynamics can inform policy in the sustainable use of these wetlands, as well as the impacts of inundation dynamics on biodiversity and greenhouse gas budgets.<\/jats:p>","DOI":"10.3390\/rs16122078","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T08:49:03Z","timestamp":1718009343000},"page":"2078","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["RadWet-L: A Novel Approach for Mapping of Inundation Dynamics of Forested Wetlands Using ALOS-2 PALSAR-2 L-Band Radar Imagery"],"prefix":"10.3390","volume":"16","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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7896-502X","authenticated-orcid":false,"given":"Ake","family":"Rosenqvist","sequence":"additional","affiliation":[{"name":"solo Earth Observation (soloEO), Tokyo 104-0054, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1038\/s41586-022-05447-w","article-title":"Wetland Emission and Atmospheric Sink Changes Explain Methane Growth in 2020","volume":"612","author":"Peng","year":"2022","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4863","DOI":"10.5194\/acp-23-4863-2023","article-title":"Methane Emissions Are Predominantly Responsible for Record-Breaking Atmospheric Methane Growth Rates in 2020 and 2021","volume":"23","author":"Feng","year":"2023","journal-title":"Atmos. 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