{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T16:08:20Z","timestamp":1783613300762,"version":"3.55.0"},"reference-count":70,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T00:00:00Z","timestamp":1689033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1\/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland\/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95).<\/jats:p>","DOI":"10.3390\/rs15143495","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T01:01:41Z","timestamp":1689123701000},"page":"3495","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Wetland Mapping in Great Lakes Using Sentinel-1\/2 Time-Series Imagery and DEM Data in Google Earth Engine"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1541-1393","authenticated-orcid":false,"given":"Farzane","family":"Mohseni","sequence":"first","affiliation":[{"name":"Institute of Geodesy and Geoinformation, University of Bonn, 53115 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9495-4010","authenticated-orcid":false,"given":"Meisam","family":"Amani","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"},{"name":"WSP Environment and Infrastructure Canada Limited, Ottawa, ON K2E 7L5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8628-3563","authenticated-orcid":false,"given":"Pegah","family":"Mohammadpour","sequence":"additional","affiliation":[{"name":"Univ of Coimbra, ADAI, Department of Mechanical Engineering, Rua Lu\u00eds Reis Santos, P\u00f3lo II, 3030-788 Coimbra, Portugal"},{"name":"Universidad de Alcala, Environmental Remote Sensing Research Group, Department of Geology, Geography and Environment, Colegios 2, 28801 Alcal\u00e1 de Henares, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2318-8216","authenticated-orcid":false,"given":"Mohammad","family":"Kakooei","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Chalmers University of Technology, R\u00e4nnv\u00e4gen 6, 41258 Gothenburg, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5108-4828","authenticated-orcid":false,"given":"Shuanggen","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"},{"name":"Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0455-4882","authenticated-orcid":false,"given":"Armin","family":"Moghimi","sequence":"additional","affiliation":[{"name":"Ludwig-Franzius Institute of Hydraulic, Estuarine and Coastal Engineering, Leibniz University Hannover, Nienburger Str. 4, 30167 Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107485","DOI":"10.1016\/j.ecolind.2021.107485","article-title":"Mapping and assessment of wetland conditions by using remote sensing images and POI data","volume":"127","author":"Yang","year":"2021","journal-title":"Ecol. 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