{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T19:45:03Z","timestamp":1745523903610,"version":"3.37.3"},"reference-count":59,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T00:00:00Z","timestamp":1709164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFB3901101"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Aquatic vegetation is an important component of aquatic ecosystems; therefore, the classification and mapping of aquatic vegetation is an important aspect of lake management. Currently, the decision tree (DT) classification method based on spectral indices has been widely used in the extraction of aquatic vegetation data, but the disadvantage of this method is that it is difficult to fix the threshold value, which, in turn, affects the automatic classification effect. In this study, Sentinel-2 MSI data were used to produce a sample set (about 930 samples) of aquatic vegetation in four inland lakes (Lake Taihu, Lake Caohai, Lake Honghu, and Lake Dongtinghu) using the visual interpretation method, including emergent, floating-leaved, and submerged vegetation. Based on this sample set, a DL model (Res-U-Net) was used to train an automatic aquatic vegetation extraction model. The DL model achieved a higher overall accuracy, relevant error, and kappa coefficient (90%, 8.18%, and 0.86, respectively) compared to the DT method (79%, 23.07%, and 0.77) and random forest (78%,10.62% and 0.77) when utilizing visual interpretation results as the ground truth. When utilizing measured point data as the ground truth, the DL model exhibited accuracies of 59%, 78%, and 91% for submerged, floating-leaved, and emergent vegetation, respectively. In addition, the model still maintains good recognition in the presence of clouds with the influence of water bloom. When applying the model to Lake Honghu from January 2017 to October 2023, the obtained temporal variation patterns in the aquatic vegetation were consistent with other studies. The study in this paper shows that the proposed DL model has good application potential for extracting aquatic vegetation data.<\/jats:p>","DOI":"10.3390\/rs16050867","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T12:36:21Z","timestamp":1709296581000},"page":"867","source":"Crossref","is-referenced-by-count":3,"title":["Deep-Learning-Based Automatic Extraction of Aquatic Vegetation from Sentinel-2 Images\u2014A Case Study of Lake Honghu"],"prefix":"10.3390","volume":"16","author":[{"given":"Hangyu","family":"Gao","sequence":"first","affiliation":[{"name":"School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Ruren","family":"Li","sequence":"additional","affiliation":[{"name":"School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China"}]},{"given":"Qian","family":"Shen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5671-6399","authenticated-orcid":false,"given":"Yue","family":"Yao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]},{"given":"Yifan","family":"Shao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]},{"given":"Yuting","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]},{"given":"Wenxin","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Jinzhi","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yuting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Mingxia","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Rail Transportation Affairs, Shenyang Center for Urban and Rural Construction, Shenyang 110168, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.earscirev.2017.08.013","article-title":"Global Loss of Aquatic Vegetation in Lakes","volume":"173","author":"Zhang","year":"2017","journal-title":"Earth-Sci. 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