School of Atmospheric Sciences, Southern Marine Science and Engineering
Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082,
Guangdong, China
Jinqing Wang
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research
Institute, Chinese Academy of Sciences, Beijing 100094, China
School of Electronic, Electrical and Communication Engineering, University
of Chinese Academy of Sciences, Beijing 100049, China
Jun Mi
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research
Institute, Chinese Academy of Sciences, Beijing 100094, China
School of Electronic, Electrical and Communication Engineering, University
of Chinese Academy of Sciences, Beijing 100049, China
Wendi Liu
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research
Institute, Chinese Academy of Sciences, Beijing 100094, China
School of Electronic, Electrical and Communication Engineering, University
of Chinese Academy of Sciences, Beijing 100049, China
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Total article views: 14,261 (including HTML, PDF, and XML)
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10,601
3,520
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14,261
166
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BibTeX: 166
EndNote: 248
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Total article views: 16,279 (including HTML, PDF, and XML)
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Total article views: 2,018 (including HTML, PDF, and XML)
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An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping and generates the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal wetland sub-categories (mangrove, salt marsh and tidal flats).
An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones...
