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    • Dense wetland sample production at large scale by combining multi-source thematic datasets and visual interpretation

    • “This study proposes a large-scale dense wetland sample production framework, which produced a total of 150688 wetland sample points, providing a reliable data foundation for wetland classifier training and accuracy verification.”
      • role：First author第一作者
      • Affiliation：

        State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

        School of Geographical and Environmental Science, Tianjin Normal University, Tianjin 300387, China

      • Email：pengkaifeng@mail.bnu.edu.cn
      • Introduction：彭凯锋，研究方向为植被生态与湿地遥感。E-mail： pengkaifeng@mail.bnu.edu.cn

      PENG Kaifeng

      12,
      • role：Corresponding author通信作者
      • Affiliation：

        State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

      • Email：jiangweiguo@bnu.edu.cn
      • Introduction：蒋卫国，研究方向为生态水文遥感。E-mail： jiangweiguo@bnu.edu.cn

      JIANG Weiguo

      1 * ,
      • Affiliation：

        Satellite Environment Center, Ministry of Ecology and Environment, Beijing 100094, China

      HOU Peng

      3,
      • Affiliation：

        State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

        School of Geography and Planning, Nanning Normal University, Nanning 530001, China

      LING Ziyan

      14,
      • Affiliation：

        Aerospace Information Research Institute, China Academy of Sciences, Beijing 100094, China

      NIU Zhenguo

      5,
      • Affiliation：

        Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

      MAO Dehua

      6,
      • Affiliation：

        State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

      HUANG Zhuo

      1
    • Vol. 28, Issue 2, Pages: 334-345(2024)  

    • DOI：10.11834/jrs.20211152    

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