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    • Estimating winter wheat biomass by coupling the CBA-Wheat model and multispectral remote sensing

    • “The latest research progress shows that scientists have successfully improved the accuracy of winter wheat biomass estimation using the CBA Wheat model optimized by genetic algorithm, providing an effective tool for large-scale remote sensing prediction.”
      • role：First author第一作者
      • Affiliation：

        College of Geodesy and Geomatics Information Technology, Shandong University of Science and Technology, Qingdao 266590, China

      • Email：wangshijun@sdust.edu.cn
      • Introduction：王士俊，研究方向为农业定量遥感。E-mail： wangshijun@sdust.edu.cn

      WANG Shijun

      1,
      • Affiliation：

        College of Geodesy and Geomatics Information Technology, Shandong University of Science and Technology, Qingdao 266590, China

      LIU Miao

      1,
      • Affiliation：

        Key Laboratory of Quantitative Remote Sensing in Agriculture of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

      ZHAO Yu

      2,
      • Affiliation：

        China Institute of Intelligent Information Processing and Systems, Central South University, Changsha 410083, China

      LIU Zhaoyu

      3,
      • Affiliation：

        College of Geodesy and Geomatics Information Technology, Shandong University of Science and Technology, Qingdao 266590, China

      LIU Xiuyu

      1,
      • Affiliation：

        Key Laboratory of Quantitative Remote Sensing in Agriculture of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

      FENG Haikuan

      2,
      • Affiliation：

        Institute of Agricultural Information and Economics, Shandong Academy of Agricultural Sciences, Jinan 250100, China

      SUI Xueyan

      4,
      • role：Corresponding author通信作者
      • Affiliation：

        College of Geodesy and Geomatics Information Technology, Shandong University of Science and Technology, Qingdao 266590, China

        Key Laboratory of Quantitative Remote Sensing in Agriculture of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

      • Email：lizh323@126.com
      • Introduction：李振海，研究方向为农业遥感。E-mail： lizh323@126.com

      LI Zhenhai

      12 *
    • Vol. 28, Issue 12, Pages: 3123-3135(2024)  

    • DOI：10.11834/jrs.20243080    

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