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    • A remote sensing method for plot-level rice distribution extraction based on visual foundation models and knowledge fusion

    • “The research team proposed an optimization method for land segmentation prompt information that combines visual basic models with knowledge fusion. The rice phenological features and spectral indices are transformed into dynamic prompt information required by the visual basic model through adaptive iterative learning, effectively solving the problem of high sample dependence in traditional deep learning methods. This provides a new solution to the bottlenecks of high cost and insufficient generalization in remote sensing crop mapping.”
      • role：First author第一作者
      • Affiliation：

        State Key Laboratory of Efficient Utilization of Arable Land in China/Key Laboratory of Agricultural Remote Sensing (AGRIRS) Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

      • Email：peng_zimeng@163.com
      • Introduction：彭子萌，研究方向为农业遥感监测。E-mail： peng_zimeng@163.com

      PENG Zimeng

      1,
      • role：Corresponding author通信作者
      • Affiliation：

        State Key Laboratory of Efficient Utilization of Arable Land in China/Key Laboratory of Agricultural Remote Sensing (AGRIRS) Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

      • Email：duanyulin@caas.cn
      • Introduction：段玉林，研究方向为智慧农业。E-mail： duanyulin@caas.cn

      DUAN Yuling

      1 * ,
      • Affiliation：

        State Key Laboratory of Efficient Utilization of Arable Land in China/Key Laboratory of Agricultural Remote Sensing (AGRIRS) Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

      YU Qiangyi

      1,
      • Affiliation：

        State Key Laboratory of Efficient Utilization of Arable Land in China/Key Laboratory of Agricultural Remote Sensing (AGRIRS) Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

      WU Wenbin

      1,
      • Affiliation：

        Department of Farmland Construction and Management, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Beijing 100125, China

      ZHANG Shuai

      2,
      • Affiliation：

        State Key Laboratory of Efficient Utilization of Arable Land in China/Key Laboratory of Agricultural Remote Sensing (AGRIRS) Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

      ZHAO Chunlei

      1,
      • Affiliation：

        Smart Tower Corporation Limited, Beijing 100089, China

      LI Boliang

      3,
      • Affiliation：

        Smart Tower Corporation Limited, Beijing 100089, China

      ZHANG Xin

      3
    • Vol. 30, Issue 8, Pages: 2486-2503(2026)  

    • DOI：10.11834/jrs.20265396    

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Zhang Xin 铁塔智联技术有限公司
Wu Wenbin 中国农业科学院农业资源与农业区划研究所/北方干旱半干旱耕地高效利用全国重点实验室/农业农村部农业遥感重点实验室
YU Qiangyi 中国农业科学院农业资源与农业区划研究所/北方干旱半干旱耕地高效利用全国重点实验室/农业农村部农业遥感重点实验室
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PENG Zimeng 中国农业科学院农业资源与农业区划研究所/北方干旱半干旱耕地高效利用全国重点实验室/农业农村部农业遥感重点实验室
Li Boliang 铁塔智联技术有限公司
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Zhang Shuai 农业农村部农田建设管理司

Related Institution

Department of Farmland Construction and Management, Ministry of Agriculture and Rural Affairs of the People's Republic of China
State Key Laboratory of Efficient Utilization of Arable Land in China, Key Laboratory of Agricultural Remote Sensing (AGRIRS) Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University
School of Land Engineering, Chang’an University
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences