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    • Forest above-ground biomass inversion using full-polarimetric SAR data with integrated effective degree of polarization

    • “The fully polarized synthetic aperture radar (SAR) data can effectively characterize the vertical structural characteristics of forest canopy and has important application value in aboveground biomass (AGB) inversion of forests. A reasonable polarization decomposition method is an important support for extracting key polarization features and constructing high-precision quantitative inversion models. The research progress in forest biomass monitoring is introduced. The research team has established an improved model free polarization decomposition system that integrates effective polarization degrees, providing a solution to alleviate the saturation effect of forest AGB remote sensing inversion and improve the accuracy of complex forest area inversion.”
      • role:First author第一作者
      • Affiliation:

        Hunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Central South University of Forestry and Technology, Changsha 410004, China

        Hunan Engineering Research Center for Monitoring of Natural Ecosystem Carbon Sinks, Changsha 410004, China

      • Email:zijiahuang310@gmail.com
      • Introduction:黄子加,研究方向为植被定量遥感。E-mail: zijiahuang310@gmail.com

      HUANG Zijia

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

        Hunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Central South University of Forestry and Technology, Changsha 410004, China

        Hunan Engineering Research Center for Monitoring of Natural Ecosystem Carbon Sinks, Changsha 410004, China

      • Email:longjiangping@csuft.edu.cn
      • Introduction:龙江平,研究方向为林业遥感、智慧林业和微波遥感。E-mail: longjiangping@csuft.edu.cn

      LONG Jiangping

      12 * ,
      • Affiliation:

        Shandong GEO-Surveying & Mapping Institute, Ji'nan 250003, China

      ZHAO Deliang

      3,
      • Affiliation:

        Hunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Central South University of Forestry and Technology, Changsha 410004, China

        Hunan Engineering Research Center for Monitoring of Natural Ecosystem Carbon Sinks, Changsha 410004, China

      LIN Hui

      12,
      • Affiliation:

        Hunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Central South University of Forestry and Technology, Changsha 410004, China

        Hunan Engineering Research Center for Monitoring of Natural Ecosystem Carbon Sinks, Changsha 410004, China

      SUN Hua

      12,
      • Affiliation:

        Hunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Central South University of Forestry and Technology, Changsha 410004, China

        Hunan Engineering Research Center for Monitoring of Natural Ecosystem Carbon Sinks, Changsha 410004, China

      ZHANG Tingchen

      12
    • Vol. 30, Issue 7, Pages: 2104-2120(2026)  
    • DOI:10.11834/jrs.20265452    

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