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将PROSPECT与叶子结构的预先估计相结合,以改善从双向反射因子光谱中检索银杏的叶子含量

Kai Zhou¹, Saiting Qiu¹, Fuliang Cao¹

¹Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China.

Plant phenomics (Washington, D.C.)

|December 16, 2024

概括

本研究引入了一种使用修改比率指数和双向反射系数 (BRF) 谱的新方法,以准确估计银杏树的叶子含量 (LNC). 该方法通过减少混因素和增强吸收信号来改善LNC检索.

科学领域:

植物生理学植物生理学
遥感遥感遥感遥感
林业林业林业林业林业

背景情况:

叶子含量 (LNC) 对于评估森林树的健康至关重要.
使用叶子双向反射因子 (BRF) 光谱检索LNC是具有挑战性的,原因是混因素,如中粒细胞结构和含水量.
PROSPECT-PRO模型通常用于LNC检索,但在BRF频谱方面存在局限性.

研究的目的:

开发一种基于BRF光谱的先进方法,用于准确地估计银杏树和树苗的LNC.
为了减轻镜像反射效应,并增强叶子吸收信号在BRF光谱.
调查N_struct预估对LNC检索的影响.

主要方法:

提出了一种新的方法,使用3个修改后的比率指数 (mPrior_800,mPrior_1131,mPrior_1365) 预先估计N_结构参数.
使用不同的反转方法 (STANDARD,sPROCOSINE,PROSDM,PROCWT) 组合修改比率指数.
评估了修改比率指数与标准指数的性能,并评估了用于LNC估计的最佳波段.

主要成果:

之前使用修改比率指数的N_结构估计策略,特别是mPrior_1131和mPrior_1365,显著超过了标准方法.
通过使用PROCWT_S3与mPrior_1131或mPrior_1365.5相结合,实现了对LNC_面积 (NRMSE 12.94%-14.49%) 和LNC_质量 (NRMSE 10.11%-10.75%) 的最佳估计.
在特定的光谱域中确定了用于LNC估计的最佳波段 (例如,1440-1539 nm,1580-1639 nm).

结论:

新的BRF基于光谱的方法显示了改善银杏树LNC估计的巨大潜力.
使用修改比率指数预先估计N_结构对于提高LNC检索准确性至关重要.
这项研究促进了对森林生态系统中基于光谱的LNC评估的理解.

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