Equivalent water thickness (EWT) is a major indicator for indirect monitoring of leaf water content in remote sensing. Many vegetation indices (VIs) have been proposed to estimate EWT based on passive or active reflectance spectra. However, the selection of the characteristics wavelengths of VIs is mainly based on statistical analysis for specific vegetation species. In this study, a characteristic wavelength selection algorithm based on the PROSPECT-5 model was proposed to obtain characteristic wavelengths of leaf biochemical parameters (leaf structure parameter (N), chlorophyll a + b content (Cab), carotenoid content (Car), EWT, and dry matter content (LMA)). The effect of combined characteristic wavelengths of EWT and different biochemical parameters on the accuracy of EWT estimation is discussed. Results demonstrate that the characteristic wavelengths of leaf structure parameter N exhibited the greatest influence on EWT estimation. Then, two optimal characteristics wavelengths (1089 and 1398 nm) are selected to build a new ratio VI (nRVI = R1089/R1398) for EWT estimation. Subsequently, the performance of the built nRVI and four optimal published VIs for EWT estimation are discussed by using two simulation datasets and three in situ datasets. Results demonstrated that the built nRVI exhibited better performance (R² = 0.9284, 0.8938, 0.7766, and RMSE = 0.0013 cm, 0.0022 cm, 0.0030 cm for ANGERS, Leaf Optical Properties Experiment (LOPEX), and JR datasets, respectively.) than that the published VIs for EWT estimation. It is demonstrated that the built nRVI based on the characteristic wavelengths selected using the physical model exhibits desirable universality and stability in EWT estimation.

中文翻译：

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利用PROSPECT模型进行叶片含水量估算改善植被指数特征波长的选择。

当量水厚度（EWT）是间接监测遥感中叶片含水量的主要指标。已经提出了许多植被指数（VIs）来基于被动或主动反射光谱来估计EWT。但是，VI的特征波长的选择主要基于对特定植被物种的统计分析。本研究提出了一种基于PROSPECT-5模型的特征波长选择算法来获得叶片生化参数（叶片结构参数（N），叶绿素a + b）的特征波长含量（Cab），类胡萝卜素含量（Car），EWT和干物质含量（LMA））。讨论了EWT的特征波长和不同生化参数的组合对EWT估计精度的影响。结果表明，叶片结构参数N的特征波长对EWT估计影响最大。然后，选择两个最佳特征波长（1089和1398 nm）以建立新的比率VI（nRVI = R1089 / R1398）用于EWT估算。随后，通过使用两个模拟数据集和三个原位数据集，讨论了构建的nRVI和四个用于EWT估计的最佳已发布VI的性能。结果表明，构建的nRVI表现出更好的性能（R ²分别为ANGERS，叶片光学特性实验（LOPEX）和JR数据集= 0.9284、0.8938、0.7766和RMSE = 0.0013 cm，0.0022 cm，0.0030 cm。）结果表明，基于使用物理模型选择的特征波长构建的nRVI在EWT估计中展现出理想的通用性和稳定性。