Abstract
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.
Highlights
Leaf water content (LWC) is a significant variable involved in physiological processes and drought stress of plants and is an influencing factor on short-term risk of fire [1,2,3,4,5].LWC variation is a significant factor in estimating plant growth status and in providing guidance for agricultural water management [6,7,8]
This study focused on the estimation of Equivalent water thickness (EWT) by using constructed new ratio VI (nRVI) based on selected characteristic wavelengths
The performance of nRVI and published vegetation indices (VIs) for EWT estimation based on the Gaussian process regression (GPR) model was analyzed and compared using five datasets
Summary
Leaf water content (LWC) is a significant variable involved in physiological processes and drought stress of plants and is an influencing factor on short-term risk of fire [1,2,3,4,5].LWC variation is a significant factor in estimating plant growth status and in providing guidance for agricultural water management [6,7,8].
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