Improving the Selection of Vegetation Index Characteristic Wavelengths by Using the PROSPECT Model for Leaf Water Content Estimation

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