Estimation of leaf water content from hyperspectral data of different plant species by using three new spectral absorption indices

The leaf equivalent water thickness (EWT, g cm −2 ) and fuel moisture content (FMC, %) are key variables in ecological and environmental monitoring. Although a variety of hyperspectral vegetation indices have been developed to estimate the leaf EWT and FMC, most of these indices are defined considered two or three specific bands for a specific plant species, which limits their applicability. In this study, we proposed three new spectral absorption indices (SAI 970 , SAI 1200 , and SAI 1660 ) for various plant types by considering the symmetry of the spectral absorption at 970 nm, 1200 nm and 1660 nm and spectral heterogeneity of different leaves. The indices were calculated considering the absorption peak and shoulder bands of each leaf instead of the same specific bands for all leaves. A pooled dataset of three tree species (camphor (VX), capricorn (VJ), and red-leaf plum (VL)) was used to test the performance of the SAIs in terms of the leaf EWT and FMC estimation. The results indicated that, first, SAI 1200 was more suitable for estimating the EWT than FMC, whereas SAI 970 and SAI 1660 were more suitable for estimating the FMC. Second, SAI 1200 achieved the most accurate estimation of the EWT with a cross-validation coefficient of determination ( R cv 2 ) of 0.845 and relative cross-validation root mean square error ( rRMSE cv ) of 8.90%. Third, SAI 1660 outperformed the other indices in estimating the FMC at the leaf level, with an R cv 2 of 0.637 and rRMSE cv of 8.56%. Fourth, SAI 970 achieved a moderate accuracy in estimating the EWT ( R cv 2 of 0.25 and rRMSE cv of 19.68%) and FMC ( R cv 2 of 0.275 and rRMSE cv of 12.10%) at the leaf level. These results can enrich the application of the SAIs and demonstrate the potential of using SAI 1200 to determine the leaf EWT and SAI 1660 to obtain the leaf FMC among various plant types.