A new spectral similarity water index for the estimation of leaf water content from hyperspectral data of leaves

The variability of leaf water content has considerable significance for plant–environment interactions, ecosystem functioning and crop growth. This paper describes a methodology used to create spectral similarity water indices (SWIs) to accurately retrieve leaf water content based on the similarity between the leaf reflectance spectra and the specific water absorption spectrum. The abilities of six common distance metrics to capture spectral features were tested using two in situ datasets (LOPEX93 and PANAMA) and one simulation dataset (PROSPECT). These three datasets were also used to determine the most appropriate spectral intervals and to verify the performance of SWIs against six frequently used spectral indices that were specifically designed to estimate vegetation water content. Our results demonstrate that the Spectral Angle Cosine (SAC) is the most effective metric to capture useful spectral information pertinent to the equivalent water thickness (EWT), and three spectral intervals (970–1150, 1330–1350 and 1584–1760nm) are suitable for the retrieval of leaf water content. The SWIs were then created based on the SAC distances in these three spectral intervals respectively. The results demonstrate that SWISAC indices are better indicators of leaf water content and more tolerant to species differences than the six spectral indices, including the Shortwave Angle Normalized Index (SANI), Shortwave Angle Slope Index (SASI), Moisture Stress Index (MSI), Normalized Difference Infrared Index (NDII), Normalized Difference Water Index (NDWI) and Maximum Difference Water Index (MDWI). In addition, the most accurate estimates of EWT were achieved from a single SAC distance with nRMSE of 4.08% (R¯2=0.98), 3.63% (R¯2=0.95) and 8.11% (R¯2=0.80) for PROSPECT, LOPEX93 and PANAMA, respectively. Models that combine two SAC distances from the near-infrared and shortwave infrared regions produce an even better overall performance. Spectral similarity metrics may be a new effective tool to capture useful spectral information pertinent to leaf biochemical components, not only EWT but also other components such as chlorophyll and nitrogen content and they have potential to be adapted to canopy level observations.