Spectral indices for tracing leaf water status with hyperspectral reflectance data

Abstract

Plant water stress can be detected via remote sensing. The objective of the study was to determine which leaf water index is best for assessing leaf water content from the laboratory standpoint. This study investigated the relationship between equivalent water thicknesses (EWT), gravimetric water content (GWC), and plant water concentration in the 350- to 2500-nm reflectance spectral range. A total of 277 leaf samples taken from ten different plants were used as calibration dataset, and 605 leaves from different plants, including LOPEX93 and ANGERS database, were used for validation. Three specific indices were analyzed: simple ratio, normalized ratio, and double difference (Datt type of index). A regression approach based on the iteration method at 5-nm interval was used for model calibration. Three bands index was found the most suitable and was validated by 605 leaf samples: for the linear regression model, the index is ( R1910 − R1340 ) / ( R1910 − R1125 ) with R2 = 0.96 and root mean square error ( RMSE ) = 0.001 ( g / cm2 ) and, for nonlinear regression model the index is ( R1930 − R1425 ) / ( R1930 − R1360 ) with R2 = 0.95 and RMSE = 0.001 ( g/cm2 ) for EWT. The newly proposed indices take advantage of being able to eliminate additional noise created by the leaf surface, making them helpful for agricultural-related research.