Abstract
Realistic simulations of saline field conditions and effective monitoring of phenotyping parameters in an expeditious, non-destructive manner are imperative to successful breeding of genotypes for salinity stress tolerance. This study aimed to spectrally assess the growth, water relations and ion contents of wheat under simulated saline field conditions using the subsurface water retention technique (SWRT) and three salinity water levels (control, 6, and 12 dS m(-1)). Phenotypic parameters and hyperspectral signatures of the canopy within the 350-2500 nm range were measured at the flowering stage. Multivariate analysis, including correlation, partial least squares regression, simultaneous b-coefficient and variable importance for projection (VIP), and stepwise multiple linear regression were used in the same order, to extract sensitive wavebands and effective singular wavelengths. Binary effective wavelengths as normalized spectral indices (NDSIs) were constructed and related to phenotypic parameters for pooled data and for each salinity level and cultivar. The results confirmed that the shoot dry weight (SDW), water relations and ion contents parameters were effective as screening criteria for evaluating the salt tolerance of wheat cultivars under simulated saline field conditions. It was possible to assess the phenotypic parameters by using hyperspectral canopy signatures over a broad spectrum range. All parameters exhibited stronger relationships with the wavelengths extracted in the visible-infrared (VIS) and red edge regions than those extracted in the near-infrared (NIR) and shortwave-infrared (SWIR) regions. Six wavelengths within the VIS region, five within the red edge and SWIR-1 regions, eleven within the NIR region, and nine within the SWIR-2 region were extracted as effective bands. The NDSIs based on VIS/VIS, red edge/red edge, red edge/VIS, NIR/VIS, NIR/red edge, and NIR/NIR were more appropriate for assessing the phenotypic parameters than indices based on SWIR/SWIR and SWIR/NIR, except for the SDW, K+ and Ca2+ contents, which showed strong correlations with the latter NDSIs. The close relationship between SDW and the water relations and ion contents parameters on one side and the high predictive power of the NDSIs based on the VIS, red edge, and NIR wavelengths in the assessment of phenotypic parameters on the other side indicates that the hyperspectral reflectance data and band selection techniques could be used for the indirect assessment of water relations and ion content of wheat under saline field conditions.