###### Abstract  
An area of growing interest in wheat-breeding programs for abiotic stresses is the accurate and expeditious phenotyping of large genotype collections using nondestructive hyperspectral sensing tools. The main goal of this study was to use data from canopy spectral signatures (CSS) in the full-spectrum range (400-2500 nm) to estimate and predict the plant biomass dry weight at booting (BDW-BT) and anthesis (BDW-AN) growth stages, and biological yield (BY) of 64 spring wheat germplasms exposed to 150 mM NaCl using 13 spectral reflectance indices (SRIs, consisting of seven vegetation-related SRIs and six water-related SRIs) and partial least squares regression (PLSR). SRI and PLSR performance in estimating plant traits was evaluated during two years at BT, AN, and early milk grain (EMG) growth stages. Results showed significant genotypic differences between the three traits and SRIs, with highly significant two-way and three-way interactions between genotypes, years, and growth stages for all SRIs. Genotypic differences in CSS and the relationships between the three traits and a single wavelength over the full-spectrum range depended on the growth stage. Water-related SRIs were more strongly correlated with the three traits compared with vegetation-related SRIs at the BT stage; the opposite was found at the EMG stage. Both types of SRIs exhibited comparable associations with the three traits at the AN stage. Principal component analysis indicated that it is possible to assess plant biomass variations at an early stage (BT) through published and modified SRIs. SRIs coupled with PLSR models at the BT stage exhibited good prediction capacity of BDW-BT (57%), BDW-AN (82%), and BY (55%). Overall, results demonstrated that the integration of SRIs and multivariate models may present a feasible tool for plant breeders to increase the efficiency of the evaluation process and to improve the genetics for salt tolerance in wheat-breeding programs. biological yield; booting stage; partial least squares regression; plant dry weight; principal component analysis; vegetation-related SRIs; water-related SRIs +
Footnote †: journal: Applied sciences  
(765.76 million tons), respectively [1]. It occupies a central place in human nutrition, providing approximately 18% of the global human intake of food calories and 20% of the daily protein intake [1]. Furthermore, approximately 80 million farmers in low-income countries rely on wheat for their livelihoods [2]. Most importantly, by the year 2050, the current global wheat production will need to increase by up to 60% (a 1.7% per-year increase) to meet the projected demand from the rising population, which will number over 9 billion [3]. Unfortunately, wheat production is constrained by a number of environmental abiotic stresses, especially salinity and drought, which are the major constraints to crop production in arid and semiarid regions. Salinity, in particular, influences more than 6% of the world's total land area, including 30% of irrigated land and 20% of arable land. Additionally, approximately 1.5 Mha of arable land is degraded annually by salt accumulation, with the main cause being the improper application of several agricultural activities including intensive agriculture and heavy application of fertilizers [4; 5; 6]. If the degradation of arable land continues at this rate, soil salinization will lead to the degradation of an estimated 50% of the world's agricultural land by 2050 [7]. Furthermore, salinization of agricultural soil causes approximately 50% of potential yield losses and up to $27.5 billion of economic losses annually [8]. Therefore, recovering the agricultural potential of degraded saline soils is vitally important to ensure global food security and improve farmers' livelihoods in arid and semiarid regions.