## 5 Conclusions  
The results of this study showed that the response plant biomass of a large number of wheat genotypes to salinity stress at different growth stages can be assessed using the rapid, nondestructive, and relatively inexpensive hyperspectral reflectance tool. Significant genotypic differences were observed in canopy spectral reflectance at the three regions of the spectrum (VIS, NIR, and SWIR) with a significant correlation between a single wavelength within these regions and the three plant traits related to biomass accumulation. Furthermore, the significant correlation between SRIs confirmed the potential of hyperspectral reflectance data as a simple and easy way to assess plant biomass for a large number of genotypes. However, the growth stage and type of SRIs played a key role in the capability of SRIs to accurately assess and monitor plant biomass. Water-based SRIs were much more effective than vegetation-related SRIs for assessing plant biomass at the BT stage; the opposite was found at the EMG stage. Both types of SRIs demonstrated consistently higher levels of association with plant biomass at the AN stage. Interestingly, by coupling SRIs with the PLSR model, an assessment of the plant biomass at the early stage (BT) was possible. Combining both methods resulted in a good prediction of BDW-BT (57%), BDW-AN (82%), and BY (55%) at the BT stage. Finally, our findings confirmed the importance of hyperspectral reflectance data and multivariate analysis in evaluating the salt tolerance of a large number of genotypes in wheat-breeding programs.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12041983/s1](https://www.mdpi.com/article/10.3390/app12041983/s1)]([https://www.mdpi.com/article/10.3390/app12041983/s1](https://www.mdpi.com/article/10.3390/app12041983/s1)), Figure S1. Summary statistical information of plant dry weight measured at booting (BDW-BT) and anthesis (BDW-AN) growth stages and biological yield (BY) across 64 wheat genotypes grown in 150 mM NaCl during first (S1) and second (S2) year.  
Conceptualization, S.E.-H., N.A.-S., Y.R. and E.T.; methodology, S.E.-H., M.M., N.A.-S., M.U.T., S.M. and Y.R.; software, S.E.-H., Y.R., S.M. and E.T.; validation, S.E.-H., Y.R., N.A.-S. and E.T.; formal analysis, S.E.-H., M.M., M.U.T. and E.T.; investigation, S.E.-H., Y.R. and N.A.-S.; resources, S.E.-H., Y.R., E.T. and M.M.; data curation, S.E.-H., M.M., M.U.T., N.A.-S., E.T. and Y.R.; writing--original draft preparation, S.E.-H.; writing--review and editing, S.E.-H.; visualization, S.E.-H., N.A.-S., Y.R. and E.T.; supervision, S.E.-H.; project administration, S.E.-H.; funding acquisition, S.E.-H. All authors have read and agreed to the published version of the manuscript.  
This project was funded by the National Plan for Science, Technology and innovation (MAARIFAH), King Abdul-Aziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (13-AGR2172-02).  
Not applicable.  
All data are presented within the article.  
The authors extend their appreciation to the National Plan for Science, Technology and innovation (MAARIFAH) at the King Abdul-Aziz City for Science and Technology, Saudi Arabia for funding this work through Award Project No. (13-AGR2172-02).