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Leaf water potential (LWP) is an important indicator of plant water status. However, its determination via classical pressure-chamber measurements is tedious and time-consuming. Moreover, such methods cannot easily account for rapid changes in this parameter arising from changes in environmental conditions. Spectrometric measurements, by contrast, have the potential for fast and non-destructive measurements of plant water status, but are not unproblematic. Spectral characteristics of plants vary across plant development stages and are also influenced by environmental factors. Thus, it remains unclear whether changes in leaf water potential per se can reliably be detected spectrometrically or whether such measurements also reflect autocorrelated changes in the leaf water content (LWC) or the aerial plant biomass. We tested the accuracy of spectrometric measurements in this context under controlled climate chamber conditions in series of six experiments that minimised perturbing influences but allowed for significant changes in the LWP. Short-term exposure of dense stands of plants to increasing or decreasing artificial light intensities in a growth chamber more markedly decreased LWP than LWC in both wheat and maize. Significant relationships (R2-values 0.74–0.92) between LWP and new spectral indices ((R940/R960)/NDVI; R940/R960) were detected with or without significant changes in LWC of both crop species. The exact relationships found, however, were influenced strongly by the date of measurement or water stress induced. Thus, global spectral relationships measuring LWP probably cannot be established across plant development stages. Even so, spectrometric measurements supplemented by a reduced calibration dataset from pressure chamber measurements might still prove to be a fast and accurate method for screening large numbers of diverse lines.

Under sustainable development conditions, the water quality of irrigation systems is a complex issue which involves the combined effects of several surface water management parameters. Therefore, this work aims to enhance the surface water quality assessment and geochemical controlling mechanisms and to assess the validation of surface water networks for irrigation using six Water Quality Indices (WQIs) supported by multivariate modelling techniques, such as Principal Component Regression (PCR), Support Vector Machine Regression (SVMR) and Stepwise Multiple Linear Regression (SMLR). A total of 110 surface water samples from a network of surface water cannels during the summers of 2018 and 2019 were collected for this research and standard analytical techniques were used to measure 21 physical and chemical parameters. The physicochemical properties revealed that the major ions concentrations were reported in the following order: Ca2+ > Na+ > Mg2+ > K+ and alkalinity > SO42− > Cl− > NO3− > F−. The trace elements concentrations were reported in the following order: Fe > Mn > B > Cr > Pb > Ni > Cu > Zn > Cd. The surface water belongs to the Ca2+-Mg2+-HCO3− and Ca2+-Mg2+-Cl−-SO42− water types, under a stress of silicate weathering and reverse ion exchange process. The computation of WQI values across two years revealed that 82% of samples represent a high class and the remaining 18% constitute a medium class of water quality for irrigation use with respect to the Irrigation Water Quality (IWQ) value, while the Sodium Percentage (Na%) values across two years indicated that 96% of samples fell into in a healthy class and 4% fell into in a permissible class for irrigation. In addition, the Sodium Absorption Ratio (SAR), Permeability Index (PI), Kelley Index (KI) and Residual Sodium Carbonate (RSC) values revealed that all surface water samples were appropriate for irrigation use. The PCR and SVMR indicated accurate and robust models that predict the six WQIs in both datasets of the calibration (Cal.) and validation (Val.), with R2 values varying from 0.48 to 0.99. The SMLR presented estimated the six WQIs well, with an R2 value that ranged from 0.66 to 0.99. In conclusion, WQIs and multivariate statistical analyses are effective and applicable for assessing the surface water quality. The PCR, SVMR and SMLR models provided robust and reliable estimates of the different indices and showed the highest R2 and the highest slopes values close to 1.00, as well as minimum values of RMSE in all models.

Hyperspectral sensing offers a quick and non-destructive alternative for assessing phenotypic parameters of plant physiological status and salt stress tolerance. This study compares the performance of published and modified spectral reflectance indices (SRIs) for estimating and predicting the growth and photosynthetic efficiency of two wheat cultivars exposed to three salinity levels (control, 6.0, and 12.0 dS m−1). Results show that individual SRIs based on visible- and near-infrared (VIS/VIS, NIR/VIS, and NIR/NIR) estimate and predict measured parameters considerably more efficiently than those based on shortwave-infrared (SWIR/VIS and SWIR/NIR), with the exception of some modified indices (the water balance index (WABI-1(1550, 482), WABI-2(1640, 482), and WABI-3(1650, 531)), normalized difference moisture index (NDMI(1660, 1742)), and dry matter content index (DMCI(1550, 2305)), which show moderate to strong relationships with measured parameters. Overall results indicate that modified SRIs can serve as rapid and non-destructive high-throughput alternative approaches for tracking growth and photosynthetic efficiency of wheat under salt stress field conditions.

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