Combining Hydrogeochemical Characterization and a Hyperspectral Reflectance Tool for Assessing Quality and Suitability of Two Groundwater Resources for Irrigation in Egypt

Abstract

Hyperspectral reflectance sensing provides a rapid and cost-effective technique for assessing the suitability of groundwater for irrigation by monitoring real-time changes in its quality at a large scale. In this study, we assessed the quality of 15 groundwater samples from El Fayoum depression in the Western Desert (WD) and 25 groundwater samples from the Central Nile Delta (CND) in Egypt using a traditional approach of the physiochemical parameters, irrigation water quality indicators (IWQIs), and hydrochemical facies. The spectral reflectance data of the water surface was used to build new simple reflectance indices (SRIs), and the performance of these indices for assessing IWQIs was compared with those by partial least square regression (PLSR) that was based on all SRIs or the full-spectrum ranges. Generally, the groundwater of the CND was fresher and more suitable for irrigation purposes than those of the WD. Based on the six IWQIs, ~6.7–60.0% and 85.0–100.0% of the groundwater samples of the WD and CND, respectively, were categorized as highly suitable for irrigation purposes. Based on hydrochemical facies, Na-Cl and Ca-HCO3 were dominant in the WD and CND, respectively, as well as the alkali earth metals (Na+ + K+), which significantly exceeded the alkaline earth metals (Ca2+ + Mg2+) in the WD, with the reverse for the CND. Most developed SRIs had a moderate, weak, and moderate to strong relationship with physiochemical parameters and IWQIs in the WD, CND, and across both regions, respectively. The PLSR models based on all SRIs provided a more accurate estimation of IWQIs in calibration and validation datasets than those based on full-spectrum ranges, and both PLSR models provided better estimation than the individual SRIs. These findings support the feasibility of using ground reflectance measurements as a fast and low-cost tool for the assessment and management of groundwater for irrigation in arid and semiarid regions.

Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

“…The concentrations of trace elements occurred in the following sequence: [66]. The low concentrations of trace elements indicated that there were no significant effects on the irrigation water suitability [48]. For example, El-Bana [75], El-Bana et al [76] and El Bouraie et al [7] studied the temporal variation of Al, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn in Nile River water in the northern Nile Delta and the effects of these elements on the water quality.…”

“…Over the past decade, several research initiatives have been aimed at creating and enhancing water quality prediction models [45][46][47]. Recently, machine learning and data-driven results have shown promising results in the development of precise water quality estimation models [48,49]. Therefore, multivariable statistical regression methods, including the PCR, SVMR and SMLR, were tested as alternative approaches for predicting WQIs for irrigation.…”

Using Optimized Two and Three-Band Spectral Indices and Multivariate Models to Assess Some Water Quality Indicators of Qaroun Lake in Egypt

“…The NSRIs-2b was calculated as a ratio spectral index according to Gad et al [55] as shown in Table 1 and as follows:…”

“…Although SRIs are a simple tool and several indices have been efficiently used in estimating WQIs, they are hindered by their use of just a few bands and are impacted by environmental conditions, timeliness, and regional specificity [55,81,86]. Furthermore, using several wavebands sensitive to water quality parameters through SRIs combined with PLSR models could enhance the performance of the models to predict the WQIs.…”

“…Moreover, the PLSR is mainly powerful in cases of spectral analysis when the number of predictor variables (i.e., wavelengths or SRIs) is massively greater than the number of observations (i.e., Chl-a). Multivariate integration methods such as PLSR have been proposed to resolve the strong multi-collinear and noisy variables in visible (VIS) and short-wave infrared (SWIR) spectrum data and to efficiently assess the water quality indicators [55]. In this regard, PLSR may offer a deep insight into the potential effectiveness of spectral un-mixing approaches.…”