Monitoring Salinity Stress in Moringa and Pomegranate: Comparison of Different Proximal Remote Sensing Approaches

Abstract

Cultivating crops in the hot, arid conditions of the Arabian Peninsula often requires irrigation with brackish water, which exposes plants to salinity and heat stress. Timely, cost-effective monitoring of plant health can significantly enhance crop management. In this context, remote sensing techniques offer promising alternatives. This study evaluates several low-cost, ground-level remote sensing methods and compares them with benchmark analytical techniques for assessing salt stress in two economically important woody species, moringa and pomegranate. The species were irrigated under three salinity levels: low (2 dS m⁻¹), medium (5 dS m⁻¹), and high (10 dS m⁻¹). Remote sensing tools included RGB, multispectral, and thermal cameras mounted on selfie sticks for canopy imaging, as well as portable leaf pigment and chlorophyll fluorescence meters. Analytical benchmarks included sodium (Na) accumulation, carbon isotope composition (δ¹³C), and nitrogen (N) concentration in leaf dry matter. As salinity increased from low to medium, canopy temperatures, vegetation indices, and δ¹³C values rose. However, increasing salinity from medium to high levels led to a rise in Na accumulation without further significant changes in other remote sensing and analytical parameters. In moringa and across the three salinity levels, the Normalized Difference Red Edge (NDRE) and leaf chlorophyll content on an area basis showed significant correlations with δ¹³C (r = 0.758, p < 0.001; r = 0.423, p < 0.05) and N (r = 0.482, p < 0.01; r = 0.520, p < 0.01). In pomegranate, the Normalized Difference Vegetation Index (NDVI) and chlorophyll were strongly correlated with δ¹³C (r = 0.633, p < 0.01 and r = 0.767, p < 0.001) and N (r = 0.832, p < 0.001 and r = 0.770, p < 0.001). Remote sensing was particularly effective at detecting plant responses between low and medium salinity, with stronger correlations observed in pomegranate.

1. Introduction

Soil salinity is a significant environmental stressor that severely impacts crop productivity. Global estimates suggest that salt-affected soils cover between ~8.31 and 11.73 million square kilometers, depending on the methods used for estimation. These soils are found across all climate zones, but they are particularly widespread in drylands, where high evaporation rates surpass water input, leading to salt accumulation in the upper soil layers [1]. With the increasing global population and growing food demands, improving productivity on degraded, low-yielding soils is critical, especially as arable land and freshwater resources continue to decline [2].

The Gulf Cooperation Council (GCC) countries—Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the UAE—face significant challenges due to water scarcity, dwindling arable land, and poor soil quality. The region’s hyper-arid climate and the projected effects of climate change exacerbate these issues, raising concerns about food security. The GCC countries generally experience physical water scarcity, where water development has reached or exceeded sustainable limits [3]. As a result, these nations depend heavily on food imports, which creates vulnerabilities during geopolitical instability or periods of increased global food demand [4]. In the UAE, harsh climatic conditions, freshwater shortages, high soil salinity, and limited fertile land place immense pressure on agriculture [5]. Around 34% of the UAE’s land is affected by salinity, with coastal sabkha regions reaching salinity levels as high as 28.8 dS m⁻¹, and, in some areas of the Abu Dhabi Emirate, exceeding 200 dS m⁻¹ [6]. Rising demand for fruits and vegetables, fueled by economic growth and a growing expatriate population, has increased the country’s reliance on imports. In response, the UAE government has taken steps to improve domestic agricultural production, particularly for fruits and vegetables, to enhance food security. However, given the high salinity in both soil and water, the use of saline water in agriculture is unavoidable.

Salinity has a particularly detrimental effect on plant physiological processes, especially in fruit crops, which are highly sensitive to salt stress. This is compounded by the fact that many fruit crops are perennial, making them more vulnerable to long-term salt accumulation. Symptoms of salt stress are often observed quickly in annual crops, but they can take months or even years to manifest in perennial crops [7]. As salinity stress progresses, it creates two main challenges: osmotic stress, which resembles water stress, followed by ionic stress, leading to toxicity [8]. Ionic stress destabilizes cell membranes through lipid peroxidation and electrolyte leakage [9]. This oxidative stress triggered by salinity results in the production of reactive oxygen species, which can damage cell membranes, organelles, enzymes, and photosynthetic pigments [10]. Additionally, the accumulation of Na⁺ and Cl⁻ ions in leaf tissues hinders photosynthesis and reduces chlorophyll levels by breaking down cellular membranes and activating the chlorophyllase enzyme [11].

The scarcity of scientific literature on fruit tree cultivation in the GCC is likely due to limited data availability, underscoring the need for further research. For instance, studies on the responses of different fruit tree species to varying salinity levels in the UAE could provide valuable insights for optimizing fruit farming in the region. The current study evaluates locally available, high-value fruit tree varieties for their salinity tolerance potential under UAE climatic conditions. Moringa (Moringa oleifera Lam.), known as the horseradish tree, is a fast-growing, small- to medium-sized tree, ranging from 5 to 12 m in height [12]. It has diverse uses, including in pharmaceuticals, nutrition, biogas production, and as fertilizer [12,13,14]. Moringa is an underutilized crop with potential in both the food and pharmaceutical industries [15], and it is well-adapted to climate change challenges, including drought and moderate salinity tolerance [15,16]. However, young trees are sensitive to high salinity, which can limit growth and productivity, although salinity can enhance their medicinal potential by increasing antioxidant compounds [16]. Pomegranate (Punica granatum L.), a member of the Punicaceae family, is known for its high antioxidant content, particularly in the peel, making it a valuable crop in saline, water-scarce conditions [17,18]. Pomegranate tolerates saline irrigation up to an EC of 15 dS·m⁻¹ with minimal damage and slight growth reduction [19], and it is increasingly popular in arid and semi-arid regions [20].

To date, the available literature on the response of moringa and pomegranate to salinity stress remains relatively limited. Most studies have focused on the effects of salinity on secondary metabolite production due to the economic importance of these compounds [16] or on the use of biostimulants and biofertilizers to enhance the salinity tolerance of these species [21]. In contrast, there are far fewer studies aimed at evaluating the performance of remote sensing approaches for assessing plant responses to salinity. When such approaches are employed, they are typically limited to the single-leaf level, often measuring pigment content and sometimes chlorophyll fluorescence as indicators of stress [22,23,24,25]. One notable exception is a study that utilized a high-resolution spectroradiometer (400–2500 nm) equipped with a leaf probe and an internal light source to assess salinity response in pomegranate [26]. Nevertheless, this approach was still restricted to individual leaves and involved highly specialized equipment, which is costly (approximately USD 100,000) and technically complex, making it unsuitable for practical use by farmers. Alternatively, another study on pomegranate evaluated the performance of various vegetation indices at the canopy level and, in this case, using open access satellite images but with the objective of scheduling irrigation in the absence of salinity [27]. Furthermore, all previously mentioned studies addressing salinity responses were conducted on potted plants—either in open-air conditions or within greenhouses or net houses—rather than in natural field settings. In contrast, the present study offers a more comprehensive perspective by including both moringa and pomegranate trees exposed to three different salinity levels under full field conditions. This research was conducted as part of a long-term trial at the headquarters of the International Center for Biosaline Agriculture (ICBA) in Dubai, UAE. It presents a robust evaluation of low-cost remote sensing techniques for monitoring salinity stress in arid environments. In this study, we compared low-cost remote sensing techniques for monitoring salinity stress under arid and hot environmental conditions. The traits evaluated included canopy-level measurements, such as temperature and vegetation indices, as well as leaf-level assessments of pigment content and chlorophyll fluorescence. These remote sensing indicators were benchmarked against established analytical measures of plant salinity tolerance, including carbon and nitrogen isotope composition and mineral concentrations [28,29]. Additionally, changes in leaf anatomy and chloroplast ultrastructure in response to salinity stress were examined to provide a comprehensive understanding of physiological responses.

2. Materials and Methods

2.1. Plant Material and Growing Conditions

The study was conducted at the Experimental Fields of ICBA in Dubai, United Arab Emirates (25°13′N, 55°17′E). The site is characterized by an arid desert climate, with high temperatures and minimal rainfall from April to November [30]. The soil is a carbonatic, hyperthermic Typic Torripsamment, with negligible inherent salinity (0.2 dS m⁻¹). The species studied included moringa (Moringa oleifera Lam.) and pomegranate (Punica granatum L.).

The experiment was established in winter 2019. Before planting, the site was prepared by incorporating organic compost at a rate of 3 kg per tree. One-year-old, uniform, disease-free seedlings of similar growth, vigor, and size were planted in a randomized complete block design with three salinity treatments: S1 (2 dS m⁻¹), S2 (5 dS m⁻¹), and S3 (10 dS m⁻¹). Each treatment had three replicates, and each replicate consisted of three trees, spaced 6 × 6 m apart, totaling 27 trees per fruit species across all treatments. Drip irrigation was used, with water volumes adjusted to meet the experimental requirements based on the UAE climate. Fertilization followed recommended guidelines, and periodic pruning was performed to enhance tree growth and overall health.

The experimental plots were irrigated with water sourced from two reservoirs, supplying high- and low-salinity water. A mixing unit adjusted the water’s salinity to the target levels before distribution through the drip system. Medium and high salinity levels (5 and 10 dS m⁻¹) were achieved by blending saline groundwater (ECw up to 25 dS m⁻¹, SAR > 26 mmol/L) with freshwater [31]. The sodium (Na), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in the supplied irrigation water (fresh and saline water) are reported in Table S1. Salinity levels were monitored biweekly using a portable EC meter. The composition of the manure, as well as the fresh and saline water irrigation used in this study, are detailed elsewhere [32]. Remote sensing measurements and additional sampling were conducted during the middle of the day (between 10:50 a.m. and 1:00 p.m., GMT + 4) on 21 and 22 March 2021 (link to photo documentation: https://photos.app.goo.gl/AJQaYZwPe9CssC5JA, accessed on 10 June 2025). For each tree, three fully expanded, mature leaves of similar age were selected from the mid-canopy, oriented toward the southwest, to represent the overall crown condition. This leaf selection strategy ensured consistency and minimized variability, particularly as the trees were uniformly subjected to both heat and salinity stress—factors known to affect the entire canopy. No signs of nutrient deficiency or pest infestation were observed during the study, reducing potential variability among leaves. Senescent leaves were deliberately excluded from sampling. The timing of the measurements was chosen to coincide with a transitional seasonal window in the UAE. Middle to late March marks the shift from the optimal growing period of winter and early spring to the onset of the extreme heat of late spring and summer, during which daily temperatures can approach 50 °C. Leaf samples were collected from the 4th and 5th fully expanded leaves of pomegranate and the 2nd pinnate leaves of moringa. These samples were then oven-dried at 70 ± 2 °C until they reached a constant weight for dry matter determination. Afterward, the dried leaves were finely milled for further analysis.

2.2. Leaf Pigments and Chlorophyll Fluorescence

Leaf chlorophyll, anthocyanins, and flavonoids were measured using a portable handheld leaf sensor (Dualex, Force-A, Orsay, France). Simultaneously, the Nitrogen Balance Index (NBI), which relates carbon and nitrogen allocation [33], was calculated as the ratio of chlorophyll to flavonoids. The content of various leaf pigments was assessed using a Dualex leaf-clip sensor (Force-A, Orsay, France), which operates with a red reference beam at 650 nm and UV light at 375 nm [33]. This sensor measures chlorophylls a + b (Chl), flavonoids (Flav), and anthocyanins (Anth), and it also calculates the NBI, which is the ratio of Chl/Flav and reflects nitrogen and carbon allocation [33]. The measured area was a circle with a 5 mm diameter. For each tree, at least three different leaves were assessed, with five measurements taken per leaf. The measurements were taken from the middle portion of the leaf. Chlorophylls a + b (Chl): (NIRtrans − Redtrans)/Red trans. Flavonoids (Flav): log (NIR fluor excited red)/(NIR fluor excited UV − A). With a measurement area of 19.6 mm², the Dualex sensor offers a larger sampling area compared to other handheld leaf meters (https://metos.global/wp-content/uploads/2022/07/Dualex_leaflet-ENG.pdf; accessed on 11 June 2025). Steady-state leaf chlorophyll fluorescence (Ft) was measured on the same leaves using a FluorPen FP100 handheld device (Photon Systems Instruments, Drasov, Czech Republic). Ft represents the continuous fluorescence yield in non-actinic light, which is equivalent to F0 when the leaf sample is dark-adapted. This parameter has been positively correlated with yield under dry, hot conditions [34].

2.3. Multispectral and RGB Vegetation Indices

Remote sensing measurements were taken at ground level due to restrictions on UAV use in areas near airports (in this case, the proximity to Dubai Airport). Multispectral indices were calculated from images captured zenithally using an Airinov Multispec4C camera (ARINOV, Paris, France). This camera features four 1.2 megapixel CMOS imaging sensors, each equipped with separate bandwidth filters covering the visible, red edge, and near-infrared wavelengths. The multispectral sensor also includes a custom grayscale ground calibration panel, a global incident light sensor, and a stand-alone GPS unit. Data were captured every 2 s, with the camera mounted zenithally at the tip of a five-meter pole.

To minimize background interference (from soil and woody vegetation), a soil mask derived from an NDVI threshold of <0.4 was applied to isolate pixels corresponding to the tree canopy leaves [35]. This mask was applied for all multispectral vegetation indices, except for NDVI. The following multispectral vegetation indices were calculated using the camera, with image processing and vegetation index averaging performed using MosaicTool software (https://gitlab.com/sckefauver/MosaicTool, version 16) and custom FIJI (https://imagej.net/software/fiji/, ImageJ version 1.54f) code. These methods ensure that each image captures the same ground area and calculates the “_veg” values, representing only the vegetation pixels, as described elsewhere [36,37,38,39]:

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Normalized Difference Vegetation Index (NDVI): (NDVI): (R780 − R670)/(R780 + R670). NDVI is the most widely used metric for quantifying vegetation health and amount. It is sensitive to both biomass and chlorophyll content and calculated from the red and near-infrared bands, making it applicable across various sensor platforms, from satellites to portable leaf meters [40]

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Soil Adjusted Vegetation Index (SAVI): (800 nm – 670 nm)/(800 nm + 670 nm + L) (1 + L), L = 0.5. SAVI corrects for soil brightness and is especially useful in areas with sparse vegetation. It includes a soil brightness correction factor (L) to reduce soil influence. The parameter L, which accounts for the canopy background adjustment, is a function of vegetation density and typically takes a value of 0.5 for intermediate vegetation cover [41]. SAVI is often preferred over NDVI when soil exposure is significant [41], which was the case in our study.

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Optimized Soil Adjusted Vegetation Index (OSAVI): (1 + Y) (800 nm – 670 nm/800 nm + 670 nm + Y), Y = 0.16. OSAVI is based on SAVI and uses a standard background adjustment factor (Y = 0.16). It is most effective in areas with sparse vegetation where soil is visible through the canopy [42].

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Renormalized Difference Vegetation Index (RDVI): 800 nm − 670 nm)/(√800 nm + 670 nm). RDVI is a variant of NDVI designed to better identify healthy vegetation while being less sensitive to soil and sun geometry effects [43].

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Normalized Difference Red Edge (NDRE): (NIR − Red-edge)/(NIR + Red-edge). NDRE is used to assess chlorophyll content and plant health, detecting crop stress through reduced chlorophyll levels [44].

In addition to the multispectral indices, RGB vegetation indices were derived from conventional digital images [45,46] using MosaicTool software (https://gitlab.com/sckefauver/MosaicTool, version 16) to calculate BreedPix indices based on Hue, Saturation, and Intensity (HSI) color space transformation. This transformation separates light intensity from color values [47]. The software ensured that each RGB image covered the same ground area as the multispectral images. The indices measured were the Green Area (GA), which includes all pixels within the 60° to 180° range of the hue wheel, and the Greener Area (GGA), a more restrictive index that only includes pixels within the 80° to 180° range. GA represents the overall green biomass in the image, while the GGA specifically highlights more yellowish-green pixels. Both indices were calculated from zenithally acquired RGB images. Additionally, side-acquired images were also collected, and GA was calculated for these, as well.

2.4. Canopy Temperature

Thermal images were captured zenithally using an FLIR Duo R camera (Teledyne FLIR LLC, Wilsonville, OR, USA) mounted 5 m atop a pole (Figure 1). This camera combines a high-resolution radiometric uncooled VOx microbolometer thermal imaging sensor with 160 × 120 pixel resolution and a co-aligned RGB color camera with 1920 × 1080 pixel resolution. Thermal images were also acquired from the canopy’s side profile using a CatS60 smartphone (Caterpillar Inc.; Bullitt Group Ltd., Reading, UK) equipped with a full suite of integrated sensors. The Cat S60 was the first smartphone to feature an integrated thermal imaging camera from FLIR, using the FLIR Lepton sensor with an 8–14 µm spectral range and a thermal resolution of 80 × 60 pixels (https://en.wikipedia.org/wiki/Cat_S60). Both the FLIR Duo R and the Cat S60 combine radiometric thermography with co-aligned RGB cameras using MSX imaging technology. This technology produces detailed thermal images by merging visual and thermal spectra, which helps extract temperature values from small leaves. Additionally, data fusion analyses were performed to extract temperatures exclusively from green pixels (Green Area, GA, as described below).

Temperature measurements were also taken using an infrared gun thermometer (Raytek Photo-Temp™ MXSTM TD infrared thermometer, Raytek Corporation, Santa Cruz, CA, USA). For side profile temperature measurements, the sun was always positioned opposite the measurement.

Each RGB + thermal fusion camera used its own RGB and co-aligned thermal infrared (TIR) camera, along with the FLIR RGB + TIR MSX product, to filter out non-vegetation pixel temperatures. The classification of GA pixels, resulting from the Cat S60 RGB camera analysis, was used to remove background interference from the Cat S60 thermal images. Similarly, the GA pixel threshold obtained from the zenithally acquired RGB images from the FLIR Duo R was applied to mask the thermal images captured by the FLIR Duo R’s TIR camera.

2.5. Stable Isotope Composition and Nitrogen Concentration

Dry leaf samples were finely ground using a ball mill (MM 400, Retsch GmbH, Haan, Germany). Approximately 0.7 to 0.8 mg of the powdered samples were then weighed into tin capsules for the measurement of total nitrogen (N) concentration and the stable carbon (¹³C/¹²C) and nitrogen (¹⁵N/¹⁴N) isotopic ratios. These measurements were performed using an elemental analyzer (Flash 1112 EA; Thermo-Finnigan, Bremen, Germany) coupled with an isotope ratio mass spectrometer (Delta V Advantage via Conflo IV interface IRMS, Thermo Fisher Scientific, Waltham, MA, USA) in continuous flow mode at the Scientific Facilities of the University of Barcelona. The ¹³C/¹²C ratios (R) of the plant material were expressed in delta notation (δ) as follows [48]: δ¹³C (‰) = [(R_sample/R_standard) − 1] × 1000, where the sample refers to the plant material and the standard is Pee Dee Belemnite (PDB) calcium carbonate. International secondary isotope standards with known ¹³C/¹²C ratios, including IAEA CH7 (polyethylene foil), IAEA CH6 (sucrose), and USGS 40 (L-glutamic acid), were used, with an analytical precision of 0.1‰. The same δ notation was applied to the ¹⁵N/¹⁴N ratio (δ¹⁵N), with the standard being N₂ in air. For nitrogen, international secondary isotope standards IAEA N1, IAEA N2, IAEA NO3, and USGS 40 were used, with a precision of 0.3‰.

2.6. Leaf Mineral Content

To determine the mineral content in the leaves, 100 mg of each leaf sample was weighed into Teflon^® beakers and digested in 2 mL of HNO₃ and 1 mL of H₂O₂ at 90 °C overnight. This process was carried out at the ionomics service of the Centro de Edafología y Biología Aplicada in Murcia, Spain. The same dry, milled material used for stable isotope analysis was used for the mineral content determination. After digestion, the samples were diluted in 30 mL of MilliQ water (18.2 Ω) and refrigerated until analysis.

The mineral contents of the digests, including iron (Fe), potassium (K), magnesium (Mg), sodium (Na), phosphorus (P), and calcium (Ca), were determined through Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES, Optima 8300, Perkin Elmer, Waltham, MA, USA). For every 11 samples, a procedural blank and an aliquot of certified reference materials were analyzed in the same manner. The reference materials used were BCR–60 (aquatic plant, Joint Research Centre—JRC, Brussels, Belgium), BCR-62 (olive leaves, JRC), and BCR–279 (sea lettuce, JRC). The digestion beakers were acid-washed and rinsed with Milli-Q water before use. The quality of the acids used was 69–70% Baker Instra-Analyzed Reagent 9598.34 (Thermo Fisher Scientific, Waltham, MA, USA) for HNO₃ and 30% Suprapur 1.07298.1000 (Merck, Darmstadt, Germany) for H₂O₂. Plastic tubes and caps were rinsed three times with the digest before being filled for analysis.

2.7. Leaf Anatomy and Ultrastructure

For each tree, one fully expanded leaf from the external part of the canopy was selected for anatomical observations. Leaves were sampled in the field for light microscopy imaging of semithin sections and then brought to the laboratory. A 2 mm² (2 mm × 1 mm) sample was cut from the center of the lamina using a scalpel while the leaf was kept covered with a fixative solution. The cuts were then submerged in pre-prepared glass vials containing the fixative solution, composed of 2.5% glutaraldehyde and 2% paraformaldehyde in 0.1 M cacodylate buffer. The fixative was changed until the sample became completely transparent, maintaining a sample-to-fixative ratio of approximately 1:10. The fragments were left in the fixative solution until they were transferred to the Electron Cryomicroscopy Unit, Scientific and Technological Centers, University of Barcelona (CCiTUB). At CCiTUB, the samples were gently washed with 0.1 M cacodylate buffer and then processed through the following steps: (1) cryoprotection with glycerol in 0.1 M cacodylate buffer at 4 °C; (2) cryofixation through immersion in liquid propane; (3) and freeze substitution in an EM AFS2 (Leica Microsystems, Vienna, Austria) with 2% osmium tetroxide (EMS, Hatfield, PA, USA) and 0.1% uranyl acetate (EMS, Hatfield, PA, USA) in acetone at −90 °C for 72 h, followed by warming to 4 °C at a 5 °C/h slope. The samples were subsequently (4) stored at 4 °C for 3 h and then transferred to room temperature and kept in the dark for 2 h, followed by gentle washing with acetone, and (5) resin embedded in Epon-812 (EMS, Hatfield, PA, USA) and polymerized at 60 °C for 48 h. (6) For light microscopy, 1 μm semi-thin cross-sections were cut using a UC6 ultramicrotome (Leica Microsystems, Vienna, Austria) with a glass knife, stained with methylene blue, and photographed using a light microscope (Olympus 175 CX41, Tokyo, Japan) coupled with a digital camera (Olympus DP70), and (7) ultrathin sections (60 nm thick) were obtained using the same ultramicrotome with a 45° diamond knife (Diatome, Biel, Switzerland) and a clearance angle of 6°. (8) Epon sections were mounted on formvar-coated 200-mesh copper grids and stained with 2% uranyl acetate and lead citrate, and (9) the acquired sections were observed in a Tecnai Spirit Electron Microscope (FEI, Eindhoven, The Netherlands) equipped with a tungsten cathode, and images were acquired at 120 kV using a 1376 × 1024 pixel Megaview III CCD camera (Thermo Fisher Scientific, Waltham, MA, USA).

2.8. Statistical Analyses

Data from all variables evaluated in this study were subjected to factorial analysis of variance (ANOVA) to assess the effects of salinity on both moringa and pomegranate. Mean differences across salinity treatments were analyzed using Tukey’s Honestly Significant Difference (HSD) test. To explore relationships between traits, bivariate Pearson correlation analyses were performed separately for each species, with results visualized using a heatmap. Additionally, a principal component analysis (PCA) was conducted using all of the variables studied (excluding GA-DUO and GA-CatS60) and combining all growing conditions. PCA was performed to examine the relationships between all measured analytical and remote sensing traits. Statistical analyses were conducted using IBM SPSS Statistics Version 25.0 (SPSS Inc., Chicago, IL, USA). The heatmap and PCA visualizations were generated using the Past (PAleontological STatistics) program.

3. Results

3.1. Water Status: Carbon Isotope Composition and Temperature

In moringa, salinity significantly increased the carbon isotope composition (δ¹³C). The most substantial increase was observed from low (2 dS m⁻¹) to medium (5 dS m⁻¹) salinity, with the trend continuing, but less markedly, at high (10 dS m⁻¹) salinity. The temperature of the canopy leaves (i.e., with the background removed based on the RGB vegetation index GA, measured with the same sensor: GA-DUO) when measured with a DUO FLIR camera placed zenithally on a 5 m pole (GA-DUO-TIR) increased notably from low to medium salinity and, to a lesser extent, from medium to high salinity (

Table 1. Effect of salinity on carbon isotope composition (δ¹³C), Green Area (GA), canopy temperature with the background removed based on GA (GA-TIR), and total canopy temperature including the background (Env-TIR). Measurements of canopy temperature were performed zenithally using a DUO-FLIR camera mounted on a 5 m pole and from the side using a CatS60 mobile phone and a thermal gun. Together with the masking backgrounds GA-DUO and GA-CatS60, the following temperature indices were measured: GA-DUO-TIR, Env-DUO-TIR, GA-CatS60-TIR, Env-CatS60-TIR, and gun temperature (gun-TIR) for moringa and pomegranate trees. The values shown are means ± SE for each species under three different salinity levels (2, 5, and 10 dS m⁻¹). Salinity means followed by different letters are significantly different (p < 0.05) according to Tukey’s Honestly Significant Difference (HSD) test. The associated sums of squares and probabilities are provided (ns = not significant; *** = p < 0.001; ** = p < 0.01; * = p < 0.05). Bold indicates significant results.

	2 dS m−1	5 dS m−1	10 dS m−1	Sum of Squares and Level of Significance
Moringa
δ13C (‰)	−29.37 a ± 0.26	−27.70 b ± 0.09	−27.13 b ± 0.18	24.52 ***
GA-CatS60	17.03 b ± 1.70	11.96 a ± 1.19	12.94 ab ± 1.32	130.10 *
GA-CatS60-TIR (°C)	22.21 a ± 1.24	23.79 a ± 1.18	23.66 a ± 2.53	13.71 ns
GA-DUO	20.25 a ± 3.15	24.10 a ± 7.13	20.32 a ± 5.36	87.47 ns
GA-DUO-TIR (°C)	41.18 a ± 0.45	43.49 ab ± 0.83	43.98 b ± 0.84	40.48 *
Env-CatS60-TIR (°C)	28.46 a ± 1.58	31.09 a ± 0.96	27.29 a ± 3.11	68.33 ns
Env-DUO-TIR (°C)	47.47 a ± 0.70	49.27 ab ± 0.79	50.47 b ± 0.68	40.93 *
Gun-TIR (°C)	33.96 a ± 1.03	37.55 b ± 0.74	36.24 ab ± 0.93	59.36 *
Pomegranate
δ13C (‰)	−29.28 a ± 0.12	−28.10 b ± 0.19	−28.66 ab ± 0.16	6.36 ***
GA-CatS60	7.52 a ± 1.35	5.48 a ± 0.84	10.22 a ± 2.46	73.12 ns
GA-CatS60-TIR (°C)	30.61a ± 0.68	33.67 b ± 0.58	33.63 b ± 1.19	50.99 **
GA-DUO	10.65 a ± 0.47	6.97 a ± 0.80	25.17 a ± 3.63	1108.56 ns
GA-DUO-TIR (°C)	40.34 a ± 0.34	42.14 a ± 0.36	45.30 b ± 1.75	78.98 ***
Env-CatS60-TIR (°C)	39.15 a ± 0.80	41.56 a ± 0.67	40.60 a ± 1.74	26.36 ns
Env-DUO-TIR (°C)	49.19 a ± 0.25	51.01 b ± 0.29	51.75 b ± 0.25	25.69 ***
Gun-TIR (°C)	38.23 a ± 0.46	38.61 a ± 0.45	38.90 a ± 0.47	1.53 ns

). The overall environmental temperature of the tree canopy, including both leaves and background (Env-DUO-TIR), measured with the same device was higher than that of the leaves alone and also increased with salinity. However, when the temperature was measured with the CatS60 mobile phone (GA-CatS60-TIR), with the background removed based on the GA, the temperature of the canopy leaves (GA-CatS60-TIR) and the total canopy (Env-CatS60-TIR), as measured from the side with the CatS60 thermal camera, did not exhibit significant increases as salinity increased. A trend was observed from low to medium salinity. Additionally, the temperature of the canopy leaves was lower than that of the whole canopy when measured with the CatS60. On the other hand, the side canopy temperature measured with an infrared thermometer (TIR-gun) also increased from control to medium salinity, but it did not show further increases at high salinity.

In pomegranate, δ¹³C significantly increased from low to medium salinity (Table 1). The temperature of the canopy leaves (i.e., with the background removed based on GA) increased progressively from low to high salinity when measured with the DUO-TIR (GA-DUO-TIR) camera placed zenithally. When measured with the CatS60 (GA-CatS60-TIR) from the side, the temperature increased from low to medium salinity. The overall temperature of the tree canopy (including both leaves and background) was higher than the canopy leaves, and it exhibited a slight, albeit significant, increase in response to high salinity when measured with the DUO camera (Overall-TIR-DUO). However, no significant differences were observed when measured with the CatS60 (Overall-TIR-CatS60) or with the infrared gun (gun-TIR). Additionally, temperature values were consistently higher when measured zenithally (GA-DUO-TIR) compared to side measurements (GA-CatS60-TIR or gun-TIR).

3.2. Photosynthetic and Nitrogen Metabolism: Leaf Pigments, N Indicators, and Chlorophyll Fluorescence

In moringa, chlorophyll content on an area basis increased from low to medium salinity, and the NBI followed a similar trend, both tending to decrease at high salinity (

Table 2. Effect of salinity on the chlorophyll, flavonoids, anthocyanins, nitrogen concentration (N), nitrogen isotope composition (δ¹⁵N), Nitrogen Balance Index (NBI), and steady-state leaf chlorophyll fluorescence (Ft) in moringa and pomegranate trees. The values shown are the means ± SE for each species under the three different levels of salinity (2, 5, and 10 dS m⁻¹). Salinity means followed by letters are significantly different (p < 0.05) according to Tukey’s Honestly Significant Difference (HSD) test. The associated sum of squares and probabilities (ns, not significant; *** p < 0.001; ** p < 0.01; * p < 0.05) are shown. Bold indicates significant results.

	2 dS m−1	5 dS m−1	10 dS m−1	Sum of Squares and Level of Significance
Moringa
Chlorophyll (µg cm−2)	18.48 a ± 1.84	25.96 b ± 1.15	22.86 ab ± 1.45	254.19 **
Flavonoids (relat. units)	1.98 a ± 0.04	1.99 a ± 0.02	1.96 a ± 0.02	0.01 ns
Anthocyanins (relat. units)	0.08 a ± 0.01	0.06 a ± 0.01	0.08 a ± 0.01	0.01 ns
N (%)	3.08 a ± 0.15	3.43 ab ± 0.07	3.73 b ± 0.09	1.87 ***
δ15N (‰)	3.73 a ± 0.84	4.06 a ± 0.58	3.00 a ± 0.20	5.36 ns
NBI	9.48 a ± 1.01	13.01 b ± 0.68	11.49 ab ± 0.71	56.30 **
Ft	3032.37 a ± 180.17	3421.18 a ± 132.18	3247.44 a ± 136.68	682,858.98 ns
Pomegranate
Chlorophyll (µg cm−2)	9.16 a ± 0.93	16.86 b ± 1.47	13.83 ab ± 1.28	269.62 ***
Flavonoid (relat. units)	2.04 a ± 0.03	2.02 a ± 0.03	1.93 a ± 0.05	0.04 ns
Anthocyanin (relat. units)	0.22 b ± 0.02	0.13 a ± 0.01	0.20 ab ± 0.04	0.04 *
N (%)	0.93 a ± 0.02	1.26 b ± 0.05	1.28 b ± 0.03	0.62 ***
δ15N (‰)	−0.03 a ± 0.52	3.91 b ± 0.72	2.18 ab ± 0.05	70.24 ***
NBI	4.44 a ± 0.44	8.57 b ± 0.67	6.56 ab ± 0.87	76.52 ***
Ft	2906.70 a ± 242.98	3072.62 a ± 168.46	2966.26 a ± 187.70	126,033.98 ns

). Total nitrogen concentration (N) showed a more progressive increase from low to high salinity. In contrast, the contents of anthocyanins and flavonoids, the nitrogen isotope composition (δ¹⁵N), and the Ft were not significantly affected by saline irrigation.

Pomegranate exhibited a pattern similar to moringa with respect to chlorophyll content and NBI, increasing from low to medium salinity and decreasing at high salinity (Table 2). Nitrogen concentration increased progressively across salinity levels. While the anthocyanin content and the Ft were unaffected by saline irrigation, flavonoid content decreased, and δ¹⁵N increased from low to medium salinity, with a reverse trend at high salinity.

To investigate the reasons behind the increase in chlorophyll content and nitrogen concentration in the leaves of both species as salinity increased from low to medium levels, leaf cross-sections were examined under a light microscope. Moringa leaves (Figure 2) were generally thicker and spongier (less dense) than those of pomegranate (Figure 3). In both species, salinity did not appear to affect leaf thickness. However, as irrigation salinity increased from low to medium, there was a noticeable increase in the density of mesophyll cell packing, especially in the palisade cells (Figure 2 and Figure 3). While the anatomical structure of moringa leaves remained unchanged at high salinity, pomegranate leaves exhibited a reduction in the number of palisade cell layers under high-salinity conditions.

To further investigate whether the increase in salinity led to alterations in the photosynthetic machinery of both species, transmission electron microscopy (TEM) images of the leaf mesophyll cells were obtained. The TEM images revealed chloroplasts containing large starch granules, with the grana exhibiting well-aligned thylakoids. Notably, no degradation structures, such as plastoglobuli, were observed across all three salinity levels (Figure 4).

3.3. Vegetation Indices

In moringa, the vegetation indices NDVI, OSAVI, and NDRE all significantly increased from control to medium salinity, with NDRE showing a further slight increase at high salinity. However, for OSAVI, no significant changes were observed, while NDVI even showed a slight decrease at high salinity (

Table 3. Effect of salinity on multispectral vegetation indices. Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Optimized Soil Adjusted Vegetation Index (OSAVI), Renormalized Difference Vegetation Index (RDVI), Normalized Difference Red Edge Index (NDRE), and the RGB vegetation indices Green Area (GA) and Greener Area (GGA) in moringa and pomegranate trees. The values shown represent the means ± SE for each species under three different salinity levels (2, 5, and 10 dS m⁻¹). Salinity means followed by different letters are significantly different (p < 0.05) according to Tukey’s Honestly Significant Difference (HSD) test. The associated sums of squares and probabilities are provided (ns = not significant; *** p < 0.001; ** p < 0.01; * p < 0.05). Bold indicates significant results.

	2 dS m−1	5 dS m−1	10 dS m−1	Sum of Squares and Level of Significance
Moringa
NDVI	0.18 a ± 0.02	0.28 b ± 0.03	0.25 ab ± 0.02	0.05 *
SAVI	0.18 a ± 0.03	0.27 a ± 0.02	0.26 a ± 0.02	0.04 ns
OSAVI	−0.003 a ± 0.01	0.09 b ± 0.01	0.09 b ± 0.01	0.05 ***
RDVI	0.22 a ± 0.03	0.29 a ± 0.03	0.25 a ± 0.02	0.02 ns
NDRE	0.07 a ± 0.01	0.21 b ± 0.02	0.25 b ± 0.01	0.16 ***
GA × 100	12.44 a ± 3.25	8.00 a ± 3.27	6.30 a ± 1.55	181.03 ns
GGA × 100	0.48 a ± 0.23	0.70 a ± 0.54	0.15 a ± 0.06	1.37 ns
Pomegranate
NDVI	0.17 a ± 0.01	0.29 b ± 0.01	0.31 b ± 0.01	0.09 ***
SAVI	0.16 a ± 0.01	0.27 b ± 0.01	0.29 b ± 0.01	0.07 ***
OSAVI	0.04 a ± 0.01	0.13 b ± 0.01	0.15 b ± 0.02	0.05 ***
RDVI	0.15 a ± 0.01	0.27 b ± 0.01	0.29 b ± 0.01	0.08 ***
NDRE	−0.01 a ± 0.01	0.02 a ± 0.02	0.01 a ± 0.00	0.005 ns
GA × 100	8.22 a ± 0.01	13.49 ab ± 2.03	24.04 b ± 4.74	806.52 **
GGA × 100	0.14 a ± 0.01	0.09 a ± 0.05	0.05 a ± 0.01	0.02 ns

). Two additional indices, SAVI and RDVI, both tended to increase from low to medium salinity but decreased at high salinity, although these changes were not statistically significant. The GA generally decreased as salinity increased, and the GGA also tended to decrease from medium to high salinity, although these differences were not significant.

In pomegranate, the vegetation indices NDVI, SAVI, OSAVI, and RDVI increased from low to medium salinity, with only minor further increases at high salinity. In contrast, GA progressively increased from low to high salinity, while GGA tended to decrease with increasing salinity, although these differences were not significant. NDRE, however, did not show any changes in response to salinity (Table 3).

3.4. Mineral Content

In moringa, the Na concentration in the leaves remained stable from low to medium salinity but showed a significant increase at high salinity (

Table 4. Effect of salinity on the concentrations of potassium (K), calcium (Ca), magnesium (Mg), phosphorus (P), iron (Fe), sodium (Na), and the K/Na ratio in moringa and pomegranate trees. The values presented are the means ± SE for each species under the three salinity levels (2, 5, and 10 dS m⁻¹). Salinity means followed by different letters are significantly different (p < 0.05) according to Tukey’s Honestly Significant Difference (HSD) test. The associated sums of squares and probabilities (ns, not significant; *** p < 0.001; ** p < 0.01; * p < 0.05) are also provided. Bold indicates significant results.

	2 dS m−1	5 dS m−1	10 dS m−1	Sum of Squares and Level of Significance
Moringa
K (mg Kg−1)	11,885.55 a ± 612.42	10,304.44 a ± 686.33	10,770 a ± 400.93	11,883,355.55 ns
Ca (mg Kg−1)	16,211.11 a ± 1529.20	22,711.11 b ± 2036.36	20,455.55 ab ± 658.09	196,058,518.50 *
Mg (mg Kg−1)	3953.33 a ± 478.38	5474.44 b ± 446.34	5505.55 b ± 335.39	14,172,422.22 *
P (mg Kg−1)	2162.22 a ± 136.471	2098.88 a ± 158.92	2014.44 a ± 69.82	98,940.74 ns
Fe (mg Kg−1)	175.37 a ± 14.30	185.91 a ± 13.95	184.11 a ± 9.39	571.30 ns
Na (mg Kg−1)	2727.77 a ± 897.36	2671.11 a ± 355.45	6134.44 b ± 778.95	70,809,800 **
K/Na	6.94 a ± 1.38	4.67 ab ± 0.98	2.06 b ± 0.33	107.23 **
Pomegranate
K (mg Kg−1)	8502.44 a ± 573.76	12,131.55 b ± 549.62	10,283.20 ab ± 684.91	59,271,483.97 ***
Ca (mg Kg−1)	9904.06 a ± 926.98	7682.03 a ± 625.02	8458.70 a ± 692.86	22,655,849.11 ns
Mg (mg Kg−1)	2028.77 a ± 177.26	2028.66 a ± 71.36	2594.80 b ± 185.07	1,253,911.55 *
P (mg Kg−1)	1412.55 a ± 76.97	1595 a ± 103.21	1231.40 a ± 125.08	440,094.23 ns
Fe (mg Kg−1)	71.77 a ± 6.49	57.43 a ± 3.93	63.27 a ± 5.24	932.33 ns
Na (mg Kg−1)	286.55 a ± 31.26	442.44 a ± 76.07	2345.80 b ± 839.11	15,470,202.66 ***
K/Na	32.72 b ± 4.48	36.14 b ± 6.99	7.56 a ± 2.30	2878.01 **

). In contrast, Ca and Mg concentrations increased from low to medium salinity but remained unchanged (Mg) or tended to decrease (Ca) at high salinity. The concentrations of K, P, and Fe were unaffected by saline irrigation. Furthermore, the K/Na ratio progressively decreased as salinity increased from low to high.

In pomegranate, the pattern for Na concentration was similar to moringa, with a significant increase in Na from medium to high salinity, while the concentration remained unchanged from low to medium salinity (Table 4). Mg concentration followed a similar trend, with increases from medium to high salinity. K concentration increased from low to medium salinity but tended to decrease at high salinity. The concentrations of Ca, P, and Fe were not significantly affected by irrigation salinity. Additionally, the K/Na ratio decreased significantly from medium to high salinity but remained unaffected from low to medium salinity.

3.5. Correlations Between Analytical and Remote Sensing Indicators

The relationships between various analytical parameters (δ¹³C, δ¹⁵N, N, K, and K/Na) and remote sensing traits (such as leaf pigments, canopy temperature, and vegetation indices) were investigated across the three irrigation salinity levels for both moringa and pomegranate.

In moringa, δ¹³C did not show any correlation with the side-measured canopy temperature obtained from the CatS60 or the thermal gun. However, δ¹³C correlated positively with the zenithally measured canopy temperature assessed using the FLIR-Duo (GA-CatS60-TIR and Env-DUO-TIR). Furthermore, δ¹³C was positively correlated with total leaf chlorophyll content (and total nitrogen content) and several vegetation indices, including NDRE and OSAVI (Figure 5). Leaf δ¹⁵N negatively correlated with leaf anthocyanins. Nitrogen concentration showed positive correlations not only with total chlorophyll content but also with the side-measured canopy temperature using the infrared thermometer and the vegetation index NDRE. The Ft correlated positively with the side canopy temperature measured with the CatS60 (Env-CatS60-TIR) and the thermal gun. The Na concentration in leaves was only weakly positively correlated with the vegetation index OSAVI. The K/Na ratio was negatively correlated with the vegetation indices OSAVI, NDRE, and NDVI.

In pomegranate, δ¹³C showed a positive correlation with the side-measured canopy temperature assessed using the CatS60 (GA-CatS60-TIR and Env-CatS60-TIR), while no correlation was observed with the thermal gun measurements (Figure 5). In terms of the zenithally measured temperature, only the overall temperature, including the background (Env-DUO-TIR), showed a positive correlation with δ¹³C. Total leaf chlorophyll content (and total nitrogen content) also correlated positively with δ¹³C. Additionally, δ¹³C was positively correlated with several vegetation indices, including RDVI, NDVI, and SAVI.

Leaf δ¹⁵N showed a positive correlation with the side-measured canopy temperature from the CatS60 (GA-CatS60-TIR). Similarly to moringa, leaf δ¹⁵N was negatively correlated with the concentration of anthocyanins. However, in pomegranate, δ¹⁵N showed a stronger positive correlation with leaf chlorophyll content and several vegetation indices, such as NDVI, RDVI, and SAVI. Leaf nitrogen concentration was positively correlated with leaf chlorophyll content and the vegetation indices NDVI, RDVI, SAVI, OSAVI, and GA. Additionally, leaf nitrogen concentration was positively correlated with both the side-measured canopy temperature (CatS60, GA-CatS60-TIR, and Env-CatS60-TIR) and the temperature of the canopy measured zenithally with the FLIR-Duo (GA-DUO-TIR). The Ft was positively correlated with leaf chlorophyll content and anthocyanins for both species. Notably, the strength of the correlations between nitrogen concentration and chlorophyll content, as well as the vegetation indices, was stronger in pomegranate compared to moringa.

The Na concentration in leaves correlated positively with the zenithally measured temperature of the canopy using the FLIR-Duo (GA-DUO-TIR and Env-DUO-TIR), as well as with the side canopy temperature from the CatS60 (GA-CatS60-TIR). However, no correlations were observed between Na concentration and vegetation indices. The K/Na ratio showed negative correlations with both the zenithally measured temperature (FLIR-Duo: GA-DUO-TIR and Env-DUO-TIR) and the side canopy temperature from the thermal gun. This ratio also correlated negatively with the vegetation index NDRE.

In moringa, correlations between vegetation indices and canopy temperature were generally absent, except for a few cases where measurements were taken zenithally, where correlations were either positive (GA and GGA) or negative (NDRE). In contrast, in pomegranate, most vegetation indices showed positive correlations with canopy temperature, particularly (but not exclusively) when measured zenithally. Overall, the strength of the correlations between remote sensing and analytical traits was stronger in pomegranate compared to moringa. Furthermore, when the correlation matrix was recalculated using only low and medium salinities together, the strength of the correlations increased significantly, particularly in pomegranate (Supplemental Figure S1). Correlations assessed separately under each irrigation salinity level revealed that the relationships between remote sensing indices and analytical traits were stronger at 2 and 5 dS·m⁻¹ compared with 10 dS·m⁻¹ in pomegranate, while, in moringa, stronger correlations were observed at 5 and 10 dS·m⁻¹ compared with 2 dS·m⁻¹ (Supplementary Figure S2).

Concerning the PCA, in the case of moringa, δ¹³C was positioned near the NDRE, followed by OSAVI, NDVI, and SAVI, and it was opposite to K and P (Figure 6 and Supplemental Table S2). On the other hand, GA and, to some extent, Na were placed almost perpendicular to δ¹³C (Figure 6 and Supplemental Table S2). Chlorophyll content, nitrogen content, NBI, FT, RDVI, and Gun-TIR were clustered together and positioned opposite to anthocyanins and flavonoids (Figure 6 and Supplemental Table S2), and they were relatively perpendicular to Na, with δ¹³C being somewhat less influential in their positioning. Regarding Na, it was placed in the same quadrant as δ¹³C, near Mg, Ca, and Fe, as well as GA-DUO-TIR and Env-DUO-TIR, but opposite to K and the K/Na ratio. Finally, δ¹⁵N was positioned near GA and opposite to flavonoids, and, to a lesser extent, it was opposite GA-DUO-TIR and Env-DUO-TIR (Figure 6 and Table S2).

For pomegranate, δ¹³C was placed near Env-CatS60-TIR, as well as K, δ¹⁵N, Chl, and NBI, with a lesser proximity to N, and opposite to anthocyanins, Ca, and Fe (Figure 6 and Supplemental Table S3). Na was positioned close to NDRE and surrounded by Mg, GA-DUO-TIR, Env-DUO-TIR, and Gun-TIR, while it was opposite flavonoids and the K/Na ratio (Figure 6 and Supplemental Table S3). N was positioned perpendicular to δ¹³C, with the vegetation indices SAVI, OSAVI, NDVI, and RDVI falling between (Figure 6 and Supplemental Table S3).

4. Discussion

The primary analytical indicators used in this study to assess leaf response to salinity were δ¹³C and Na accumulation in the leaf dry matter. These indicators reflect the two-phase nature of plant response to salinity: first, an osmotic effect that induces water stress and leads to a decrease in stomatal conductance, followed by a toxicity effect associated with Na accumulation [8]. The increases in δ¹³C primarily reflect the initial stage of the plant’s response to salinity, driven by stomatal closure, which reduces stomatal conductance and consequently alters the ratio of intercellular to atmospheric CO₂ concentrations. This has been observed in both herbaceous species [32,49,50,51] and woody species [46]. In addition, differences in mesophyll conductance do not appear to significantly affect the interpretation of δ¹³C values as an indicator of plant water status. For instance, Ref. [52] reported that drought stress led to a proportional reduction in both stomatal and mesophyll conductance, thereby maintaining the reliability of δ¹³C as a proxy for water availability.

When irrigation salinity increased from low (2 dS m⁻¹) to moderate (5 dS m⁻¹), the effect on Na accumulation in the leaf was minimal, consistent with the glycophyte nature of the species, which actively prevents Na uptake and accumulation [8]. However, as salinity increased from medium (5 dS m⁻¹) to high (10 dS m⁻¹), Na accumulation increased sharply, and the K/Na ratio decreased, reflecting the onset of the second phase of salinity stress [28,32]. The PCA analysis (Figure 6) revealed a distinct pattern for δ¹³C relative to Na and K/Na in pomegranate, with a nearly perpendicular alignment of these traits, while moringa showed a more intermediate angle between these variables.

Another important analytical trait was δ¹⁵N, which serves as an indicator of the impact of salinity on nitrogen metabolism [28,51]. For moringa, δ¹⁵N was positioned perpendicular to δ¹³C and opposite to Na, suggesting that nitrogen metabolism in moringa is affected by osmotic stress at an earlier stage. In contrast, for pomegranate, δ¹⁵N and δ¹³C were aligned closely and perpendicular to Na, indicating that changes in nitrogen metabolism in pomegranate are more closely associated with Na accumulation.

4.1. Remote Sensing Assessment of Salinity: Stable Carbon Isotope Composition and Canopy Temperature

For both species, canopy temperature showed a positive correlation with δ¹³C across the three salinity levels (Figure 5), which aligns with the fact that water stress induced by salinity decreases stomatal conductance, leading to an increase in δ¹³C [53,54]. This decrease in stomatal conductance reduces transpiration, which in turn causes an increase in canopy temperature [55,56]. However, this study reveals a dual response to increasing salinity under irrigation. As salinity rises from low (2 dS m⁻¹) to moderate (5 dS m⁻¹) levels, a water stress effect is induced, leading to an increase in δ¹³C. This suggests that there is a decrease in stomatal conductance in response to increasing salinity from low to moderate levels. However, from moderate to high salinity (10 dS m⁻¹), changes in δ¹³C and temperature were minimal (Table 1). Specifically, temperature only slightly increased when salinity shifted from medium to high, indicating that stomata were already closed at medium salinity by the time measurements were taken (around noon).

Despite this, the Na concentration in dry matter across the three salinity levels showed a positive correlation with some temperature traits (specifically, leaf temperature measured with the DUO-TIR), but this was only true for pomegranate, with no correlations observed in moringa. Additionally, Mg was the only mineral that also positively correlated with temperature (again, with DUO-TIR measurements) for both species. It has been reported for herbaceous crops like quinoa that the huge levels of manure used to increase soil fertility in the sandy soils of Dubai can lead to salinity, even under irrigation with fresh water, due to Mg accumulation [57].

Nevertheless, the placement of the thermal sensor relative to the canopy (e.g., zenithally versus side-placed) can influence not only the absolute temperature values recorded but also the effectiveness of temperature measurements as indicators of salinity stress. In this study, zenithal measurements recorded higher temperature values compared to side-placed measurements. When a GA mask was applied to select only the leaves and exclude the background, lower temperatures were recorded compared to those that included the background. However, even with this adjustment, the temperatures measured from above the canopy (zenithally) were still higher than those measured from the side. This difference is likely due to heat radiation from the soil being captured in the zenithal measurements. Interestingly, the zenith-measured canopy temperature showed stronger correlations with δ¹³C, Na, and even vegetation indices (Figure 5), particularly when the GA mask was used, compared to side-measured temperatures.

4.2. Remote Sensing Assessment of Salinity: Leaf Pigment Content

Chlorophyll content on an area basis was not a reliable indicator of the second phase of salinity stress (Na-toxicity), as there was no correlation between chlorophyll content and Na levels in either species. Interestingly, chlorophyll content was positively correlated with δ¹³C (Figure 5), indicating that higher salinity levels led to an increase in chlorophyll content. Similarly, the concentrations of anthocyanins and flavonoids showed no correlation with Na, with only a weak negative correlation between flavonoids and δ¹³C in pomegranate. The lack of correlation between chlorophyll content and Na supports previous studies that found chlorophyll content to be an unreliable indicator of salinity stress, whether in pot-grown crops [28] or under field conditions [54,57]. This is likely because osmotic stress induced by salinity increases leaf thickness and/or compaction, leading to greater chlorophyll content per unit leaf area. In fact, for both species, chlorophyll content was more closely associated with δ¹³C than Na accumulation in the PCA (Figure 6) and positively correlated with δ¹³C (Figure 3).

In moringa, both nitrogen concentration on a dry matter basis and chlorophyll content on an area basis were closely aligned, suggesting that changes in chlorophyll content were influenced by the salinity’s effect on nitrogen concentration (Figure 6). In contrast, for pomegranate, nitrogen concentration and chlorophyll content on an area basis were largely unrelated, indicating that changes in chlorophyll content were more closely linked to leaf anatomy.

Increased salinity also triggers adaptations in leaf structure, resulting in thicker and/or more compact leaves with fewer intercellular air spaces, which leads to an increase in chlorophyll content per unit area [28,58]. Additionally, multispectral vegetation indices (NDVI, SAVI, OSAVI, and RDVI) tend to increase. The effects of moderate salinity on leaf structure may involve changes in both leaf anatomy, enhancing succulence and thickness, and an increase in chloroplast density. These adaptations not only explain the rise in chlorophyll content per unit area but also contribute to the total nitrogen concentration per dry matter in moringa. Moringa manages moderate salinity (50 mM NaCl) by maintaining leaf succulence and minimizing reductions in dry biomass [16]. This adaptation involves modulating physiological and biochemical attributes to effectively manage ion toxicity and oxidative stress. However, high salinity (100 mM NaCl) significantly reduces growth parameters [16].

For pomegranate, while previous studies have suggested a strong ability to exclude Na and Cl from leaf tissues with minimal foliar salt damage or discoloration [19], our results do not entirely align with this. Whereas in our study Na concentration in pomegranate was ten times lower than that of moringa at the lower salinity assayed, in agreement with the excluding capacity for Na of this species [19], the response to increasing salinity contradicts previous studies. Notably, δ¹³C decreased from medium to high salinity, suggesting a decline in internal (metabolic) photosynthetic capacity. This, combined with a reduction in chlorophyll content, suggests that photosynthetic capacity was compromised under high-saline conditions, consistent with the onset of senescence. Na accumulation was particularly evident when salinity increased from medium to high levels, resulting in a five-fold increase in pomegranate and a nearly two-fold increase in moringa.

4.3. Remote Sensing Assessment of Salinity: Vegetation Indices

The positive correlation observed between δ¹³C and vegetation indices initially seems paradoxical. In fact, a positive relationship has been found in pomegranate between vegetation indices derived from satellite images and the amount of water supplied, with NDVI, followed by SAVI and then a red edge vegetation index, showing the strongest correlations [27]. Similarly, another study demonstrated a strong correlation between NDVI values acquired from a UAV flying at 60 m and the potential irrigation requirements in pomegranate [59]. In contrast, the positive correlation observed in our study between δ¹³C and vegetation indices may be explained by the impact of increasing salinity from control to moderate levels and by the fact that vegetation indices were derived from images captured at a very short distance from the leaves. This close-range imaging likely enhanced sensitivity to changes in leaf anatomy, physiology, and pigment concentration. Thus, as salinity increases, leaf compaction occurs, which enhances chlorophyll content on an area basis. Indeed, transitioning from low to moderate salinity increased both the values of most vegetation indices and chlorophyll content on an area basis. However, further increases in salinity, from moderate to high, did not result in increases in spectral vegetation indices or chlorophyll content. In fact, chlorophyll content decreased in both species, and the NDVI also decreased in moringa (Table 3). The RGB index GGA further supported the onset of senescence, showing a clear trend from control to high salinity in both species (Table 3). As noted earlier, the shift from medium to high salinity appears to trigger the beginning of senescence, as indicated by the decrease in leaf chlorophyll content on an area basis and a decline in the NBI. This occurred despite an overall increase in total nitrogen concentration in the leaves as salinity rose, while most vegetation indices showed no clear pattern.

The observed increase in nitrogen concentration on a dry matter basis could be explained by mesophyll cell compaction, a response to increasing salinity, combined with the absence of degradation symptoms in the chloroplasts. This suggests that the increased nitrogen concentration is not due to chloroplast degradation. The chloroplast ultrastructure under all salinity conditions did not show signs of senescence, such as distortion of thylakoid membranes or the appearance of plastoglobuli [60,61]. These observations, along with the lack of differences in the chlorophyll fluorescence parameter Ft across salinity levels, point to other environmental stresses, such as high temperatures and atmospheric drought, having a greater impact on the plants than irrigation salinity itself. The large starch granules in the chloroplasts are consistent with ongoing photosynthetic activity, but their accumulation may also indicate a lack of starch retranslocation due to environmental stress, such as extreme temperatures [62].

The pattern observed in this study, where vegetation indices increased from low to moderate salinity but showed no clear effect from moderate to high salinity, contrasts with findings in many previous studies. Typically, a decrease in vegetation indices with increasing salinity has been widely reported in crops (e.g., [49,56]) and plant assemblages (e.g., [63] for oasis plants). This discrepancy may be due to the nature of the study’s subjects—individual trees with sparse canopies, as opposed to well-developed tree orchards with dense canopies and less exposed soil. Additionally, a methodological factor likely contributed to this pattern. Given the sparse canopy structure of the trees, we processed the images using masks (applying an NDVI threshold to remove the background), which resulted in vegetation indices reflecting the pigment content of individual leaves. In fact, for both species, leaf chlorophyll content on an area basis positively correlated with most vegetation indices.

In general, multispectral indices correlated better than RGB indices with analytical traits. Among the multispectral indices, NDVI showed the weakest correlation. NDVI tends to have limited performance under low to medium vegetation cover, where soil background effects become more prominent. This issue is less of a concern for vegetation indices like SAVI and OSAVI, which are designed to correct for soil brightness in areas with low vegetation cover [64]. Moreover, multispectral vegetation indices, such as SAVI and OSAVI, demonstrated better discrimination performance than RGB-based indices [65].

4.4. Remote Sensing Assessment of Salinity’s Effect on Nitrogen Status

In pomegranate, nitrogen concentration on an area basis, followed by δ¹⁵N and the Nitrogen Balance Index (NBI), were positively correlated with most of the vegetation indices. Additionally, δ¹⁵N, nitrogen concentration, and NBI strongly correlated with total chlorophyll content. In contrast, in moringa, the correlations were weaker but still positive for NBI with the vegetation indices, while nitrogen concentration and δ¹⁵N showed no correlation. Furthermore, chlorophyll content in moringa only correlated positively with NBI (which is based on chlorophyll) and, to a lesser extent, with nitrogen concentration. The positive correlations between vegetation indices, chlorophyll content, and various nitrogen-related traits support the idea that these remote sensing indices assess nitrogen concentration and related traits [37,66]. In fact, similarly to chlorophyll content, traits like NBI and δ¹⁵N exhibited a dual response, as they increased from control to moderate salinity but then decreased under high-salinity conditions (Table 2).

4.5. Remote Sensing Performance Is Species- and Salinity-Level-Dependent

The stronger and generally more consistent relationships between analytical data and remote sensing traits (such as leaf pigments, thermal canopy, and vegetation indices) observed in pomegranate compared to moringa are likely due to the more homogeneous, compact, and rounded canopy structure of pomegranate. However, leaf structure also plays a role because correlations of analytical traits, such as δ¹³C, δ¹⁵N, nitrogen concentration, and even Ft, with leaf chlorophyll content, the NBI ratio, or total anthocyanins were stronger in pomegranate than in moringa. This difference may be attributed to the more compact leaf anatomy of pomegranate, which features fewer intercellular spaces compared to moringa leaves [67,68].

Furthermore, the correlations between remote sensing parameters and the analytical/physiological indices were clearly higher when only low and medium salinity were considered compared with the inclusion of the three salinities. These results support the fact that for a long-established trial (i.e., several years prior to the study), the performance of the remote sensing parameters as stress indicators will depend on the range of salinities tested.

5. Conclusions

Canopy temperature proved to be the most effective at assessing stress when moving from low to moderate salinity, reflecting osmotic stress and the subsequent decrease in stomatal conductance, as indicated by δ¹³C. However, temperature measurements were less effective in detecting the impact of further increases in irrigation salinity from medium to high concentrations. This is likely because stomata were already nearly closed at medium salinity levels. Interestingly, compared to side-measured temperatures, zenith-measured canopy temperature showed stronger correlations with Na and Mg and even with vegetation indices, particularly when the GA mask was used. For example, in the case of pomegranate, Na correlated more strongly with GA-DUO-TIR (r = 0.814, p < 0.001) than GA-CatS60-TIR (r = 0.465, p < 0.05).

For low to moderate salinity conditions, the increase in vegetation indices mirrored the accumulation of chlorophyll on a leaf area basis, which was caused by mesophyll leaf compaction. However, further increases in salinity from moderate to high (10 dS m⁻¹) caused a significant rise in Na concentration, which triggered mild leaf senescence. This was reflected by a slight decrease in chlorophyll content per unit surface area and the absence of a corresponding increase in most vegetation indices, with some even showing a decrease. The positive correlations between vegetation indices and the analytical traits suggest that the indices more accurately reflect the condition of individual leaves rather than the overall canopy.

Species also played a significant role in the performance of different remote sensing approaches to characterizing the response to salinity. In pomegranate, analytical indicators (δ¹³C, Na, and even N concentration and δ¹⁵N) were much more strongly correlated with the various remote sensing indices (thermal and vegetation indices) than in moringa. This was likely due to differences in both leaf anatomy and the tree canopy structure, which is much more homogeneous in pomegranate than in moringa. The practical implementation of remote sensing by farmers or agronomists may involve the use of low-cost multispectral and/or thermal imaging systems mounted on poles to capture zenithal images. Future research could explore the comparative performance of various zenithally positioned sensors, including the use of smartphones equipped with both RGB and thermal imaging capabilities. Such devices can enable the simultaneous acquisition of RGB and thermal images, further facilitating real-time decision making through smartphone applications.

High-resolution RGB images also offer the potential to develop new RGB-based vegetation indices, which could eventually serve as alternatives to traditional multispectral indices. However, several limitations still need to be addressed. For example, the relatively low resolution of thermal images and the potential for direct sunlight exposure to interfere with temperature readings when smartphones are placed above the canopy may affect data accuracy. These challenges highlight the need for further validation and optimization before widespread adoption in field conditions.