Research Article

Indicators of Chemical Pollution and Soil Salinity and Their Relationship to Reflectance Spectroscopy

Mazin Fadhil Khudhair1*, Abdul Baqi D.S.Al Maamouri1 and Sadeq Jaafar Hassan Dwenee2

1Department of Soil Science and Water Research, Coll. Agric. Engin. Sci. University of Baghdad, Iraq; 2Commission Agricultural Research Centre, Iraq.

Received | April 29 2025; Accepted | September 11, 2025; Published | February 23, 2026

*Correspondence | Mazin Fadhil Khudhair, Lecturer, Dept. of Soil Sci. and Water Rese. Coll. Agric. Engin. Sci. University of Baghdad, Iraq; Email: [email protected]

Citation | Khudhair, M.K., A.B.D.S. Al Maamouri and J.H. Dwenee. 2026. Indicators of chemical pollution and soil salinity and their relationship to reflectance spectroscopy. Pakistan Journal of Agricultural Research, 39(1): 48-68.

DOI | https://dx.doi.org/10.17582/j.pjar/2026/39.1.48.68

Keywords | Heavy metal pollution, Spectral indices, Anthropogenic soil degradation, Soil quality.

Copyright: 2026 by the authors. Licensee ResearchersLinks Ltd, England, UK.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Introduction

Chemical pollution refers to the disruption of ecological balance through the introduction of hazardous chemical compounds, whether of natural or anthropogenic origin, which inflict significant harm on living organisms and degrade natural resources. These pollutants originate from diverse sources, encompassing industrial, agricultural, and urban activities, as well as natural phenomena such as volcanic eruptions. Chemical substances attain pollutant status when their concentrations surpass environmentally acceptable thresholds or when they accumulate in incompatible ecosystems, detrimentally impacting organismal health and ecological integrity (Brusseau and Artiola, 2019). As emphasized by Kolawole and Iyiola (2023), soil pollution stems from both geogenic processes (e.g., rock weathering and mineral deposition) and anthropogenic practices, including excessive application of agrochemicals, irrigation with untreated wastewater, and improper disposal of industrial effluents (liquid, solid, and gaseous), collectively contributing to soil degradation and loss of biospheric viability. Notably, pesticides represent a critical class of chemical contaminants, exerting direct and indirect effects on human, animal, and plant health, while concurrently inducing profound perturbations in soil’s physicochemical and biological attributes (Alengebawy et al., 2021).

Heavy metal and industrial chemical pollution represent critical threats to global biodiversity, with widespread chemical usage disrupting ecosystem functions and necessitating quantification of environmental assimilation capacities to ensure sustainability. The pollutant potential of any chemical substance regardless of quantity is governed by its intrinsic toxicity and reactivity, encompassing heavy metals, salts, and compounds across all physical states (solid, liquid, gaseous) that demonstrate ecotoxicological effects. The ramifications of such pollution extend systemically from terrestrial to aquatic environments, where intensive agriculture and population pressures have been identified as dominant drivers of aquatic chemical contamination (Wang et al., 2023). This is exacerbated by urbanization and industrial expansion, which elevate pollutant loads in water bodies (Morin-Crini et al., 2022). Water scarcity has precipitated the use of untreated wastewater for irrigation, resulting in concomitant groundwater and soil pollution (Wahba et al., 2022), while industrial effluents significantly degrade adjacent water quality, impairing agricultural viability (Sharafi and Salehi, 2025). Comprehensive studies have cataloged contamination sources including: (i) municipal and industrial wastewater discharges (Khudhair et al., 2018), (ii) treatment plant effluents (Safa and Mustafa, 2025), (iii) biomedical and industrial waste streams (Chyad et al., 2022), and (iiii) petroleum extraction sites (Qadoori, 2025). Supplementary research implicates energy generation facilities, construction material production, and textile manufacturing in aquatic chemical pollution (Farhan, 2020), with petroleum operations further compromising water resources through extraction and transport processes (Al-Mashhadani, 2024). Of particular concern is the trophic transfer of pollutants through bioaccumulation in flora, creating exposure risks for higher organisms. Phytotoxic effects manifest as physiological damage (chlorosis, necrosis, growth retardation), metabolic disruption, and impaired photosynthetic efficiency (Chyad et al., 2022). At molecular levels, these contaminants induce genotoxic effects, chlorophyll degradation, and germination suppression, ultimately diminishing agricultural output (Alengebawy et al., 2021).

Soil salinization constitutes one of the most severe environmental and agricultural challenges, particularly in arid and semiarid ecosystems, where it induces progressive soil fertility decline and diminished agricultural yields. As evidenced by Lekka et al. (2024), anthropogenic factors including unsustainable irrigation practices coupled with climate change accelerate salinization processes, substantially impeding sustainable land management. The phytotoxic effects of salinity manifest primarily through osmotic stress and ion toxicity, impairing root water/nutrient absorption and consequently causing growth retardation. This phenomenon demonstrates marked spatiotemporal heterogeneity, with current estimates suggesting 20-33% of global irrigated lands and 6-20% of total cultivated areas are salinity-affected (Devkota et al., 2022). The present review systematically evaluates contemporary findings regarding the synergistic relationship between heavy metal pollution and soil salinization, with specific focus on their diagnostic spectral signatures. Moreover, it assesses the efficacy of emerging remote sensing platforms in spatially monitoring these degradation processes, offering cost and time efficient alternatives to traditional laborintensive soil assessment methodologies.

Chemical soil pollution

Soil constitutes a dynamic natural system comprising mineral and organic constituents, gaseous and liquid phases, and diverse biotic communities, formed through pedogenic processes involving climate, biota, parent material, topography, and time (the five soilforming factors). This vital resource exhibits both biotic (including microbiota and macrofauna) and abiotic (mineral and organic matrices) components. Soil pollution has progressively become a principal factor in pedospheric degradation, with its incidence escalating markedly post-Industrial Revolution due to intensified agricultural practices and fossil fuel combustion, establishing anthropogenic activities as the dominant source of terrestrial chemical contamination. From a regulatory perspective, chemically compromised soils are classified as such when contaminant loads, whether organic pollutants (e.g., polycyclic aromatic hydrocarbons) or inorganic species (e.g., heavy metal(loid)) - exceed thresholds that impair ecosystem func-tionality (Cachada et al., 2017).

Kerfahi et al. (2019) operationalized soil chemical pollution as the deliberate or incidental introduction of xenobiotic substances that compromise pedological quality and inhibit biota viability. Building upon this conceptual framework, Münzel et al. (2023) quantitatively defined anthropogenic soil contamination as the accumulation of synthetic chemical species at concentrations surpassing geogenic baselines, constituting measurable threats to both public health and ecological stability. The principal contaminant categories comprise: (i) metallic elements (e.g., Cd, Pb, As), (ii) persistent organic pollutants (e.g., organochlorine pesticides), and (iii) recalcitrant particulate matter (e.g., microplastics <5mm), all of which induce deleterious alterations to soil’s biogeochemical cycling and edaphic functionality.

Pedogenic and anthropogenic sources of terrestrial chemical contamination

Kolawole and Iyiola (2023) systematically demonstrated that chemical soil contamination derives from both geogenic sources (as lithogenic constituents of parent materials) and anthropogenic processes, encompassing: (i) intensive agricultural practices (synthetic agrochemical application and untreated wastewater irrigation) and (ii) industrial activities (multiphase waste effluents). These anthropogenic inputs cumulatively compromise pedological integrity, rendering soils biologically inhospitable. Principal contamination pathways comprise: (i) agrochemical loading (NPK fertilizers and persistent organic pesticides); (ii) accelerated pedospheric erosion through hydrometeorological events and anthropogenic disturbance (forest clearance, urban expansion, extractive industries); (iii) nonjudicious saline irrigation practices; and (iiii) industrial pointsource emissions from pharmaceutical synthesis, azodye production, petrochemical refining, and cosmetic formulation sectors, with particular emphasis on low density polyethylene (LDPE) macromolecular contaminants.

Chemical soil pollution originates from multiple sources, with anthropogenic activities constituting the primary driver, though natural processes also contribute significantly (Issa et al., 2019). Geogenic sources of heavy metal contaminants include the weathering of mafic igneous and metamorphic rocks, along with sedimentary deposits originating from igneous protoliths (Al-Rawi and Razzak, 2020). Angon et al. (2024) further characterized key natural sources as the physical and chemical breakdown of metal-enriched lithologies and pyroclastic emissions from volcanic activity. The major anthropogenic contamination vectors consist of: (i) mineral fertilizer inputs for soil productivity augmentation, (ii) agricultural biocides (herbicides, insecticides, fungicides) applied for crop protection, and (iii) irrigation with contaminated water sources (Taghavi et al., 2024). Corroborating these observations, Sharafi and Salehi (2025) quantitatively established through systematic monitoring that industrial point sources induce substantial pedological and hydrological degradation, ultimately causing agricultural land abandonment.

Chyad et al. (2022) demonstrated that irrigation with effluent from industrial and clinical waste-water systems constitutes a major vector for soil chemical contamination. In parallel research, Basha and Issa (2025) quantified significantly enhanced soil contaminant loads attributable to combustion-derived particulate and gaseous emissions. Naser (2025) further corroborated these anthropogenic mechanisms, identifying irrigation water as the predominant source of heavy metal accumulation in agricultural topsoil. Complementing these findings, Ali and Abdullah (2024) systematically verified dual contamination pathways: (i) pedogenic processes involving phyllosilicate minerals (illite, kaolinite), feldspars, and carbonate phases (dolomite); and (ii) anthropogenic inputs from intensive agriculture and industrial operations.

Emerging research has systematically characterized the role of energy infrastructure and construction material industries in pedochemical contamination. Muslim (2019) established that surficial soil degradation stems from three principal anthropogenic point sources: (i) brick kiln emissions, (ii) asphalt plant effluents, and (iii) particulate matter from thermal power generation, with agricultural inputs acting as a secondary diffuse source. Farhan (2020) subsequently isolated two additional contamination vectors through isotopic tracing: coalfired power plants and textile manufacturing facilities, both demonstrating significant heavy metal enrichment factors (>3.0). AL-Heety et al. (2021) provided mechanistic validation via X-ray fluorescence spectroscopy, quantifying that diesel-powered generators contribute 18-23% of total petroleum hydrocarbon (TPH) loads in adjacent soils, with detectable Pb, Cd, and Zn.

Chemical pollution in irrigation water

Water, being fundamental to life on Earth, is increasingly contaminated by anthropogenic activities. Deficient management of water resources poses significant risks to ecological balance and human wellbeing. Chemical contamination of irrigational water stems from both deliberate and incidental introduction of xenobiotic compounds into aquatic systems (Kolawole and Iyiola, 2023).

Chemical contaminants accumulate in both surface water and groundwater systems through Anthro-pogenic inputs and geogenic processes (Chorol and Gupta, 2023). Contemporary research indicates aquatic pollution presents more acute ecological threats than terrestrial contamination (Kerpelis et al., 2025), as soil-derived chemical pollutants frequently serve as principal contributors to aquatic ecosystem degradation (Münzel et al., 2023). These contaminants demonstrate distinct longitudinal dispersion patterns along hydrological gradients, with concentration profiles exhibiting exponential decay relative to source proximity.

Principal aquatic systems, encompassing lotic (rivers, streams) and lentic (lakes, ponds) fresh-water bodies, along with marine and estuarine environments, represent essential resources supporting agricultural, industrial, and ecological functions. Chemical contamination of these hydrologic systems presents substantial risks to their sustainability and service provision. Contemporary research classifies aquatic pollutant sources into two principal categories: (1) Anthro-pogenic inputs (industrial effluents, agricultural runoff, urban wastewater) and (2) geogenic/natural processes (mineral weathering, volcanic emissions) (Babuji et al., 2023).

Sources of chemical pollution in irrigation water

Kolawole and Iyiola (2023) classified aqueous chemical pollutant sources into two principal categories: anthropogenic and geogenic origins. Anthropogenic contributions encompass: (i) industrial effluent discharges (both liquid and solid phases), (ii) agriculturally derived runoff laden with agrochemicals (fertilizers, pesticides, and herbicides), and (iii) untreated municipal wastewater. Geogenic sources primarily involve the hydrologic weathering of metalliferous mineral formations. Additionally, these pollution sources are dichotomized as point sources (e.g., direct sewage outfalls into watercourses) or nonpoint sources (e.g., diffuse agricultural runoff.

Singh et al. (2020) classified the origins of aquatic chemical pollutants into three principal categories based on their sources: (i) geogenic (e.g., volcanic emissions), (ii) naturally occurring substances with anthropogenic concentration amplification (e.g., heavy metals in irrigation systems), and (iii) exclusively synthetic compounds (e.g., organophosphate pesticides). The authors further delineated contamination pathways into direct (pointsource discharges) and indirect (non-point source transport through pedospheric or atmospheric vectors). Wang et al. (2023) established agricultural intensification and demographic pressures as dominant drivers of aquatic chemical contamination, while Morin-Crini et al. (2022) documented exponential pollutant load increases associated with urban-industrial expansion. In water-stressed regions, the practice of wastewater irrigation has been shown to induce concomitant groundwater and soil contamination (Wahba et al., 2022), with industrial effluents representing a critical point-source contributor to proximate aqueous system degradation (Sharafi and Salehi, 2025).

Khudhair et al. (2018) identified sewage and municipal waste discharge as significant contributors to the contamination of the Tigris River. Expanding on this, Safa and Mustafa (2025) conducted a comprehensive water quality analysis, revealing that pollutant concentrations consistently surpassed regulatory thresholds, primarily due to untreated wastewater effluents. Further investigations by Chyad et al. (2022) highlighted the detrimental impact of industrial and medical waste discharges on riverine ecosystems. In contrast, Qadoori (2025) attributed the predominant chemical pollution in the region’s water resources to hydrocarbon infiltration from oilfield operations.

Chemical pollution in plants

Chemical pollution in plants refers to the degradation of plant health due to the accumulation of toxic chemical substances in the soil, water, or plant tissues. This contamination disrupts critical physiological and biochemical processes, impairing growth, reducing yield, and, in severe cases, causing plant mortality. Prominent chemical pollutants include heavy metals (e.g., cadmium, lead), agrochemicals (such as pesticides and herbicides), and airborne toxic compounds (Rodríguez et al., 2018).

The absorption of chemical pollutants by plants, their bioaccumulation in plant tissues, and subsequent entry into the food chain through human and animal consumption present a serious ecological and public health risk. Documented that chemical pollutants induce phytotoxic effects, including leaf scorching, growth retardation, foliar deformities, and tissue necrosis. Elevated concentrations of these contaminants can disrupt cellular metabolic pathways and displace vital nutrients, compromising essential physiological functions. Further demonstrated that high pollutant levels impair photosynthetic efficiency and transpiration rates, resulting in inhibited growth, foliar chlorosis, and, in severe cases, plant mortality (Chyad et al., 2022). Additionally, Alengebawy et al. (2021) reported that chemical pollutants induce genotoxic effects, reduce chlorophyll synthesis, suppress enzymatic activity, and diminish crop yields. Prolonged exposure leads to oxidative stress, manifested as leaf curling, necrosis, and, at critical concentrateions, complete inhibition of photosynthesis and plant death.

The extensive variety of chemical pollutants exerts multiple observable and nonobservable impacts on plants, which can be assimilated through roots, stems, or foliage. Chronic exposure of plants to contaminants has been shown to diminish nutrient acquisition efficiency, suppress photosynthetic activity, induce growth retardation, and reduce crop yield (Rahman et al., 2023). Emphasized that chemical pollutants in soil compromise crop productivity, degrade food quality, and undermine long-term agricultural sustainability. Furthermore, Rashid et al. (2023) established that pollutant toxicity in crops is influenced by plant species and developmental stage, pollutant bioavailability, soil physicochemical and biological characteristics, and rhizosphere chemistry.

Sources of chemical pollution in plants

Humaira et al. (2024) identified a wide range of chemical contaminants in plants, all traceable to three principal sources: soil, water, and air. Established that anthropogenic activities were the primary drivers of these contaminants, including the application of chemical fertilizers to improve soil fertility, the use of pesticides for pest and weed control, irrigation with sewage and industrial effluent, as well as emissions from industrial fumes, gaseous pollutants, improper disposal of industrial waste, and vehicular exhaust emissions. These findings were further corroborated by Vasilachi et al. (2023) in their investigation into the effects of pollutants on the growth and yield of cereal crops cultivated in contaminated soil. Additionally, numerous studies have highlighted that agricultural practices, along with industrial emissions, such as noxious fumes, gaseous discharges, and solid waste constitute major contributors to plant chemical contamination. The research conducted by Uzakov et al. (2023) conclusively demonstrated that the bioaccumulation of toxic compounds in plant tissues resulted from both direct and indirect anthr-opogenic influences.

Narayanan et al. (2020) identified soil as a significant reservoir of diverse pollutants, emphasizing its role in facilitating plant growth and the subsequent uptake of toxic compounds through root systems. Highlighted soil as a natural medium that supplies chemical contaminants, particularly heavy metals, derived from both geogenic processes (e.g., weathering of parent rock material) and anthr-opogenic activities, including vehicular emissions, industrial operations, power generation, waste disposal, and the combustion of coal and fossil fuels. Furthermore, Speight (2017) demonstrated that inadequate waste management practices exacerbate adverse effects on plant health.

The global water scarcity crisis has compelled farmers to increasingly rely on industrial wastewater for agricultural irrigation (Wahba et al., 2022). Souri et al. (2022) documented the widespread use of both sewage and industrial effluents to fulfill irrigation demands, resulting in significant chemical contamination of crops. The application of untreated sewage water for irrigation introduces pollutants into edible plant tissues (Aslam et al., 2023), a conclusion further supported by Atta et al. (2023), who identified sewage effluent as a major vector for phytochemical contamination. Aslam et al. (2024) demonstrated that crops irrigated with sewage water accumulated contaminants at concentrations surpassing the maximum permissible limits established by the U.S. Environmental Protection Agency. Similarly, Yan et al. (2024) reported that even treated wastewater used for vegetable irrigation led to detectable heavy metal accumulation. Singh et al. (2024) investigated contamination sources in crops cultivated near industrial zones and attributed elevated heavy metal concentrations to the utilization of industrial wastewater. In a compr-ehensive analysis, Kong et al. (2025) examined five vegetable species grown in sewage-irrigated soils, revealing contaminant levels exceeding internationally recognized safety thresholds, thereby posing substantial public health risks.

Agricultural practices, industrial activities, and the discharge of solid, liquid, and gaseous waste from manufacturing processes have been recognized as significant contributors to plant contamination. Rashid et al. (2018) reported that the extensive application of chemical fertilizers and pesticides substantially exacerbates plant pollution. Furthermore, Alengebawy et al. (2021) demon-strated that the overuse of pesticides across diverse crop varieties results in the bioaccumulation of toxic elements in plant tissues. In addition, highlighted that elevated traffic density in urban areas leads to the progressive accumulation of pollutants in cultivated plants.

Mahmood (2016) identified irrigation water as the primary source of contamination in crops cultivated within the agricultural fields of Al-Dora district, Baghdad Governorate. Concurrently, Abbas (2018) demonstrated that emissions of volatile compounds and gaseous pollutants from the Al-Furat Chemical Plant and the Al-Samawa Cement Plant in Babylon Governorate significantly contributed to the contamination of cultivated vegetation. Empirical studies have established a strong positive correlation between proximity to industrial zones and increased pollutant concentrations in plants. For instance, Farhan (2020) reported statistically significant variations in contaminant levels in vegetation near industrial facilities, including the Al-Zubaidiyah Power Plant, Al-Aziziyah Soap Factory, Al-Kut Textile Factory, and Al-Hai District Brick Factory in Wasit Governorate compared to control sites farther from pollution sources.

Further investigations by Abdul Latif (2020) in Baghdad Governorate and its surrounding suburbs revealed exacerbated chemical contamination in plants within urban and agricultural areas, attributable to cumulative exposure to multiple industrial and agricultural pollutants. Additionally, Al-Mashhadi and Alabadi (2023) documented significant bioaccumulation of pollutants in vege-tation adjacent to the Baghdad–Diwaniyah high-way, with contamination levels in both aerial and root tissues surpassing permissible thresholds in Baghdad Governorate relative to Diwaniyah Governorate. This disparity was linked to elevated vehicular emissions and heavy traffic density in the metropolitan region.

Moreover, Harby et al. (2023) underscored the role of contaminated irrigation water in plant pollution, demonstrating that water sourced from the Tigris River at four monitored sites in Baghdad - Al-Rashidiya, Al-Dora Power Station, the Diyala-Tigris confluence (Al-Tuwaytha), and Salman Pak served as a key vector for the uptake of chemical pollutants in vegetation.

Heavy Metals: Their sources and harmful effects in the agricultural environment

The definition of heavy metals remains inconsistent across scientific disciplines, reflecting divergent conceptual frameworks. In chemistry, Schmitz (2018) characterizes them as elements that exhibit dual roles in biological systems serving as essential micronutrients (e.g., zinc) or toxicants (e.g., lead) while acknowledging the absence of a universally accepted definition. Their classification may be based on density, atomic mass, or chemical reactivity, analogous to historical scientific conc-epts such as Newton’s theory of gravity, which gained widespread acceptance despite initial ambiguities.

From a toxicological and public health standpoint, they define heavy metals as elements with elevated atomic weight and density, capable of bioacc-umulation and inducing toxicity even at trace concentrations. In engineering and materials science, they describe them as periodic table elements with high atomic mass and density. Classifying any metal with a density >5.0 g cm-³, specific weight >65, and demonstrable toxicity as a heavy metal.

Liu et al. (2019) adopt a broader perspective, encompassing both metals and metalloids with densities exceeding 4±1 g cm-³ (e.g., cadmium, copper, and lead). In agricultural and enviro-nmental sciences, emphasize their phytotoxic and zootoxic effects at low concentrations, while Eskandari et al. (2020) specify an atomic weight range of 63.5–200.6 a.m.u. and a density threshold >5 g cm-³. Alias et al. (2020) further expand the definition to include all metallic and metalloid elements exhibiting acute toxicity even at minimal exposure levels.

Wołowiec et al. (2019) introduce a density-based criterion (≤4×10⁶ mg L-³), underscoring the chemical diversity of heavy metals. Darwesh and Matter (2021) highlight their non-biodegradable nature, high relative density (>5 g cm-³), atomic mass (>20), and propensity for bioaccumulation in plant tissues.

Contemporary research increasingly integrates physicochemical properties (density, atomic weight) and ecotoxicological behavior (bioacc-umulation, toxicity) in defining heavy metals (Sarma et al., 2024). Some studies describe them in relation to water density multiples (Rashid et al., 2023) or as naturally occurring, high-density comp-onents of ecosystems (Machado and Dinis, 2023). Collectively, heavy metals are broadly recognized as metallic and metalloid elements posing significant ecological and human health risks (Briffa et al., 2020).

Agricultural ecosystems are increasingly subjected to chemical pollution from heavy metals due to anthropogenic agricultural and industrial activities (Ali et al., 2019). Industrial operations, agricultural practices, coal combustion, waste incineration, and mining activities significantly threaten biotic systems. Although heavy metals occur naturally in the environment, anthropogenic activities have disrupted their biogeochemical cycles (Moghadas et al., 2022). These metals are introduced into the environment through both geogenic processes and various human activities, including coal mining, leather tanning, metallurgical industries, agroch-emical applications, and improper waste disposal (Xu et al., 2024). Additional contributors include vehicular emissions, synthetic dye utilization, tobacco combustion, and the application of sewage and industrial effluents for irrigation, all of which exacerbate heavy metal accumulation, (Abdullahi et al., 2021; Fayhaa et al.,2025; Mohammed et al., 2025).

A comprehensive understanding of heavy metal sources and their chemical speciation is essential for elucidating their environmental fate and developing effective soil remediation strategies. Discriminating between lithogenic and Anthro-pogenic origins is critical for formulating targeted pollution mitigation measures. Recent regional studies in Iraq indicate that anthropogenic sources such as industrial and agricultural discharges predominate (Al-Mashhadani, 2024; Basha and Issa, 2025; Safa and Mustafa, 2025; Qadoori, 2025), whereas a minor fraction originates from natural weathering and geochemical processes (Al-Rawi and Razzak, 2020; Ali and Abdullah, 2024). Key anthropogenic pathways include the use of synthetic fertilizers, pesticides, wastewater irriga-tion, mining operations, and petrochemical indu-stries.

Heavy metal pollution has emerged as a pressing global environmental concern due to its detrimental effects on public health and ecosystem integrity (Wan et al., 2024). Their accumulation in agricultural soil impairs crop productivity, thereby jeopardizing food security (Humaira et al., 2024). These metals pose severe ecological hazards owing to their high toxicity, environmental persistence, and propensity for bioaccumulation in trophic chains. While certain metals, such as copper, serve as essential micronutrients at trace levels, they exhibit phytotoxicity at elevated concentrations. Their non-biodegradable nature facilitates their uptake and bioaccumulation in plant tissues (Brusseau and Artiola, 2019), where they persist without degradation (Sarma et al., 2024). Among the most hazardous heavy metals are cadmium (Cd), lead (Pb), arsenic (As), mercury (Hg), and chromium (Cr) (Angon et al., 2024), with toxicity dependent on exposure dose and duration (Chen et al., 2022). Even at low concentrations, these metals can induce significant adverse effects.

AL Maamouri and Al Shamary (2024) demo-nstrated that the accumulation of lead (Pb) and Cadmium (Cd) in soil was significantly correlated with the presence of organic matter (O.M), Calcium carbonate (CaCO3), and clay minerals. This finding was further supported by Shref et al. (2024), who confirmed that the strongest associations of Pb and Cd in soil occurred in the presence of organic matter and clay minerals. In a related study, Naser (2019) reported that boron (B) adsorption in soil varied depending on the type and concentration of salts present.

Upon entering soil and aquatic systems, heavy metals are assimilated by plants through root uptake and foliar absorption (Sarma et al., 2024), subsequently infiltrating the food chain and posing health risks to humans and wildlife (Atta et al., 2023). Chronic exposure via contaminated crops or water has been linked to renal dysfunction, carcinogenesis (Vielee and Wise, 2023), metabolic disorders, and neurodegenerative diseases (Dagdag et al., 2023). Furthermore, Humaira et al. (2024) demonstrated that heavy metal contamination degrades soil health and diminishes agricultural yields, underscoring the urgency of sustainable remediation approaches.

Indicators of chemical pollution

Chemical soil pollution occurs when chemicals are used, either intentionally or unintentionally, caus-ing an imbalance in the soil components. This imbalance leads to noticeable changes in the soil’s characteristics, which are detrimental to agriculture and pose health risks to humans. International pollution standards are used to evaluate the level of pollution in soil and include the following stan-dards:

Factor enrichment (EF)

(Chester and Stoner, 1973)

Where: (C metal sample) concentration of pollutant element in soil, (C normalizer sample) total concentration of iron in the soil, (C metal control) concentration of the contaminant in the comparison treatment, and (C normalizer control) total concentration of iron in the soil for the comparison treatment.

 

Table1: EF classification values (Sutherland, 2000).

classification	values EF
low	EF < 2
Moderate	2 < EF < 5
high	5 < EF < 20
very high	20 < EF < 40
Extremely high	EF > 40

 

6-2- Geoaccumulation Index (Igeo):

…….. (Müller, 1969)

Where: (C metal) Heavy metal concentration in soil, (C control) Concentration of the heavy element in control, and (1.5) It is a constant used to analyze natural and abnormal fluctuations in the concentration of heavy metals in soil.

 

Table2: Igeo classification values (Müller, 1986).

Class	Value Igeo	Classification
0	Igeo ≤ 0	Uncontaminated
1	0 < Igeo <1	Uncontaminated to moderately
2	1 < Igeo < 2	Moderately
3	2 < Igeo < 3	Moderately to heavily
4	3 < Igeo < 4	Heavily
5	4 < Igeo < 5	Heavily to Extremely
6	Igeo ≥ 5	Extremely

 

6-3- Contamination Factor (CF):

.……(Hakanson, 1980)

Where: (Cm sample) the measured concentration sample value, and (Cm background) the measured background value.

 

Table3: CF The classes of contamination factor (Hakanson, 1980).

Value CF	Contamination degree
CF < 1	Low
1 ≤ CF < 3	Moderate
3 ≤ CF < 6	Considerable
CF ≥ 6	Very High

 

6-4- Pollution Load Index (PLI)

PLI= (CF1xCF2xCF3x…xCFn)1/n (Tomlinson et al., 1980)

Where: (CF) is The Contamination factor, and (n) The Number of metals.

 

Table4: PLI Pollution level (Tomlinson et al., 1980).

Value PLI	Pollution level
PLI < 1	Unpolluted
1 ≤ PLI < 2	Moderately to unpolluted
2 ≤ PLI < 3	Moderately polluted
3 ≤ PLI < 4	Moderately to highly polluted
4 ≤ PLI < 5	Highly polluted
PLI ≥ 5	Very highly polluted

 

6-5- Bioaccumulation Coefficient (BAC)

(Trapp and Matthies, 1995)

Where: (BAC) Refers to the concentration of heavy metals in the shoot plant to its total concentration in the soil, (Metal Shoot) Concentration of heavy metals in the shoot plant, and (Metal Soil) Concentration of heavy metals in Soil.

 

Table5: BAC values (Baker and Books, 2007).

Value BAC	Indicators
BAC < 1	Ability
BAC > 1	Inability

 

Soil Salinity: Detrimental Effects on Soil and Crops

The classification of saline soil lacks a universal standard, as definitions vary depending on cont-extual and regional factors. According to the U.S. Salinity Laboratory (USSL), soil is classified as saline when its soluble salt concentration reaches a threshold that adversely affects the growth of most conventional crops. This condition is quantitatively defined by an electrical conductivity of the satu-rated paste extract exceeding 4 dS m-¹ at 25°C, coupled with a pH below 8.5 and an exchangeable sodium percentage (ESP) of less than 15%. Characterized saline soils by elevated levels of soluble salts primarily calcium (Ca²+), magnesium (Mg²+), and sodium (Na+) ions resulting in an EC >4 dS m-¹, corresponding to an osmotic potential of 0.2 MPa induced by sodium chloride (NaCl) at 40 mmol. A more comprehensive definition by Corwin and Scudiero (2019) describes saline soils as containing high concentrations of readily dissociable salts, including free ions (e.g., Na+, K+, Mg²+, Ca²+, Cl-, NO₃-, SO₄²-, CO₃²-, HCO₃-) and ion pairs.

The criteria for delineating salt-affected soils are influenced by regional environmental conditions, leading to the development of both global and localized classification frameworks. Soil solution EC serves as a primary diagnostic parameter, given its direct correlation with dissolved salt conce-ntrations (Mane et al., 2024). However, threshold EC values for saline soil classification vary among international standards, with some institutions adopting 2 dS m-¹ while others, including the USSL, employ 4 dS m-¹ (Paul and Rashid, 2017). Classification methodologies are further shaped by the predominant salt species, their concentrations, and the physicochemical properties of the affected soil, all of which inform salinity diagnostics and mitigation strategies (Chhabra, 2021). Prominent classification systems include the Russian Soil Classification System (RSCS), the U.S. Salinity Laboratory (USSL) guidelines (Table 6), the Australian Soil Classification (ASC), and the FAO World Reference Base (WRB).

Soil salinity represents one of the most critical environmental and agricultural challenges, particularly in arid and semi-arid regions, where it contributes to land degradation and substantial reductions in crop productivity. Lekka et al. (2024) emphasized that salinization is exacerbated by anthropogenic activities such as intensive irrigation and natural processes, including climate change, which may further intensify salinity stress in the future. The primary mechanism of crop inhibition under saline conditions involves osmotic stress, which impedes water uptake and disrupts nutrient assimilation, ultimately impairing plant growth and yield. Empirical studies suggest that agricultural output in salt-affected regions may decline by 20–60%, posing a significant threat to global food security (Eswar et al., 2021).

 

Table 6: Classification of Salt-affected soils by US Salinity Laboratory (Richard, 1954).

Soil classification	ECe (dS m-1)	pH	ESP (%)
Non-saline	4>	8.5>	15>
Saline	4<	8.5>	15>
Saline - sodic	4<	8.5>	15<
sodic	4>	8.5<	15<

 

Soil salinity adversely alters soil chemical properties through diverse mechanisms, including elevated osmotic pressure, accumulation of phytotoxic ions (e.g., sodium and chloride), nutrient imbalances, and competition for adsorption sites, collectively impairing plant physiological functions (Joshi et al., 2022). Additionally, salinity degrades soil physical properties, diminishing its water retention capacity and hydraulic conductivity (Tedeschi et al., 2023). Mahmoud and Shatha (2019) reported that excessive salinity reduces nutrient bioa-vailability by immobilizing potassium in the soil matrix, thereby restricting its release and subsequent plant uptake. In arid and semi-arid regions, soil salinity exacerbates due to Anthro-pogenic and climatic factors, including groun-dwater table elevation, shallow soil profiles, and intensified evaporation rates driven by elevated temperatures particularly following the replace-ment of deep-rooted perennial vegetation with shallow-rooted annual crops (Hailu and Mehari, 2021). Etesami and Noori (2023) further demonstrated that saline conditions induce severe osmotic stress in plants, severely limiting water absorption and resulting in physiological drought, ionic toxicity, nutrient deprivation, and subs-tantial yield reductions. Van et al. classified the impact of different soil salinity levels on crop growth and productivity (Demo et al., 2025) (Table 7).

 

Table 7: Soil salinity levels and crop impact.

Class	EC (dS m-¹)	Effect on crops
Non-saline	0 – 2	No adverse effect on crops.
Slightly saline	2 – 4	Yield reduction in sensitive crops.
Moderately saline	4 – 8	Most crops are affected by reduced productivity.
Highly saline	8 – 16	All crops are affected; only salt-tolerant varieties may produce.
Extremely saline	> 16	Suitable only for halophytic plants (halophytes).

 

Spectral indicators

Spectral indices are mathematical formulations derived from reflectance measurements across distinct regions of the electromagnetic spectrum (e.g., visible and infrared bands). These indices serve as critical tools in remote sensing for characterizing surface features, including veget-ation, water bodies, soil properties, and urban infrastructure. Their efficacy stems from the unique spectral signatures of individual components, which arise from their interactions with electromagnetic radiation, thereby enabling precise discrimination of surface constituents.

In soil science, spectral indices have been widely adopted to assess soil degradation processes, such as salinization and contaminant accumulation (Nadporozhskaya et al., 2022). Recent studies have identified 44 established spectral indices utilized in soil-environment research, classified into categories such as salinity, vegetation, soil comp-osition, and oxide indices (Wang et al., 2024). Furthermore, Esmaili (2021) demonstrated the feasibility of developing predictive mathematical models and machine learning algorithms that correlate spectral data with pollutant conce-ntrations, offering a viable alternative to conve-ntional laboratory-based analyses. However, the accuracy of these models is contingent upon soil type and localized environmental conditions, underscoring the necessity for region-specific calibration.

The integration of multi-platform remote sensing data, particularly the fusion of high-resolution airborne sensor readings with satellite imagery, has significantly enhanced spatial resolution and coverage. Concurrent advancements in sensor tech-nology and artificial intelligence (AI) are further optimizing the robustness and applicability of spectral indices in environmental monitoring.

Spectral indicators and their relationship to soil salinity

Soil salinity has emerged as a critical global environmental challenge, particularly in semi-arid and arid regions, where escalating salinity levels adversely impact plant growth, agricultural productivity, and soil degradation. The severity and spatial distribution of soil salinization are influenced by a complex interplay of natural and anthropogenic factors, necessitating robust monitoring and assessment of salt-affected soils to formulate effective reclamation strategies. However, the inherent spatial and temporal variability of salinity complicates these efforts.

Remote sensing techniques, particularly multis-pectral satellite imagery (e.g., Landsat TM, ETM+, OLI; Sentinel-2 MSI; MODIS; ASTER), offer a scalable solution for large-scale soil salinity estimation through spectral indices. Empirical studies have demonstrated significant correlations between spectrally derived indices and laboratory-measured electrical conductivity (EC) (Mehla et al., 2024). Wang et al. (2024) emphasized the efficacy of spectral salinity indices in monitoring soil salinity via visible and near-infrared reflectance analysis, facilitating precise salinity mapping, temporal monitoring of salt accumulation, and data-driven agricultural management. The accuracy of these indices hinges on the spectral resolution and radiometric fidelity of satellite data (Table 8).

Conducted a comparative evaluation of spectral indices against field-measured EC, identifying the SI3 and NDSI indices as the most robust for salinity mapping. Gorji et al. (2020) validated the utility of Sentinel-2A and Landsat-8 OLI-derived indices, achieving high predictive accuracy (R² = 0.73–0.74). Similarly, Nguyen et al. (2020) confirmed Landsat-8 OLI’s capability for spatiotemporal salinity assessment, with the NIR-based VSSI index exhibiting a strong correlation with EC (R² = 0.89). Kilic et al. (2022) reported the highest EC sensitivity in SWIR bands using Landsat 5 TM data.

 

Table 8: Spectral salinity indices (Yang et al., 2023; Wang et al., 2024).

Formula	Salinity indices
(G × R)1/2	SI (Salinity indices)
(G + R)1/2	SI1 (Salinity indices)
(NIR2 + G2 + R2)1/2	SI2 (Salinity indices)
(G2 + R2)1/2	SI3 (Salinity indices)
SWIR1/NIR	SI4 (Salinity indices)
B/R	S1 (Salinity indices)
B-R) / (B+R))	S2 (Salinity indices)
G × R / B	S3 (Salinity indices)
B × R / G	S5 (Salinity indices)
R × NIR / G	S6 (Salinity indices)
SWIR1-SWIR2 / SWIR1+SWIR2	S7 (Salinity indices)
G + R) /2)	S8 (Salinity indices)
(G + R + NIR)/2	S9 (Salinity indices)
R / NIR ×100	SI-T (Salinity indices)
R - NIR	SSSI-1 (Soil Salinity and Sodicity indices1)
(R – NIR – NIR × NIR)/R	SSSI-2 (Soil Salinity and Sodicity indices2)
(NIR – SWIR1) / (NIR + SWIR1)	NDSI (Normalized difference Salinity indices)
[(NIR×R – G ×B) / (NIR×R + G×B)]1/2	CRSI (Canopy response Salinity indices)
[(NDVI – 1)2 + SI2]1/2	SRSI (Salinization remote sensing indices)
(G – NIR) / (B+NIR)	SAIO (Salinity ratio index)
G2 / (B × SWIR1)	ERSSI (Enhanced residues Soil Salinity index)
2 × G – 5 × (R + NIR)	VSSI (Vegetation Soil Salinity index)
(R2 + NIR2)1/2	BI (Brightness index)
where: B (Blue), G (Green), R (Red), NIR (Near Infrared), and SWIR (Shortwave Infrared).

 

In a comprehensive comparative study, Cui et al. (2023) evaluated 17 vegetation indices (VIs) and 13 salinity indices (SIs), concluding that VIs outperformed SIs (R² = 0.71). Voitik et al. (2023) highlighted the superior performance of NDSI in detecting soil variability, while Haoyuan Yin (2023) demonstrated enhanced accuracy through multi-sensor data fusion. Yang Han et al. (2023) further corroborated strong correlations between Landsat 8-derived SIs and laboratory-measured salinity.

Recent advancements include the integration of multi-source data, as exemplified by Kumar et al. (2024), who combined Landsat imagery with in-situ measurements to improve salinity mapping. Salem and Jia (2024) provided additional validation for the reliability of spectral indices, whereas Rakhmanov et al. (2024) investigated the relationship between irrigation practices and salinity using NDVI, SI, and NDSI, with NDSI exhibiting the strongest correlation. Hossain et al. (2025) identified an inverse relationship between VIs and soil salinity, underscoring the utility of vegetation-based indices.

In the context of Iraq, Al-Atabi (2017) reported a significant correlation between spectral indices and EC (r = 0.68), while Al-Jubouri (2019) explored the spectral signatures of different salt types. Naser (2021) observed elevated reflectance in saline soils, and Naser and Kusay (2022) validated the effectiveness of SI7 and NDSI in classifying salt-affected soils. Hadid and Ahmed (2024) docu-mented a progressive increase in salinity in Diyala (2016–2022), and Al-Jumaili (2025) reported an 8.4% rise in soil salinity in Fallujah (2014–2024), indicative of ongoing agricultural land degradation.

Spectral indicators and their relationship to soil chemical contamination

Heavy metal contamination in agricultural soils, resulting from anthropogenic activities, poses a significant threat to crop safety and ecosystem health, underscoring the need for efficient mitigation strategies. Accurate spatial mapping of contaminated areas is essential, necessitating the development of advanced monitoring techniques. In this regard, spectral indices derived from satellite remote sensing have emerged as a promising approach for predicting heavy metal concentrations in soils, offering a cost-effective and scalable alternative to conventional laboratory analyses.

The visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) spectral regions are particularly effective for detecting soil heavy metal contamination due to their high sensitivity to soil composition and vegetation health. Studies have demonstrated that spectral reflectance data can indirectly predict low to moderate heavy metal concentrations by analyzing their associations with soil organic carbon, iron oxides/hydroxides, and clay minerals (Cao et al., 2020) or through their phytotoxic effects on vegetation

 

Table 9: Spectral indices used for the indirect prediction of heavy metals in soil

Cited	Formula	Spectral Index
Lillesand et al., 2015	(NIR-Red)/(NIR+Red)	NDVI (Normalized Difference Vegetation Index)
	2.5(NIR-Red)/(NIR+0.6×red-7.5×blue+1)	EVI (Enhanced Vegetation Index)
Eid et al., 2020	(Green-NIR)/(Green+NIR)	MNDWI (Modified Normalized Difference Water Index)
Omondi and Boitt, ٢٠٢٠	(CIred-edge)/PSRI	HMSSI (Heavy Metal Stress-Sensitive Index)
	(R680-R500)/R750	PSRI (Plant Senescence Reflectance Index)
	(R783/R705) - 1	CIred-edge (Chlorophyll Index)
	B8 × B4	WDVI (Weighted Difference Vegetation Index)
	((B8 × B4)/ B8 × B4+L)×(1+L)	SAVI (Soil Adjusted Vegetation Index)
Han et al., 2021	(Ri - Rj) / (Ri + Rj)	NDSI (Normalized Difference Spectral Index)
Liu, 2024	(a/b)	HMSI (Heavy Metal Stress Index)
Jugnee et al., 2025	(B11/B12)	CLI (Clay Index)
	((B11+B4)-(B8+B2)/ (B11+B4)-(B8+B2))×100+100	BSI (Bare Soil Index)
	(BB42/(B2×B43)	RI (Redness Index)
	(B11-B12)/(B11+B12)	MID-IR (MID-Infrared Index)
	(B8-B12)/(B8+B12)	NDI (Normalized Difference Index)

 

(Gholizadeh and Kopacková, 2019). Successfully utilized visible and near-infrared (VNIR) and SWIR spectroscopy to estimate concentrations of chromium, arsenic, cadmium, copper, lead, nickel, and zinc in soils.

Traditional soil heavy metal detection methods are often labor-intensive, expensive, and impractical for large-scale monitoring, particularly in resource-limited regions. Satellite-based spectral analysis presents a viable solution, with platforms such as Landsat providing high-accuracy predictive capabilities for spatial distribution studies (Anthony, 2023). Visible and near-infrared refle-ctance (Vis-NIR) spectroscopy has proven partic-ularly effective in estimating soil heavy metal content (Chen et al., 2022). For instance, Shi et al. (2014) demonstrated the utility of the Normalized Difference Spectral Index (NDSI) in rice plants for arsenic detection, leveraging correlations between soil arsenic levels and chlorophyll a/b ratios and leaf cellular structure. Hyperspectral satellite data have also been employed for arsenic pollution detection (Agrawal and Petersen, 2021), while Zhao et al. (2022) identified five lead-sensitive spectral bands (522 nm, 1668 nm, 2207 nm, 2296 nm, and 2345 nm) using VNIR-SWIR data.

Recent advancements in spectral index devel-opment have further enhanced predictive accuracy. Omondi and Boitt (2020) derived four indices from Sentinel-2 imagery Heavy Metal Stress Sensitive Index (HMSSI), Soil-Adjusted Vegetation Index (SAVI), Weighted Difference Vegetation Index (WDVI), and Normalized Difference Vegetation Index (NDVI) which significantly improved heavy metal concentration predictions (Table 9). Their findings indicated that NDVI outperformed other indices for zinc and lead estimation, while HMSSI and NDVI were most effective for cadmium. Similarly, Liu et al. (2021) developed predictive models for arsenic, lead, and cadmium using five spectral bands (B2, B4, NIR, DVI, and NDSI), with lead exhibiting the highest estimation accuracy.

Innovative multi-band spectral indices have been introduced to optimize heavy metal detection in varying environments. Proposed the Vegetation Index based on Greenness and Short-wave infrared (VIGS) to identify vegetation anomalies in heavily vegetated areas using Landsat 7 data. Wu et al. (2019) developed the Heavy metal Cadmium Stress Index (HCSI) for Sentinel-2, based on chlorophyll sensitivity to cadmium stress in rice. Further refinements include Li et al. (2019)’s modified Normalized Vegetation Index (NVI) for lead detection and Liu (2024)’s Standardized Heavy Metal Stress Index (HMSI), which achieved a strong Pearson correlation (r = 0.84) between Sentinel-2 spectral data and plant traits affected by heavy metals.

Xu et al. (2021) identified an optimal spectral band combination for predicting mercury (Hg), chromium (Cr), and copper (Cu) concentrations by evaluating

 

Table 10: Predictive models of heavy metals in soil based on spectral bands

Reference	R2	Prediction model
Shi et al., 2014	0.68	CAs= 1930 × NDSI (R812, R782) + 16.5
Mirzaei et al., 2021	0.72	CPb= 159.47(B2) +26.77 (B3/B8) +54.09(B11/B6) -102.77
	0.65	CCu= 2.072 – 10.786(B3/B8) +59.887 (B4) +18.548(B11/B6)
	0.59	CNi= 26.747 – 5.26(B3/B8) +6.188(B6/B8) +15.673(B3)
Liu et al., 2021	0.89	CAs= 40.65 + 18.59 × DVI – 178.33 ×NDSI + 3.6 × Blue – 10.23 × Red + 11.6 × NIR
	0.89	CPb= 102.7 + 54.18 × DVI – 480.8 ×NDSI + 16.7 × Blue – 44.33 × Red + 154.6 × NIR
	0.86	CCd= 0.27 + 0.07 × DVI – 0.55 ×NDSI + 0.032 × Blue – 0.047 × Red + 0.116 × NIR
Felegari et al., 2023	0.76	CPb= 147.31(B2) +28.17 (B3/B6) +58.12(B11/B6) -110.96
	0.80	CCu= 1.98 – 9.48(B3/B8) +98.21 (B4) +19.23(B11/B6)
	0.72	CZn= 29.1 – 6.37(B3/B8) +8.21(B6/B8) +110.41(B3)

 

five dual-band spectral indices: the Ratio Index (RI), Normalized Difference Index (NDI), Difference Index (DI), Product Index (PI), and Sum Index (SI). Similarly, Lassalle et al. (2021) optimized the Normalized Difference Vegetation Index (NDVI) for four heavy metals chromium (Cr), copper (Cu), nickel (Ni), and zinc (Zn) by analyzing their correlations with red and near-infrared (NIR) spectral bands. Han et al. (2021) demonstrated that multiband spectral indices exhibit significantly stronger correlations with nickel (Ni), mercury (Hg), chromium (Cr), copper (Cu), and arsenic (As) concentrations in agricultural soils compared to single-band indices. Moreover, Chen et al. (2022) established that triple-band spectral indices provide the highest predictive accuracy for soil heavy metal concentrations. Table 10 summarizes the correlation coefficients between spectral indices and bands in predictive models for contaminated soil heavy metals.

Felegari et al. (2023) employed Sentinel-2 satellite imagery integrated with stepwise regression analysis in ArcGIS to assess spatial variations in lead (Pb), copper (Cu), and zinc (Zn) concentrations and their associations with spectral indices. Additionally, Jugnee et al. (2025) developed robust predictive models for heavy metal contamination in rice paddy soils using a suite of spectral indices, including the Clay Index (CLI), Bare Soil Index (BSI), Redness Index (RI), Mid-Infrared Index (MID-IR), Brightness Index (BI), Normalized Difference Index (NDI), and the Short-Wave Infrared to Near-Infrared Ratio (SWIR1/NIR).

Conclusions

• Heavy metal pollution (Pb, Cd, As) and soil salinity driven by industrial, agricultural, and natural processes significantly reduce agricultural productivity, especially in arid regions.
• Spectral indices (NIR, SWIR) from Sentinel-2 enable cost-effective, large-scale monitoring of soil pollution and salinity, surpassing traditional lab methods in coverage and efficiency.
• Pollution indices (PLI, Igeo) quantify soil contamination, while salinity is measured via electrical conductivity (EC). These pollutants bioaccumulate in crops, increasing health risks (e.g., cancer, kidney disease) through the food chain.

Recommendations

• Integrate AI-enhanced remote sensing with ground-truth data (e.g., EC measurements, lab assays) for accurate, real-time soil health monitoring and predictive mapping.
• Enforce strict regulations on industrial/agricultural pollutants while promoting sustainable alternatives like drip irrigation and salt-tolerant crops.
• Implement phytoremediation for heavy metals and gypsum for saline soils, supported by early-warning systems using spectral indices to detect degradation risks.
• Develop AI-driven multi-sensor systems for precision soil monitoring, coupled with farmer education programs to translate data into actionable sustainable practices.

Acknowledgement

My gratitude to my supervisors Dr. Al Maamouri and Dr. Dwenee and Mrs. Najwa Almajid for providing the necessary facilities and time to complete Review Article.

Novelty Statement

The novelty of this review article lies in its potential to monitor agricultural soil degradation by pollutants (heavy metals and salt) through spectral evidence, without the need for field visits and laboratory analysis. This is an unprecedented approach in monitoring soil contamination by heavy metals and salinity, relying on spectral evidence. Consequently, it reduces the effort and financial costs required for predicting and mapping these pollutants in agricultural soils.

Author’s Contribution

Mazin Fadhil Khudhair: Conceptualization, Methodology, Article preparation, Review and editing, Supervision.

Abdul Baqi D.S. Al Maamouri: Conceptualization, Methodology, Investigation, Validation, Supervision.

Sadeq Jaafar Hassan Dwenee: Conceptualization, Methodology, Supervision.

Generative AI and AI-assisted technology statement

Generative AI and AI-assisted technologies were used only to assist in searching for relevant references. The authors are fully responsible for the selection, interpretation, and use of all sources cited in this manuscript.

Conflict of interest

The authors have no conflict of interest.

References

Abbas, A.K. 2018. The Effect of Vapors and Gases from Al-Furat Chemical Factories and Al-Saddah Cement Plant in Babil Province on Soil, Water, and Plant Pollution with Lead, Nickel, and Cadmium Elements. PhD Thesis. Department of Soil and Water Resources, College of Agricultural Engineering Sciences, University of Baghdad.

Abdul Latif, A.A. 2020. Heavy and Radioactive Element Pollution in Soils and Plants of Baghdad City. PhD Thesis. Department of Soil and Water Resources, College of Agricultural Engineering Sciences, University of Baghdad.

Abdullahi, A., M.A. Lawal, and A.M. Salisu. 2021. Heavy metals in contaminated soil: source, accumulation, health risk and remediation process. Bayero J. Pure Appl. Sci., 14(1): 1–12. https://doi.org/10.4314/bajopas.v14i1.1

Agrawal, A. and M.R. Petersen. 2021. Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning. Toxic., 9(12): 333. https://doi.org/10.3390/TOXICS9120333

Al Maamouri, A.B.D. and H.A.S. Salwa. 2024. Effect of mineral composition on sequential extract of lead and cadmium in some iraqi soils. Iraqi J. Agric. Sci., 55(6): 2060–2073. https://doi.org/10.36103/K8MC7Q15

Al-Atabi, J. and H. Sh. 2017. Monitoring Spatial Variability of Soil Salinity in the Al-Sheikh Saad Project, Wasit Governorate, Using Geospatial Technologies and the EM38 Device. PhD Dissertation. Department of Soil and Water Resources, College of Agricultural Engineering Sciences, University of Baghdad.

Alengebawy, A., S.T. Abdelkhalek, S.R. Qureshi, and M.Q. Wang. 2021. Heavy Metals and Pesticides Toxicity in Agricultural Soil and Plants: Ecological Risks and Human Health Implications. Toxic., 9(3): 42.

AL-Heety, L.F.D., O.M. Hasan, and E.A.M. Salih Al-Heety. 2021. Heavy metal pollution and ecological risk assessment in soils adjacent to electrical generators in Ramadi city, Iraq. Iraqi J. Sci., 62(4): 1077–1087.

Ali, H., E. Khan, and I. Ilahi. 2019. Environmental Chemistry and Ecotoxicology of Hazardous Heavy Metals: Environmental Persistence, Toxicity, and Bioaccumulation.

Ali, N.A. and E.J. Abdullah. 2024. Distribution Of Some Heavy Metals in Soils of Abu-Ghraib Land, Baghdad, Iraq. Iraqi J. Sci., 65(12): 7021–7035.

Alias, N., S.A. Rosli, N.A.H. Sazalli, H.A. Hamid, S. Arivalakan, S.N.H. Umar, B.K. Khim, B.N. Taib, Y.K. Keat, K.A. Razak, Y.F. Yee, Z. Hussain, E.A. Bakar, N.F. Kamaruddin, A.A. Manaf, N. Uchiyama, T.W. Kian, A. Matsuda, G. Kawamura, … Z. Lockman. 2020. Metal oxide for heavy metal detection and removal. Metal Oxide Powder Technologies: Fundamentals, Processing Methods and Applications., 299–332.

Al-Jubouri, A.K.S. 2019. Study of the Spectral Reflectance of the Red and Near-Infrared Bands on Some Vegetation Cover Characteristics in Salinity-Affected Soils at Al-Salamiyah Project / Baghdad. Master’s Thesis. Department of Soil and Water Resources, College of Agricultural Engineering Sciences, University of Baghdad.

Al-Jumaili, S.A.J. 2025. Using Spectral Indices (NDVI, SI, BCI, LDI) to Assess Land Conditions Between 2014–2024 in the Center of Fallujah District. Ournal of Educational and Scientific Studies – College of Education – Iraqi University., 4(25): 95–114.

Al-Mashhadani, M. 2024. Study of Hydrocarbon and Heavy Metal Pollution and Their Impact on Soil and Water Characteristics in Some Oil Fields in Southern Iraq. Ph.D. Dissertation. Department of Soil Science and Water Resources. College of Agricultural Engineering Sciences. University of Baghdad.

Al-Mashhadi, M.J.H. and L.A.S. Alabadi. 2023. Investigating the Efficiency of (Alhagi graecorum) Plant in Accumulating some Heavy Metals. IOP Conference Series: Earth Environ. Sci., 1214(1).

Al-Rawi, S.A.D.A. and S.K.A. Razzak. 2020. Sources of heavy minerals of the neogene clastics at Bekhme, Northern Iraq. Iraqi J. Sci., 61(10): 2610–2618.

Angon, P.B., M.S. Islam, S. KC, A. Das, N. Anjum, A. Poudel, and S.A. Suchi. 2024. Sources, effects and present perspectives of heavy metals contamination: Soil, plants and human food chain. In Heliyon., 10(7). https://doi.org/10.1016/j.heliyon.2024.e28357

Anthony, T. 2023. Assessment of Heavy Metal Contamination in Wetlands Soils Around an Industrial Area Using Combined GIS-Based Pollution Indices and Remote Sensing Techniques. Air, Soil Water Res., 16. https://doi.org/10.1177/11786221231214062.

Aslam, A., A. Naz, S.S.H. Shah, F. Rasheed, R. Naz, A. Kalsom, N. Mukhtar, A. Niaz, M. Aftab, I. Rasheed, M.A.U. Haq, M. Arif, A. Sattar, and S. Hussain. 2023. Heavy metals contamination in vegetables irrigated with wastewater: a case study of underdeveloping regions of Pakistan. Environ.l Geochem. Health., 45(12): 8911–8927.

Aslam, M.A., M.S. Abbas, M. Mustaqeem, M. Bashir, A. Shabbir, M.T. Saeed, and R.M. Irfan. 2024. Comprehensive assessment of heavy metal contamination in soil-plant systems and health risks from wastewater-irrigated vegetables. Colloids and Surfaces C: Environ. Aspect., 2: 100044.

Atta, M.I., S.S. Zehra, D.Q. Dai, H. Ali, K. Naveed, I. Ali, M. Sarwar, B. Ali, R. Iqbal, S. Bawazeer, U.K. Abdel-Hameed, and I. Ali. 2023. Amassing of heavy metals in soils, vegetables and crop plants irrigated with wastewater: Health risk assessment of heavy metals in Dera Ghazi Khan, Punjab, Pakistan. Front. Plant Sci., 13.

Babuji, P., S. Thirumalaisamy, K. Duraisamy, and G. Periyasamy. 2023. Human Health Risks due to Exposure to Water Pollution: A Review. In Water (Switzerland) Multidisciplinary Digital Publishing Institute (MDPI)., 15(14). https://doi.org/10.3390/w15142532

Baker, A.J.M., and R.R. Brooks. 1989. Terrestrial Higher Plants which Hyperaccumulate Metallic Elements. A Review of Their Distribution, Ecology and Phytochemistry. Biorecov., 1: 81–126. https://speciation.net/Database/Journals/Biorecovery-;i3416

Basha, N.T. and M.J. Issa. 2025. Determine the Pollution Indices of the Soil Near the Najaf Refinery in the Middle of Iraq. Iraqi J. Sci., 66(1): 219–229.

Briffa, J., E. Sinagra, and R. Blundell. 2020. Heavy metal pollution in the environment and their toxicological effects on humans. In Heliyon. Elsevier Ltd., 6(9)

Brusseau, M.L. and J.F. Artiola. 2019. Chemical Contaminants. Environ. Pollution Sci., 175–190.

Cachada, A., T.A.P. Rocha-Santos, and A.C. Duarte. 2017. Soil and pollution: An introduction to the main issues. In Soil Pollution: Monit. Remed. Elsevier., 1–28. https://doi.org/10.1016/B978-0-12-849873-6.00001-7

Cao, J., C. Li, Q. Wu and J. Qiao. 2020. Improved Mapping of Soil Heavy Metals Using a Vis-NIR Spectroscopy Index in an Agricultural Area of Eastern China. IEEE., 8: 42584–42594. https;//doi.org/10.1109/ACCESS.2020.2076902

Chen, L., J. Lai, K. Tan, X. Wang, Y. Chen, and J. Ding. 2022. Development of a soil heavy metal estimation method based on a spectral index: Combining fractional-order derivative pretreatment and the absorption mechanism. Sci. Total Environ., 813: 151882. https://doi.org/10.1016/J.SCITOTENV.2021.151882

Chester, R. and J.H. Stoner. 1973. Pb in particulates from the lower atmosphere of the Eastern Atlantic. Nature., 245(5419): 27–28. https://doi.org/10.1038/245027b0

Chhabra, R. 2021. Nature and Origin of Salts, Classification, Area and Distribution of Salt-affected Soils. Salt-Affect. Soil Marginal Water., 1–47. https://doi.org/10.1007/978-3-030-78435-5_1

Chorol, L. and S.K. Gupta. 2023. Evaluation of groundwater heavy metal pollution index through analytical hierarchy process and its health risk assessment via Monte Carlo simulation. Process Safety Environ. Protect., 170: 855–864.

Chyad, A.A. A.M. Saeed, and A.S. Alhendi. 2022. Determination of Heavy Metals in Irrigation Water, Soil, Paddy, and Produced Rice of Some Paddy Fields of Iraq. Iraqi J. Sci., 63(11): 4637–4649. https://doi.org/10.24996/ijs.2022.63.11.2

Corwin, D.L. and E. Scudiero. 2019. Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors. Advan. Agron., 158: 1–130. https://doi.org/10.1016/BS.AGRON.2019.07.001

Cui, X., W. Han, H. Zhang, Y. Dong, W. Ma, X. Zhai, L. Zhang, and G. Li. 2023. Estimating and mapping the dynamics of soil salinity under different crop types using Sentinel-2 satellite imagery. Geoderma., 440: 116738. https://doi.org/10.1016/J.GEODERMA.2023.116738

Dagdag, O., T.W. Quadri, R. Haldhar, S.C. Kim, W. Daoudi, E. Berdimurodov, E.D. Akpan, and E.E. Ebenso. 2023. An Overview of Heavy Metal Pollution and Control. In ACS Symposium Series., 1456: 3-24. American Chemical Society. https://doi.org/10.1021/bk-2023-1456.ch001

Darwesh, O.M. and I.A. Matter. 2021. Nanomaterials in the biological treatment of contaminated soil. Nanomater. Soil Remediat., 285–300.

Demo, A.H., M.K. Gemeda, D.R. Abdo, T.N. Guluma, and D.B. Adugna. 2025. Impact of soil salinity, sodicity, and irrigation water salinity on crop production and coping mechanism in areas of dryland farming. Agrosyst. Geosci. Environ., 8(1): e70072.

Devkota, K.P., M. Devkota, M. Rezaei, and R. Oosterbaan. 2022. Managing salinity for sustainable agricultural production in salt-affected soils of irrigated drylands. Agric. Syst., 198: 103390.

Eskandari, E., M. Kosari, M.H. Davood Abadi Farahani, N.D. Khiavi, M. Saeedikhani, R. Katal, and M. Zarinejad. 2020. A review on polyaniline-based materials applications in heavy metals removal and catalytic processes. Separat. Purificat. Technol., 231: 115901.

Esmaili, R.B. 2021. Earth observation using Python: A practical programming guide. Earth Observation Using Python: A Practical Programm. Guide., 1–287.

Eswar, D., R. Karuppusamy, and S. Chellamuthu. 2021. Drivers of soil salinity and their correlation with climate change. Curr. Opin. Environ. Sustain., 50: 310–318. https://doi.org/10.1016/J.COSUST.2020.10.015

Etesami, H. and F. Noori. 2019. Soil Salinity as a Challenge for Sustainable Agriculture and Bacterial-Mediated Alleviation of Salinity Stress in Crop Plants. Saline Soil-Based Agric. Halotoler. Microorgan., 1–22.

Farhan, B.H. 2020. Study of Soil, Water, and Plant Pollution by Some Heavy Elements from Industrial Waste in Wasit Province. PhD Thesis. Department of Soil Science and Water Resources. College Agric. Engineer. Sci. University Baghdad.

Fayhaa Muhammad Najm, Hind Tahseen Ibrahim, Raed Amer Ali Alsahoo, Ibrahim Ayad Jihad, R.M.A.Z. Qais. 2025. oRGINAL article. J. Animal Health Product., 13(s1): 39–48. https://doi.org/https://dx.doi.org/10.17582/journal.jahp/2024/13.s1.39.48

Felegari, S., A. Sharifi, M. Khosravi, S. Sabanov, A. Tariq, and S. Karuppannan. 2023. Using Sentinel-2 data to estimate the concentration of heavy metals caused by industrial activities in Ust-Kamenogorsk, Northeastern Kazakhstan. Heliyon., 9(11).

Gholizadeh, A. and V. Kopačková. 2019. Detecting vegetation stress as a soil contamination proxy: a review of optical proximal and remote sensing techniques. Int. J. Environ. Sci. Technol., 16(5): 2511–2524. https://doi.org/10.1007/s13762-019-02310-w

Gorji, T., A. Yildirim, N. Hamzehpour, A. Tanik, and E. Sertel. 2020. Soil salinity analysis of Urmia Lake Basin using Landsat-8 OLI and Sentinel-2A based spectral indices and electrical conductivity measurements. Ecolog. Indicat., 112: 106173. https://doi.org/10.1016/J.ECOLIND.2020.106173

Hadid, R.S. and B.A. Ahmed. 2024. Determining The Agricultural Drought and Desertification Intensity in Diyala Province / Iraq Using Sentinel-2 images. Iraqi J. Sci., 65(5): 2946–2957. https://doi.org/10.24996/IJS.2024.65.5.45

Hailu, B. and H. Mehari. 2021. Impacts of Soil Salinity/Sodicity on Soil-Water Relations and Plant Growth in Dry Land Areas: A Review. J. Natur. Sci. Res., 21: 1–10. https://doi.org/10.7176/JNSR/12-3-01

Hakanson, L. 1980. An ecological risk index for aquatic pollution control.a sedimentological approach. Water Res., 14(8): 975–1001. https://doi.org/10.1016/0043-1354(80)90143-8

Han, C., J. Lu, S. Chen, X. Xu, Z. Wang, Z. Pei, Y. Zhang, and F. Li. 2021. Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices. Sustain., 2021 13(21): 12088. https://doi.org/10.3390/SU132112088

Haoyuan Yin, C.C.Y.H.J.J.Y.C.R. D.R.X. X.Z. Z.Z. 2023. Synergistic estimation of soil salinity based on Sentinel-1 image texture and Sentinel-2 salinity spectral indices. J. Appl. Remote Sensing., 1(17).

Harby, A.F., K.M. Naser, and I.M. Rasheed. 2023. Effect of Tigris Water Irrigation on Heavy Metal Contamination in Soil and Plant. Ind. J. Ecol., 50(6): 2031–2036.

Hossain, M.K. M.A. Kabir, M.O.F. Molla, M.A. Hossain, M.S. Khatun, M.N.J. Lima, S. Kumar, and R. Islam. 2025. Geospatial analysis of soil salinity dynamics: Exploring topographic and vegetation influences in coastal Bangladesh. Geosys. Geoenviron., 4(3): 100418. https://doi.org/10.1002/9781119606925

Lekka, C. S.E. Detsikas, G.P. Petropoulos, and C. Chalkias. 2024. Mapping and monitoring of salt-affected soils: the contribution of geoinformation. Remote Sens. Precision Agric. Transfor. Scient. Advanc. Innov., 71–91. https://doi.org/10.1016/B978-0-323-91068-2.00016-3

Humaira, A., U. Ali, K. Misbah Ullah, H. Shehla, U. Aman, A. Muhammad Ahsan, A. Ghulam, N.M.J. Nazia, K. Shahid, and A. Adeel. 2024. A Review on Heavy Metals in Ecosystems, Their Sources, Roles, and Impact on Plant Life. J. Genet. Med. Gene Ther., 7(1): 020–034.

Issa, M.J., H.M. Hussain, and I. Shaker, hadi. 2019. Assessment of the Toxic Elements Resulting from the Manufacture of Bricks on Air and Soil at Abu Smeache Area - Southwest Babylon governorate - Iraq. Iraqi J. Sci., 60(11): 2443–2456.

Joshi, S., J. Nath, A.K. Singh, A. Pareek, and R. Joshi. 2022. Ion Transporters and their regulatory signal transduction mechanisms for salinity tolerance in plants. Physiol. Plantar., 174(3).

Jugnee, U., L. Jiao, S. Dalantai, L. Huo, Y. An, B. Batsaikhan, U. Tsogtbaatar, M. Ulziibaatar, and B. Natsagdorj. 2025. A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models. Heliyon., 11(4): 42619.

Kerfahi, D., B.M. Tripathi, J.W.F. Slik, R.S. Sukri, S. Jaafar, K. Dong, M.C. Ogwu, H.K. Kim, and J.M. Adams. 2019. Soil Metagenome of Tropical White Sand Heath Forests in Borneo: What Functional Traits Are Associated with an Extreme Environment Within the Tropical Rainforest? Pedosphere., 29(1): 12–23. https://doi.org/10.1016/S1002-0160(18)60054-2

Kerpelis, P.N., S.K. Golfinopoulos, and D.E. Alexakis. 2025. Potential seismic impacts of wastewater treatment plants on soil or water pollution, due to internal vulnerabilities. Int. J. Environ. Sci. Technol.

Khudhair M. Fadhil, F.W. Ahmed, A.D. Baqi Salman, and W. Abd. 2018. Evaluation of Irrigation Water Quality Index for Tigris River and Pollution Levels of Heavy Metals in Baghdad. RJPBCS., 9(6): 993–1000.

Kilic, O.M., M. Budak, E. Gunal, N. Acir, R. Halbac-Cotoara-Zamfir, S. Alfarraj, and M.J. Ansari. 2022. Soil salinity assessment of a natural pasture using remote sensing techniques in central Anatolia, Turkey. PLOS ONE., 17(4): e0266915.

Kolawole A.S. and A.O. Iyiola. 2023. Environmental Pollution: Threats, Impact on Biodiversity, and Protection Strategies. In M. C. Izah Sylvester Chibueze and Ogwu (Ed.), Sustainable Utilization and Conservation of Africa’s Biological Resources and Environment., 377–409. Springer Nature Singapore. https://doi.org/10.1007/978-981-19-6974-4_14

Kong, Y., J. Liu, J. Pan, X. Ruan, and Wang. 2025. Migration characteristics and health risk assessment of heavy metals in soil-vegetable system in a typical wastewater irrigation area. J. Food Composit. Analys., 143: 107564.

Kumar, P., P. Tiwari, A. Biswas, and P. Kumar Srivastava. 2024. Spatio-temporal assessment of soil salinization utilizing remote sensing derivatives, and prediction modeling: Implications for sustainable development. Geosci. Front., 15(6): 101881. https://doi.org/10.1016/J.GSF.2024.101881

Lassalle, G., S. Fabre, A. Credoz, R. Hédacq, D. Dubucq, and A. Elger. 2021. Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices. Scient. Report., 11(1): 1–13.

Li, X., L. Li, and X. Liu. 2019. Collaborative inversion heavy metal stress in rice by using two-dimensional spectral feature space based on HJ-1 A HSI and radarsat-2 SAR remote sensing data. I. J. Appl. Earth Observ. Geoinfo., 78: 39–52. https://doi.org/10.1016/J.JAG.2018.12.011

Lillesand, T.M., R.W. Kiefer, & J.W. Chipman. 2015. NOAA POES SATELLITES. Remote Sens. Image Interpret., 360–363. https://doi.org/10.14358/PERS.81.8.615

Liu Meiling, G.C.Y.W. L.W.T.W. 2024. Combining radiative transfer model with statistical model for distinguishing heavy metal stress level in rice from Sentinel-2 satellite images. J. Appl. Remote Sensing., 18(٤). https://doi.org/10.1117/1.JRS.18.044516

Liu, L., X.B. Luo, L. Ding, and S.L. Luo. 2019. Application of Nanotechnology in the Removal of Heavy Metal From Water. Nanomat. Remov. Pollut. Resour Reutiliz., 83–147.

Liu, W., Q. Yu, T. Niu, L. Yang, and H. Liu. 2021. Inversion of Soil Heavy Metal Content Based on Spectral Characteristics of Peach Trees. Forest., 12(9): 1208.

Machado, de C.C., and R.J. Dinis-Oliveira. 2023. Clinical and Forensic Signs Resulting from Exposure to Heavy Metals and Other Chemical Elements of the Periodic Table. J. Clinical Med., 12(7): 2591.

Mahmood, M.B. 2016. Estimation the levels of some heavy metals in the soil and vegetables irrigated with wells water in some agriculture fields at Al-Dora district-Baghdad Iraqi J. Sci., 57(3B): 1918–1925.

Mahmoud, M.A. and Shatha Majed Al-Rubaai. 2019. Evaluation of the potassium availability in the saline soils using the traditional chemical criterion. Biochem. Cell. Arch., 19(1): 1407–1413. https://doi.org/10.35124/bca.2019.19.1.1407

Mane, S., N. Das, G. Singh, M. Cosh, and Y. Dong. 2024. Advancements in dielectric soil moisture sensor Calibration: A comprehensive review of methods and techniques. Comput. Electron. Agric., 218: 108686. https://doi.org/10.1016/J.COMPAG.2024.108686

Mehla, M.K., A. Kumar, S. Kumar, and R.K. Jhorar. 2024. Soil salinity assessment and mapping using spectral indices and geostatistical techniques - concepts and reviews. Remote Sensing of Soils: Mapping, Monit. Measur., 429–437. https://doi.org/10.1016/B978-0-443-18773-5.00019-3

Mirzaei, F., Y. Abbasi, & T. Sohrabi. 2021. Modeling the distribution of heavy metals in lands irrigated by wastewater using satellite images of Sentinel-2. Egypt. J. Remote Sens. Space Sci., 24(3): 537–546. https://doi.org/10.1016/J.EJRS.2021.03.002

Moghadas, B.K., H. Esmaeili, S. Tamjidi, and A. Geramifard. 2022. Advantages of nanoadsorbents, biosorbents, and nanobiosorbents for contaminant removal. Nano-Biosorbents Decontamin. Water, Air, Soil Pollut., 105–133. https://doi.org/10.1016/B978-0-323-90912-9.00006-X

Mohammed, M.D., T.K. Karyagdi, A.M. Qneed, I.A. Jihad, Q.R. Lahhob, M. Mudhafar, H.A. Alsailawi, & A.A. Ayada. 2025. Molecular characterization and zoonotic potential of giardia species in livestock with respect to their transmission dynamics and host adaptation. J. Anim. Health Prod., 13(s1): 411–421. https://doi.org/https://dx.doi.org/10.17582/journal.jahp/2025/13.s1.411.421

Morin-Crini, N., E. Lichtfouse, G. Liu, V. Balaram, A. Rita Lado Ribeiro, Z. Lu, F. Stock, E. Carmona, M. Ribau Teixeira, L.A. Picos-Corrales, J. Carlos Moreno-Piraján, L. Giraldo, C. Li, A. Pandey, D. Hocquet, G. Torri, and G. Crini. 2022. Worldwide cases of water pollution by emerging contaminants: a review Worldwide cases of water pollution by emerging contaminants: a review Worldwide cases of water pollution by emerging contaminants: a review. Environmental Chemistry Letters.

Müller, G. 1969. Index of geoaccumulation in sediments of the Rhine River. Geoj., 2(3): 108-118.

Müller, G. 1981. Die Schwermetallbelastung der sedimente des Neckars und seiner Nebenflusse: Eine Bestandsaufnahme. Chem. Zeitung., 105: 157–164.

Münzel, T., O. Hahad, A. Daiber, and P.J. Landrigan. 2023. Soil and water pollution and human health: what should cardiologists worry about? In Cardiovascular Research. Oxford University Press., 119(2): 440–449.

Muslim, R.I., M.J. Issa, and B.S. Al-Obaidi. 2019. Environmental assessment of heavy metals concentration and distribution in surface soils of wasit governorate/ Iraq. Iraqi J. Sci., 60(4): 803–818.

Nadporozhskaya, M., N. Kovsh, R. Paolesse, and L. Lvova. 2022. Recent Advances in Chemical Sensors for Soil Analysis: A Review. Chemosens., 10(1): 35.

Narayanan, M., N. Devarajan, Z. He, S. Kandasamy, V. Ashokkumar, R. Raja, and I.S. Carvalho. 2020. Assessment of microbial diversity and enumeration of metal tolerant autochthonous bacteria from tailings of magnesite and bauxite mines. Materials Today: Proceed., 33: 4391–4401.

Naser, K.M. 2019. Effect of ionic strength from different salt resources on boron adsorption in calcareous soil. Iraqi J. Agric. Sci., 50(6): 1512–1521.

Naser, K.M. 2025. Evaluation of pollution of Diyala river water quality by some heavy metals (Pb, Cu, Ni, Cd), boron and their effect on pollution of agricultural soils. Iraqi J. Agric. sci., 56(3): 1157–1169. https://doi.org/10.36103/CXH3N392

Naser, R.S. 2021. Determining the Ranges of Some Spectral Salinity and Vegetation Indices for Selected Soils of the Iraqi Alluvial Plain. Master’s Thesis. Depart. Soil Sci. Water Resour., College of Agricultural Engineering Sciences, University of Baghdad.

Naser, S. Raad and A. Kusay Wheib. 2022. Effect of Salt Composition on Spectral Reflectance and SalinityIndices of Soil. Indian J. Ecol., 49 Special (20): 75–82.

Nasser Mohamed Eid, A., C.O. Olatubara, T.A. Ewemoje, H. Farouk, & M.T. El-Hennawy. 2020. Coastal wetland vegetation features and digital Change Detection Mapping based on remotely sensed imagery: El-Burullus Lake, Egypt. Int. Soil Water Conserv. Res., 8(1): 66–79. https://doi.org/10.1016/J.ISWCR.2020.01.004

Nguyen, K.A., Y.A. Liou, H.P. Tran, P. Hoang, and T.H. Nguyen. 2020. Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam. Progr. Earth Planet. Sci., 7(1): 1–16.

Omondi, E. and M. Boitt. 2020. Modeling the Spatial Distribution of Soil Heavy Metals Using Random Forest Model—A Case Study of Nairobi and Thirirka Rivers’ Confluence. J. Geograp. Info. Syst., 12(6): 597–619.

Paul, B.K. and H. Rashid. 2017. Salinity Intrusion and Impacts. Clima. Hazards Coast. Bangladesh., 153–182.

Qadoori, A.H. 2025. The Treatment of Polluted Water by Heavy Metals in Maysan Governorate by Phytoremediation. Iraqi J. Sci., 66(3): 1109–1120.

Rahman, S., S. Mehta, and A. Husen. 2023. Plants and their unexpected response to environmental pollution: An overview. Plants and Their Interaction to Environmental Pollution: Damage Detection, Adaptation, Tolerance, Physiological and Molecular Responses., 1–23. https://doi.org/10.1016/B978-0-323-99978-6.00004-2

Rakhmanov D., Bořivoj Šarapatka, Kamilla Alibekova, Jan Černohorský, Petr Hekera, and Zhassulan Smanov. 2024. Assessment of agricultural land salinization via soil analysis and remote sensing data: Case study in Pavlodar region, Kazakhstan. Soil Water Res., 19(2).

Rashid, A., B.J. Schutte, A. Ulery, M.K. Deyholos, S. Sanogo, E.A. Lehnhoff, and L. Beck. 2023. Heavy Metal Contamination in Agricultural Soil: Environmental Pollutants Affecting Crop Health. Agron. MDPI., 10(6). https://doi.org/10.3390/agronomy13061521

Rashid, H., Ch. Arslan, and S.N. Khan. 2018. Wastewater irrigation, its impact on environment and health risk assessment in peri urban areas of punjab pakistan – a review. Environ. Contamin. Rev., 1(1): 30–35. https://doi.org/10.26480/ecr.01.2018.30.35

Richard, L.A. 1954. Diagnosis and Improvement of Saline and Alkali Soils: Vol. 78(2). Soil Science.

Rodríguez Eugenio, Natalie., M.J. McLaughlin, and D. Pennock, John. 2018. Soil pollution : a hidden reality. Food Agric. Organiz. United Nations. http://www.fao.org/3/I9183EN/i9183en.pdf

Safa S. Alzaidi and A. Mustafa Hassan. 2025. Assessment of Heavy Elements’ Pollution in Surface Water of Tigris River Between Baghdad and Kut cities, Iraq. Iraqi J. Sci., 66(4): 1632–1640.

Salem, O.H. and Z. Jia. 2024. Evaluation of Different Soil Salinity Indices Using Remote Sensing Techniques in Siwa Oasis, Egypt. Agron., 14(4): 723.

Sarma, H.H., A. Rajkumar, A. Baro, B.C. Das, and N. Talukdar. 2024. Impact of Heavy Metal Contamination on Soil and Crop Ecosystem with Advanced Techniques to Mitigate Them. J. Advan. Biol. Biotechnol., 27(6): 53–63.

Schmitz, K.S. 2018. Life Science. Physical Chemis., 755–832.

Sharafi, S. and F. Salehi. 2025. Comprehensive assessment of heavy metal (HMs) contamination and associated health risks in agricultural soils and groundwater proximal to industrial sites. Scient. Report., 15(1). https://doi.org/10.1038/s41598-025-91453-7

Shi, T., H. Liu, J. Wang, Y. Chen, T. Fei, and G. Wu. 2014. Monitoring arsenic contamination in agricultural soils with reflectance spectroscopy of rice plants. Environ. Sci. Technol., 48(11): 6264–6272.

Shref, A.M., A.B.D.S. Al Maamouri, M.F. Khudhair, and F.W. Ahmed. 2024. Effect of clay minerals, calcite, and organic matter on adsorption and desorption of cadmium and. Iraqi J. Agric. Sci., 55(5): 1657–1666. https://doi.org/10.36103/ZFGK8X49

Singh, J., P. Yadav, A.K. Pal, and V. Mishra. 2020. Water Pollutants: Origin and Status., 5–20.

Singh, R., P.K. Singh, P. Madheshiya, A.K. Khare, and S. Tiwari. 2024. Heavy metal contamination in the wastewater irrigated soil and bioaccumulation in cultivated vegetables: Assessment of human health risk. J. Food Composit. Analys., 128: 106054. https://doi.org/10.1016/J.JFCA.2024.106054

Souri, Z., P. Ratnakumar, A. Sharma, M.H. Siddiqui, R. Ue, U. Rabiya, M. Ali, M. Ansar Farooq, Z. Siddiq, S.A. Alamri, and W.D. Khan. 2022. Comparative efficiency of silica gel, biochar, and plant growth promoting bacteria on Cr and Pb availability to Solanum melongena L. in contaminated soil irrigated with wastewater.

Speight, J.G. 2017. Environmental inorganic chemistry for engineers. In Environmental Inorganic Chemistry for Engineers., Elsevier Inc. https://doi.org/10.1016/c2013-0-16023-0

Sutherland, R.A. 2000. Bed sediment-associated trace metals in an urban stream, Oahu, Hawaii. Environ. Geol., 39(6): 611–627. https://doi.org/10.1007/s002540050473

Taghavi, M., K. Bakhshi, A. Zarei, E. Hoseinzadeh, and A. Gholizadeh. 2024. Soil pollution indices and health risk assessment of metal(loid)s in the agricultural soil of pistachio orchards. Scient. Report., 14(1). https://doi.org/10.1038/s41598-024-59450-4

Tedeschi, A., M. Schillaci, and R. Balestrini. 2023. Mitigating the impact of soil salinity: recent developments and future strategies. Italian J. Agron., 18(2): 2173. https://doi.org/10.4081/IJA.2023.2173

Tomlinson, D.L., J.G. Wilson, C.R. Harris, and D.W. Jeffrey. 1980. Problems in the assessment of heavy-metal levels in estuaries and the formation of a pollution index. Helgoländer Meeresuntersuchun., 33(1–4): 566–575. https://doi.org/10.1007/BF02414780

Trapp, S. and M. Matthies. 1995. Generic One-Compartment Model for Uptake of Organic Chemicals by Foliar Vegetation. Environ. Sci. Technol., 29(9): 2333–2338. https://doi.org/10.1021/es00009a027

Uzakov, Z.Z., K.N. Karimov, Z.U. Uzakov, and R.A. Eshonkulov. 2023. Accumulation of Heavy Metals in Agricultural Crops and Ecological Series of Crops Placement. Baghdad Sci. J., 20(1): 58–73. https://doi.org/10.21123/bsj.2022.7210

Vasilachi, I.C., V. Stoleru, and M. Gavrilescu. 2023. Analysis of Heavy Metal Impacts on Cereal Crop Growth and Development in Contaminated Soils. In Agriculture (Switzerland). Multidisciplinary Digital Publishing Institute (MDPI)., 13(10). https://doi.org/10.3390/agriculture13101983

Vielee, S.T. and J.P. Wise. 2023. Among Gerontogens, Heavy Metals Are a Class of Their Own: A Review of the Evidence for Cellular Senescence. Brain Sci., 13(3): 500. https://doi.org/10.3390/BRAINSCI13030500

Voitik, A., V. Kravchenko, O. Pushka, T. Kutkovetska, T. Shchur, and S. Kocira. 2023. Comparison of ndvi, ndre, msavi and ndsi indices for early diagnosis of crop problems. Agric. Engineer., 27(1): 47–57. https://doi.org/10.2478/agriceng-2023-0004

Wahba, M.M., W.M. Hashesh, and N.H. Abou-Baker. 2022. Using some Natural Minerals to Remove Cadmium from Polluted Water. Baghdad Sci. J., 19(5): 1008–1013. https://doi.org/10.21123/bsj.2022.5385

Wan, Y., J. Liu, Z. Zhuang, Q. Wang, and H. Li. 2024. Heavy Metals in Agricultural Soils: Sources, Influencing Factors, and Remediation Strategies. Multidisciplinary Digital Publishing Institute (MDPI)., 12(1).

Wang, N., S. Chen, J. Huang, F. Frappart, R. Taghizadeh, X. Zhang, J.P. Wigneron, J. Xue, Y. Xiao, J. Peng, and Z. Shi. 2024. Global Soil Salinity Estimation at 10 m Using Multi-Source Remote Sensing. J. Remote Sens. (United States)., 4. https://doi.org/10.34133/remotesensing.0130

Wang, Y., D. Luo, Z. Xiong, Z. Wang, and M. Gao. 2023. Changes in rhizosphere phosphorus fractions and phosphate-mineralizing microbial populations in acid soil as influenced by organic acid exudation. Soil Tillage Res., 225: 105543.

Wołowiec, M., M. Komorowska-Kaufman, A. Pruss, G. Rzepa, and T. Bajda. 2019. Removal of Heavy Metals and Metalloids from Water Using Drinking Water Treatment Residuals as Adsorbents: A Review. Mineral., 9(8): 487. https://doi.org/10.3390/MIN9080487

Wu, C., M. Liu, X. Liu, T. Wang, and L. Wang. 2019. Developing a New Spectral Index for Detecting Cadmium-Induced Stress in Rice on a Regional Scale. Int. J. Environ. Res. Public Health., 16(23): 4811.

Xu, W., Y. Jin, and G. Zeng. 2024. Introduction of heavy metals contamination in the water and soil: a review on source, toxicity and remediation methods. In Green Chemist. Letters Rev., 17(1). Taylor and Francis Ltd. https://doi.org/10.1080/17518253.2024.2404235

Xu, X., S. Chen, L. Ren, C. Han, D. Lv, Y. Zhang, and F. Ai. 2021. Estimation of Heavy Metals in Agricultural Soils Using Vis-NIR Spectroscopy with Fractional-Order Derivative and Generalized Regression Neural Network. Remote Sensing., 13(14): 2718.

Yan, Q., Z. Zhong, X. Li, Z. Cao, X. Zheng, and G. Feng. 2024. Characterization of heavy metal, antibiotic pollution, and their resistance genes in paddy with secondary municipal-treated wastewater irrigation. Water Res., 252: 121208.

Yang Han, H.G., Y.X.L.Z.F.W.Q.G. and X.L. 2023. Estimating Soil Salinity Using Multiple Spectral Indexes and Machine Learning Algorithm in Songnen Plain, China. IEEE J. Select. Topic Appl. Earth Observat. Remote Sensing., 16: 7041–7050. https://doi.org/10.1109/JSTARS.2023.3274579

Zhao, D., D. Xie, F. Yin, L. Liu, J. Feng, and T. Ashraf. 2022. Estimation of Pb Content Using Reflectance Spectroscopy in Farmland Soil near Metal Mines, Central China. Remote Sensing., 14(10): 2420.