Dryland soil quality modelling in Najran, Saudi Arabia, using a GIS-based geometric approach

Abstract

Introduction:

Ensuring that natural resources are managed sustainably to achieve global food security is vital given changing climates and increasing demand for food due to population growth. In seeking to secure food security, assessing and safeguarding soil quality is an essential factor in ensuring that increasing demands for food can be met.

Methods:

This study uses the Soil Quality Index (SQI) in Najran, Saudi Arabia to conduct spatial modelling of the geomorphological, physical, chemical, and fertility parameters of soil, using a Geometric Mean Algorithm (GMA) approach. A Digital Elevation Model (DEM) was used with Sentinel-2 satellite images and processed to generate digital soil and landform maps.

Results and discussion:

The GMA results for Najran show a SQI range from 0.33 to 0.59, corresponding to “very low” to “very high” soil quality grades. Five categories of soil quality (SQ) – very low, low, moderate, high, and very high – were developed using the GMA model, and these cover 2.98, 45.69, 40.68, 8.98, and 1.65% of the land area, respectively. The small wadis in the area are nutrient-rich due to fluvial sediment deposition from the surrounding mountainous areas, and correlate to the higher SQ values. To validate the GMA model, a weighted additive (WA) model has been used; the coefficient of determination, R², is 0.942. The proposed model achieves a high sensitivity index of 1.78, showing that the model performs well in assessing SQ in the selected area, and is therefore an effective tool for monitoring SQ.

Conclusion:

This tool can support agricultural planning strategies in the Najran region and can also be used as an analogue for other dryland arid regions where there is a dearth of thorough soil research determining SQ. In addition, this integration improves the reliability of land suitability assessments and supports sustainable agricultural development.

1 Introduction

Drylands cover more than 40% of Earth’s land area and are home to nearly 2 billion people (Prăvălie et al., 2020) which include sub-humid, semi-arid, arid, and hyper-arid conditions with minimal precipitation (aridity index values < 0.65) and a high potential for evapotranspiration (Maier et al., 2018; Crapart et al., 2025). Crop output can be become unsustainable because dryland agroecosystems are often subject to extreme climates and their resources are scarce (Fadl et al., 2021). Drylands can be agriculturally poor because of the low levels of organic matter, poor vegetation cover, low water quality, high salinity and high alkalinity (Abuzaid et al., 2021; El Behairy et al., 2021). Land quality evaluations are essential in ensuring that land can be put to arable farming use (Shokr et al., 2016; Özkan et al., 2020), because soil fertility/quality can be damaged by a range of chemical, physical, biological, and ecological factors unique to drylands (Lal, 2015). It is important that agricultural practices are sustainable and protect the environment, thus soil quality (SQ) must be maintained (Al-Ghamdi et al., 2021). Sustainable agricultural practices and water filtration steps must be taken to preserve the SQ and improve potential agricultural land habitats (Al-Ghamdi et al., 2021) and natural ecosystems. Despite the significance of drylands globally, many regions, including Saudi Arabia, remain understudied due to a lack of resources and systematic data collection. Najran Province exemplifies this gap, since no comprehensive SQ dataset exists for this region. Saudi Arabia is the largest Middle Eastern country, covering 2.25 million km² (Al-Dosary, 2022), including plateaus, plains, mountains, valleys, and sand dunes. It is comprised of four major terrains: (1) the Arabian Shield (a Proterozoic volcanic and sedimentary succession with inclusions of granite and gabbro); (2) the Arabian Platform; (3) Tertiary “harrats,” mainly covering the Arabian Shield; and (4) the Red Sea Coastal Plain, comprising Tertiary and Quaternary sedimentary coral reefs and rocks. Soil quality (SQ) is influenced by both physical and chemical parameters. Physical parameters include the mineralogy of the rock properties, the rock origins, bulk density, root depth, and soil texture (Shadfan et al., 1984). Chemical indicators include cation exchange capacity (CEC), electrical conductivity (EC), and pH, which together provide essential information about nutrient availability, salinity stress, and overall soil health. These chemical indicators, combined with physical parameters, provide a comprehensive assessment of SQ that is particularly relevant in dryland ecosystems where soil degradation risks are high (Moore et al., 2016).

A soil quality index (SQI) can be developed by choosing certain indicators, allocating scoring criteria, and generating a combined score as a single number. This score helps ascertain how environmental variables and human interventions affect SQ across a range of performance indicators (Abuzaid et al., 2021). The geometric mean algorism (GMA) (the nth root of a number sequence) is a measure of central tendency (Vogel, 2022). It has been used to conduct assessments of desertification sensitivity (Mohamed, 2013), and to conduct suitability studies (El Baroudy, 2016). The GMA minimizes the impact of outliers and can provide a more balanced representation of soil variability; this ability to reduce the impact of extreme values makes it ideal for arid regions where data variability is high. When utilizing parametric or nonparametric tests, the presence of outliers can result in significant distortions of parameter and statistic estimations as well as inflated error rates (Zimmerman, 1994; Zimmerman, 1995, 1998). Compared to traditional arithmetic mean methods, which can be skewed by extreme values, GMA offers a more acceptable reflection of SQ in regions like Najran, where conditions vary significantly across short distances (Mikkonen et al., 2018). Agroecosystems have been assessed by several researchers (Özkan et al., 2020; Abuzaid et al., 2021; Fadl et al., 2021) using both geographic information systems (GIS) and remote sensing (RS), the former being used to efficiently collate and analyse spatial data, including geo-referenced data and satellite imagery (Abuzaid and Abdelatif, 2022). An understanding of SQ spatial variability can be obtained via GIS analysis when zoning land resources (Abdellatif et al., 2021). GIS analysis also enables the evaluation of various soil parameters and the ability to statistically forecast SQ in areas which are poorly sampled (Abdel-Fattah et al., 2021). Variogram analysis can be used to explore the interrelationships between different layers of soil data (Deutsch, 2003). No prior study has created an open-access SQ index SQI for Najran using an integrated GIS and GMA framework. Insufficient data relevant to a given location hinders local agricultural stakeholders’ capacity to make well-informed and sustainable land management strategies. This study aims to address this critical data gap by integrating field and geospatial methods. Soil samples collected during field investigations form a unique dataset underpinning this research and make a significant contribution to understanding local soil conditions. This investigation demonstrates how combining GIS modelling with index methods via GMA allows quantitative and qualitative SQ spatial modelling analysis to better inform land management policies in the Najran region of Saudi Arabia.

2 Methodology

2.1 Research area

Najran Province is located in the southwest of Saudi Arabia, at 17.4933^o N, 44.127^o E (Figure 1). The western region of the province is a mountainous region whilst the east of the region is desert sands, with a large wadi through the centre of the province. The wadi is the location of the city and the main agricultural activities. At the heart of the region is a rapidly growing city, with the population rising from 47,500 to 505,652 between 1974 and 2010. The climate of Saudi Arabia differs from area to area, with the central region and the coastal areas along the Red Sea and Arabian Gulf experiencing high temperatures, and the north and southeast experiencing low temperatures (Almazroui et al., 2012; Alghamdi and Moore, 2014). The Najran Province climate is arid, with hot summers and mild winters. The average annual temperature is 25 °C, ranging from 17 °C to 33 °C. Between 1978 and 2019, the mean annual temperature has increased 0.54 °C (Almazroui, 2020).

Figure 1

Geologically Najran Province is composed of pre-Cambrian igneous rocks, the stratified Cambrian–Ordovician Wajeed sandstone, and sporadic Tertiary bedrock (Sable, 1985; Al-Shanti, 1993; Youssef et al., 2014). This is capped by alluvial soil in the wadi flood plain, where there is extensive agricultural activity. The exposed rocks in mountainous parts experience ongoing erosion, facilitating the development of fertile alluvial soils inside the wadi system, whereas aeolian sands accumulate in the eastern desert areas (Al-Shanti, 1993). Pumping of ground water has caused soil compaction leading to subsidence and surface fissures, demonstrating the stress caused by anthropological activity on the natural environment (Abd El Aal and Masoud, 2018). This emphasises the need to understand the behaviour of the natural environment in Najran Province.

The approach for this work involves five distinct steps (Figure 2):

Figure 2

Remote sensing data utilization: Remote sensing data are utilized to extract geomorphological units and calculate the Normalized Difference Vegetation Index (NDVI), based on Sentinel-2 images and the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM).

Fieldwork: to gather primary data and ground reference information. Soil samples collected are positioned using a global positioning system (GPS) receiver which allows verification of the accuracy of mapping unit borders.

Laboratory investigations: The soil samples are tested to determine the physical, chemical, and fertility characteristics of the soil.

Spatial model development: A spatial model for SQ assessment is developed using the model builder using ArcGIS and following the GMA procedure.

Model validation: The proposed model is validated against the weighted additive (WA) procedure.

2.2 Remote sensing data

The European Space Agency (ESA) provides multi-spectral optical images from the Sentinel-2A satellite; Multispectral Imager (MSI) instrument images for September 2, 17, and 27, 2023 were acquired for this investigation. The (MSI) sensor includes 13 spectral bands with spatial resolutions of 10 m (4 bands), 20 m (6 bands), and 60 m (3 bands) (Malenovský et al., 2012). All spectral bands were resampled to a 10 m resolution using Google Earth Engine (GEE) (Bui et al., 2022).

2.3 Extracting landforms

Topographic slope, aspect, curvature, stratigraphic and field survey data are extracted from spectral satellite Sentinel-2 and a 30 × 30 m DEM from NASA’s Shuttle Radar Topographic Mission (SRTM). A 3D view aids in identifying and extracting the landform units by visual interpretation of the Sentinal-2 images draped over the DEM. A focus neighbourhood statistic filter is applied to refine and reduce noise when reclassifying the topographic parameters. To achieve the mean and majority focal statistic values, the designated neighbourhood pixel’s greatest and average values are assigned to the centre pixel of the moving window (Dobos and Montanarella, 2005). When compared to linear averaging filters, median filtering is statistically nonlinear (Gonzalez, 2009). The technique can quickly detect cracks since their grayscale value is typically lower than that of the surrounding area. Additionally, the template window’s noise reduction capabilities improve with size (Hou et al., 2021).

The DEM modified using the algorithm developed by Planchon and Darboux (2002) is used to create the topographic indices using ArcGIS software (Olaya and Conrad, 2009). Abdel-Kader and Yacoub (2005) study developed a clear set of definitions of landforms, and these definitions are utilized in this study, using visual analysis of satellite imagery, DEM in 3D, a hillshade function, and field truth points to identify landform units (Dobos et al., 2002). This approach demonstrates the distinct elevation variations present in each designated area (Shokr et al., 2021). Ground truth points from field surveys and maps, including a 1:50,000 scale topographic map and geological map (Map I-217) are used in this research area to improve accuracy and provide the most suitable names for the landform units.

2.4 Geostatistical model

Inverse Distance Weighting (IDW) interpolation is a widely employed spatial estimation technique in geospatial sciences and environmental modelling. IDW gives closer points greater weight when estimating the variable of interest. This method is predicated on Tobler’s First Law of Geography (Tobler, 1970) and is a useful simple tool for spatial prediction (Deutsch, 2003; Babak and Deutsch, 2009). Even though inverse-distance estimators are straightforward, experiments reveal that they are highly sensitive to the type of database, to the number of neighbours, and to the distance weighting exponent (Weber and Englund, 1992). IDW interpolation is favoured over kriging-based methods in real-world situations with sparse data (Wahba, 1990; Hutchinson, 1993) and is particularly useful for rapid visualisation of the variable being studied (Borga and Vizzaccaro, 1997). The sparse distribution of soil samples and variety of topographic and geomorphological conditions make it the most appropriate technique for this investigation. The values of the sample data points near each cell were averaged to determine the grid cell values, assuming that the local influence of each sample point decreases with distance (the points nearer the processing cell are given a higher weight than those farther away).

The fundamental equation for IDW interpolation is given as (Roberts et al., 2004):

Where represents the estimated value, denotes known (sample) value, signifies the weight assigned to each known point. The weighting function is typically defined as:

Here, ) represents the distance between the prediction location and the sampled location ᵢ, while denotes the power parameter (a power of two was chosen).

2.5 Vegetation cover calculation through NDVI

The Normalized Difference Vegetation Index (NDVI) is a standardized index that quantifies vegetation density by measuring the difference in reflectance of two different spectral bands between near-infrared (842 nm) and red (665 nm) wavelengths (Rouse et al., 1974). The concept is based on the spectral characteristics of vegetation, particularly the absorption of red light by chlorophyll for photosynthesis and the strong reflection of NIR light due to the cellular structure of leaves (Maxwell and Johnson, 2000). The normalization principle of NDVI reduces topographic and atmospheric effects while enhancing the vegetation signal, making it a reliable measure for assessing vegetation cover (Silleos et al., 2006). The index is calculated on a pixel-by-pixel basis using the following equation (Rouse et al., 1974):

Where NIR represents near-infrared reflectance and Red represents visible red reflectance. The resulting values range from −1 to +1, with higher positive values indicating denser vegetation cover, values near zero representing bare soil, and negative values typically indicating water bodies. The resulting NDVI map is used to illustrate regional variations in vegetation cover, reflecting both natural and human activity within the research area.

2.6 Soil samples and laboratory analysis

A stratified random technique was used to gather 40 representative soil samples from the research area, taking into account the landform and recognized landscape units. Figure 3 illustrates the spatial distribution of the sampling sites within the research area. Mountainous areas are excluded from the investigation area of this study and not form part the analysis. Each site was assigned a unique identifier for clear reference during subsequent laboratory analyses.

Figure 3

Three sub-samples were collected from each site up to a depth of 60 cm using a stainless-steel auger since it symbolizes the root zone’s soil (Abdel-Fattah et al., 2021; Shokr et al., 2025). These sub-samples were combined to form a single composite sample per site, in order to minimize localized variation. Sample locations were recorded using GPS and documented with field photographs to verify landform characteristics. The collected samples were stored in labelled plastic bags and transported to the laboratory for chemical analysis (Abuzaid and Abdelatif, 2022).

In the laboratory, each composite sample was air-dried, gently crushed, and passed through a 2 mm sieve. Soil reaction (pH) was measured in a 1:2.5 soil-to-water suspension using a standard glass electrode, while electrical conductivity (EC) was determined in a 1:5 soil-to-water suspension (Richards, 1954). Calcimeter was used to estimate the percentage of calcium carbonate (%CaCO₃). Two grams of soil were treated with 0.1 N HCL, and the volume of CO₂ from the samples and pure calcium carbonate was noted. The percentage of calcium carbonate was then computed using (Horváth et al., 2005). The sodium acetate method was employed to determine cation exchange capacity (CEC), and soil organic matter (SOM) was measured using the Walkley and Black method (van Reeuwijk, 1995; Baruah and Borthakur, 1997).

Particle size distribution was determined with the pipette method (Kettler et al., 2001), wherein soil samples were chemically and physically dispersed before the sand fraction was separated by filtration, and the silt and clay fractions were isolated via sedimentation. The proportions of sand, silt, and clay were then plotted on the International Soil Texture Triangle to classify soil texture categories (e.g., sandy loam, clayey). Finally, bulk density (BD) was determined by collecting intact core samples (undisturbed), oven-drying them at 105 °C for 24 h and then calculating the ratio of the dry weight to the core volume (Ahogle et al., 2022; Bogunovic et al., 2022).

2.7 Modelling of soil quality variables

In this study, the GMA was applied to integrate geomorphological, physical, chemical, and fertility-related parameters to develop the SQI. The GMA is a statistical method that integrates multiple SQ parameters by calculating the nth root of their product, where n is the number of parameters (Foster et al., 2005). This approach effectively captures the multiplicative interactions among variables, providing a distinct representation of SQ (Kaplan and Garrick, 1981). In arid environments like Najran Province, soil characteristics often exhibit significant variability, with extreme values that can skew analyses. The GMA mitigates the influence of these outliers, offering a balanced assessment of soil quality. Unlike simple additive methods, the geometric mean emphasizes the “weakest link” principle, where a particularly low score in any sub-index exerts a more pronounced influence on the overall SQI. According to Abuzaid et al. (2021), the suggested model, which used GMA to calculate SQ in arid regions, showed accuracy in determining SQ and had the highest sensitivity index. The geometric mean of the four indices—the Geomorphological Index (GI), Fertility Index (FI), Physical Index (PI), and Chemical Index (CI)—is used to compute the SQI (Equations 3–7).

Scoring are assessments that are typically given numerical values and interpreted relative to prior research that focus on arid environments similar to our study area condition. The scoring system was established using a combination of expert recommendations and an extensive literature review, drawing from authoritative sources including (Abrol et al., 1988; Soltanpour, 1991; FAO, 2006; Yao et al., 2013; Soil Survey Staff, 2017; Hazelton and Murphy, 2016), and recent studies by Mohamed et al. (2020), Prăvălie et al. (2020), and Afzali et al. (2021). Scores are allocated to the components of a specific parameter in this study, and valid scores range from the lowest score possible 0.2, indicating poor performance or unfavourable conditions, to the highest score 1 reflecting better performance or favourable conditions. The complete rating framework, including specific criteria for each parameter, is detailed in Supplementary Tables S1, S2.

2.7.1 Geomorphological index (GI)

The GI integrates slope (S), aspect (A), plan curvature (PC), and profile curvature (PrC) to assess each site’s susceptibility to erosion (Prăvălie et al., 2020). In general, steep slopes accelerate runoff and thus heighten erosion risks, while gentler slopes reduce the velocity of water flow. Aspect plays a crucial role on microclimatic conditions like temperature, humidity, and evaporation, where, in the study area, south-facing slopes (S, SE, SW, E) receive more sunlight and are prone to higher evaporation and lower moisture retention. Conversely, north-facing exposures (N, NE, and NW, W) often retain more humidity (Jasińska et al., 2019). PC highlights the horizontal convergence or divergence of flows: convergent zones (negative PC values) channel surface water aggressively, whereas divergent areas (positive PC) disperse it. PrC measures vertical curvature: convex slopes (negative PrC) favour faster runoff, while concave slopes (positive PrC) slow the flow (Blaga, 2012). The GI is computed as (Prăvălie et al., 2020):

Each variable’s score is assigned in Supplementary Table S1. The higher GI values generally indicate more geomorphologically stable conditions—namely, flatter slopes, moister area, concave shapes—whereas lower values point to topographies at greater risk of soil degradation.

2.7.2 Fertility index (FI)

The FI synthesizes key soil nutrients (Nitrogen (N), phosphorus (P), and potassium K), organic matter (SOM), and vegetative health into a single metric of soil fertility (Abuzaid et al., 2021). N, P and K are essential macro-nutrients that support plant growth and metabolic functions, whereas soil organic matter influences nutrient availability, soil structure, and water retention (Shokr et al., 2021; Jiaying et al., 2022). NDVI serves as a remote sensing indicator of vegetative health and biomass production, reflecting how effectively plants utilize the available nutrients (Fadl et al., 2024). The index is calculated as (Abuzaid et al., 2021):

Higher FI values equate to soils with robust nutrient profiles, abundant organic matter, and vigorous vegetation cover, whereas lower FI values often signal nutrient limitations, insufficient organic content, or sparse plant growth.

2.7.3 Physical index (PI)

The PI synthesizes soil texture (T), bulk density (BD), hydraulic conductivity (HC), and water holding capacity (WHC), all of which influence plant establishment and root penetration (Abdellatif et al., 2021). Texture governs both infiltration rates and water retention; sandy soils favour rapid drainage but may struggle to retain moisture, whereas clayey soils hold water well but risk poor aeration if overly compact (Huang and Hartemink, 2020). Bulk density reflects how tightly soil particles are packed, with higher BD values often restricting root extension and reducing pore space (Dexter et al., 2008). Hydraulic conductivity determines how rapidly water moves through soil, and WHC represents the amount of water stored for plant use (Çal and Barik, 2020; Abdallah et al., 2021). In addition, the most significant soil characteristics affecting crop production, yield stability, nutrient cycling, resource use efficiency, and environmental quality is WHC (Minasny and McBratney, 2018; Ogle et al., 2019). The PI is calculated as (Abuzaid et al., 2021):

The higher PI value mark soils that are physically well-suited for agricultural production, while lower PI values indicate potential limitations related to soil compaction, drainage, or water retention.

2.7.4 Chemical index (CI)

The CI focuses on salinity (electrical conductivity, EC), soil pH, calcium carbonate (CaCO₃%), gypsum (CaSO₄%), and cation exchange capacity (CEC) (Abdellatif et al., 2021). Information on organisms, nutrition availability, plant water availability, and contaminant movement is provided by the chemical indicators (AbdelRahman et al., 2019). Excessive salinity can hamper plant growth, while pH that deviates substantially from neutrality curtails nutrient availability (Sharma et al., 2023; Al Otaibi et al., 2024). Calcium carbonate and gypsum contents affect soil structure and certain micronutrient dynamics, and CEC denotes how efficiently soils hold and exchange vital ions (Grant et al., 1992; Ćirić et al., 2023). The index is calculated as (Abdellatif et al., 2021):

A higher CI value is indicative of chemically balanced soils with moderate salinity and a capacity to supply nutrients, whereas lower CI value may point to salinity stress, problematic pH levels, or poor nutrient retention, all of which can hinder plant development.

2.7.5 Soil quality index (SQI)

The final SQI is be described according to the following equation (Abdellatif et al., 2021).

The SQI reflects geomorphological stability, nutrient availability, physical suitability, and chemical balance. Since a higher SQI score indicates a greater capacity for performance, it would be indicative of superior soil function. Farmers can use this method to pinpoint problem regions, carry out focused interventions (Chaudhry et al., 2024) and enhance soil health and boost crop yield.

A spatial GIS model of SQ is developed. The tool facilitates the selected spatial analyses by chaining together different processes, where the output of each process becomes the input for the next (Supplementary Figure S1). The steps involved in generating the final SQ map for the research area are as follows: (a) the raster calculator tool is used to interpolate soil properties from point data into a raster layer; (b) the output from step (a) is reclassified into five categories: very high, high, moderate, low, and very low; (c) each SQ parameter is assigned a score (see Supplementary Tables S1, S2) based on Equations 4–8; and (d) the resulting output is used in a weighted overlay analysis to produce and visualize the final SQ map.

2.8 Grades of soil quality

The classification of each index is as follows: Grade I is very high, Grade II is high, Grade III is moderate, Grade IV is low, and Grade V is very low. The range of values for every index is split by the quantity of intervals (i.e., 5) and the breadth of each interval thereafter determined by the outcome. By adding this number to each index’s lowest value, the upper bound of the first interval is reached, and then iteratively until the index’s upper range is reached (Nabiollahi et al., 2017).

2.9 Comparative assessment with the traditional model

The weighted additive index (WA) is calculated (Equation 9) to assess the performance of the proposed GMA-based model by comparing its SQI outputs with those generated using the traditional weighted additive approach. The weighted additive approach was selected since it is the most widely used method for assessing soil quality and is frequently used in arid and semi-arid regions (Doran and Jones, 1996; Abdel-Fattah et al., 2021; Celis et al., 2024; Shokr et al., 2025).

Where, Wi represents the weight assigned to each parameter, and Si is the score for each variable. The weight for each parameter is determined based on its communality, which is calculated using factor analysis in SPSS Software version 22. Each parameter’s weight is derived as a ratio of its communality to the total communality, ensuring that the relative importance of each parameter is appropriately considered (Yu-Dong et al., 2013) (Table 1). The communality of a variable is the percentage of its variance that can be explained by the factors that were extracted (principal components). Variables with higher communality have a bigger effect on the structure of soil quality because they share more variance with the whole dataset. Because of this, their weights go up (Tavakol and Wetzel, 2020).

The spatial outputs of the GMA-derived SQI and the WA-derived SQI were statistically compared using Pearson correlation analysis and simple linear regression. In the regression analysis, the WA-based SQI was treated as the dependent variable and the GMA-based SQI as the independent variable. The coefficient of determination (R²) was used to quantify the strength of agreement between the two modelling approaches (Gao et al., 2024).

Model sensitivity was further evaluated by comparing the range of SQI values generated by each model. The sensitivity index (SI) is calculated using Equation 10 according to Yu-Dong et al. (2013):

A more sensitive model, which is better attuned to management processes, provides greater realism and accuracy in representing SQ.

3 Results

3.1 Land surface parameters

The analysis of the DEM reveals that the ground surface elevation varies in low basin areas, from approximately 1,110 m above sea level (asl) to 2,340 m (asl) (Supplementary Figure S2a); selected land surface parameters, namely slope, aspect, and curvature, were extracted from the DEM. The research area is classified into 8 slope classes: flat (0–0.2%), level (0.5–1%), very gently sloping (1–2%), gently sloping (2–5%), sloping (5–10%), strongly sloping (10–15%), moderately steep (15–30%), and steep (30–60%) (Lee, 2014; Silalahi et al., 2019) (Supplementary Figure S2b).

3.2 Geomorphology of study area

Using historical maps and field investigations, physiographic units were delineated through the integration of satellite imagery and DEM analysis (Shokr et al., 2021) (Section 2.3). Eighteen geomorphological landforms were identified across the study area (Figure 4). High-mountain regions are the most extensive unit, covering 261,320 ha (63.42%) and occurring mainly in the western sector. Alluvial and fluvial-related units form notable portions of the landscape, including the alluvial plain (34,340 ha; 8.33%) and wadis (29,350 ha; 7.13%), while aeolian landforms are concentrated in the east near the empty quarter, where sand dunes (12,490 ha; 3.03%) and sand sheet-related units (e.g., Sand sheet, Sand sheets and Dunes, and Sand sheets and Hillocks) are common (Supplementary Table S3). The remaining landforms each occupy relatively limited areas (generally <5%), but they represent important geomorphic elements within the overall physiographic setting (Supplementary Figure S3 and Figure 4).

Figure 4

3.3 Spatial variation of soil properties

Previous studies (Fuentes et al., 2004; Horn and Smucker, 2005) demonstrate that increasing soil compaction measured in Bulk Density (BD) leads to a reduction in macropores and a subsequent increase in meso- and micropores, which negatively impacts soil’s electrical conductivity, water retention, and load-bearing capacity (Indoria et al., 2020). These factors are particularly important when considering the structural stability of soil under various land uses. BD values in the research area range from 1.26 to 1.78 mg kg⁻¹ (Ali and Moghanm, 2013) (Table 2), with a standard deviation (STD) of <1.00. The east portion of the research area has the highest BD values, whereas the northern areas have smaller values (Supplementary Figure S4a). The high percentage of sand, low organic content, and urban building in some areas may be the cause of the high BD values (Sakin, 2012; Ahad et al., 2015; Zhang et al., 2024). The distribution of particle sizes in the research area has a distinct spatial pattern. According to Supplementary Figure S4b, the sand content peaks in the east (72.8–93.1%) and falls in the west to its lowest value (21.1–44.7%). Conversely, silt exhibits the opposite pattern, with lowest fractions (4.9–22.9%) in the east and largest concentration (34.6–51.4%) in the west (Supplementary Figure S4c). In the west, the clay content reaches its highest value at 8.9–12.5%, but in the east, it falls to 0–3.7% (Supplementary Figure S4d). Therefore, soil texture varied significantly between sandy, loamy, and sandy loam. These findings concur with those of Bashour et al. (1983), who demonstrated that the characteristics and nature of the soils in Saudi Arabia’s agricultural regions include mainly sandy, loamy, and sand.

Hydraulic conductivity (HC), which expresses the pore structure and water flow in soil, ranges from 2.20 to 22 cm h⁻¹ (Çal and Barik, 2020) (Table 3). Within the research area, the northwest has the greatest HC values, while the east and southwest have the lowest values (Supplementary Figure S4e). The high values of HC are due to coarse texture providing “efficient” porosity, which is associated with the amount of pore space which has a stronger correlation with HC, because it excludes the percentage of immobile water that is retained at the surface of fine particles (Clay soils) (Alakayleh et al., 2018). Moreover, due to the coarse texture of the soil, the additional water holding capacity (WHC) is very modest (Table 2). Supplementary Figure S6f shows the trends of WHC% which increases from east to west.

The research area’s EC values span a broad range, from 1.08 to 5.88 dSm⁻¹, as seen in Supplementary Figure S4g. The northwest of the research area has the greatest EC concentrations. Nachshon (2018) reported that the majority of salinized soils are found in arid and semi-arid environments with little to infrequent precipitation and high evaporation. pH values are in the range of 7.0–8.9 (as per Supplementary Figure S4h). The western regions of the research area have the lowest pH which increases moving eastwards toward the desert area, this could be because of high CaCO₃ values (11.98%) from shell fragments, which might result in solid layer formations that are impermeable to water and plant crops, as well as the fixation of P fertilizer (Von Wandruszka, 2006; Shokr et al., 2021) (Supplementary Figure S4i). The carbonate rocks, which include limestone and recrystallized limestone, sandstone, clay, and conglomerates, are the most significant raw materials in the Najran-Sharourah District (El-Aal et al., 2021). CaSO₄ has an average of 0.86% and varied from 0.07 to 4.35%. CaSO₄% increases from east to northwest of the research area (Supplementary Figure S4i). The CEC values show that the greatest value is 8.8 cmol kg⁻¹ in the northwest of the research area and the lowest is 1.12 cmol kg⁻¹in the east of the research area (Supplementary Figure S4k). Since there are strong positive relationships between CEC, clay, and organic matter (Abdel-Fattah et al., 2021), the low values are caused by the low amounts of these three components.

The research area’s nitrogen content varies from low to moderate, as indicated by the available nitrogen range of 2.07 to 74.89 mgkg^-1, with STD is 27.50 (Mather and Koch, 2022) (Table 2). The northwest portion of the research area has a high concentration of N because of agricultural practices (Supplementary Figure S4l). According to Baruah and Borthakur (1997), the research area’s available P and K content is categorized as moderate to low, with average values of 16.03 and 11.00 mg kg⁻¹, respectively. In the research area, there are significant differences in macronutrients, with STDs for P, and K being 9.64, and 5.67, respectively. The spatial distribution of P and K shows an increasing trend from east to northwest in research area (Supplementary Figures S4m,n). The soil organic matter content (SOM%) ranges from 0.01 to 1.10% (Supplementary Figure S4o). SOM is low through the research area, but it is lowest in the dry desert is in the east (Supplementary Figure S4o) and since it is known high temperatures accelerate the rate at which organic matter in the soil decomposes, dry and semi-arid climate conditions have a detrimental impact on the amount of OM in the research region (Conant et al., 2011; Moinet et al., 2020). The findings of Kurtosis show that although soil pH (0.24) and p (0.67) have the lowest values of Kurtosis, indicating flatter distributions, silt (19.28), sand (11.09), and CaCO₃ (9.83) have high values, indicating peaked distributions with extreme values (Yousif et al., 2025).

3.4 Geomorphological index (GI)

The GI classifies a region using values of slope, aspect, plan curvature, and profile curvature and provides a general indicator of resistance to degradation (Equation 4 in Section 2.7.1). Low GI values indicate that an area is highly susceptible to land degradation, because of the steep slope, or the plan and profile slope curvatures, which suggest the likelihood of accelerated, convergent surface runoff (Supplementary Figures S1a–e). GI values in the study area range from 0.42 (very low) to 1 (very high) as summarised in (Supplementary Table S4); with area of 73.11 km² (4.82%) classified as very low class, 982.75 km² (64.82%) as low class, 378.29 km² (24.94%) as moderate geomorphology class, 66.37 km² (4.37%) as high, and 5.78 km² (1.04%) as very high class (see Table 3). From Figure 5A, it is clear that the research area is dominated by low geomorphological quality, with small portions in the centre and to the east representing the very high-quality class.

Figure 5

3.5 Soil fertility index (FI)

Alongside SQI, FI could be considered the most significant measure as it denotes the soil’s ability to provide the necessary nutrients for plant growth (León-Moreno et al., 2019). The FI uses the available nitrogen, phosphorus, potassium, SOM, and NDVI values to classify regions (Equation 5 in Section 2.7.2). As shown in Supplementary Table S4, the FI ranges from 0.25 (very low) to 0.43 (very high) (Supplementary Table S5). The very low class of FI is found in the centre and east of the research area, which covers 149.01 km² (9.83%). Most of the research area falls into the low fertility class, covering 865.03 km² (57.05%) and is located in the east of the research area. The high and very high classes represent relatively small areas in the middle of the research area, covering approximately 140.044 km² (9.26%) and 39.63km² (2.61%), respectively (Table 3 and Figure 5B). The prevalence of low FI values can be due to macronutrient (N, P, K) and SOM deficiencies, as well as low NDVI values, indicating poor vegetation health and reduced soil fertility status (Raun et al., 2001; Sanchez, 2015) (Supplementary Figure S5). Furthermore, the research area’s arid climate, which is characterised by very limited precipitation, with rainfall occurring almost exclusively during the winter months, means that cultivation is restricted to the winter season and the availability of irrigation water.

3.6 Soil physical index (PI)

Soil texture, bulk density (BD), hydraulic conductivity (HC), and Water-Holding Capacity (WHC) are the basis for calculating the PI (Equation 6 in Section 2.7.3). The PI ranges from 0.26 to 0.65 (Supplementary Table S6), indicating that the PI in the research area varies greatly from extremely low to very high quality. Higher PI values reflect more favourable physical soil properties, including improved structure, permeability, and moisture retention, whereas lower values indicate physical limitations such as compaction and reduced infiltration capacity. According to PI area distribution in Table 4, approximately 92.16 km² (6.07%) of the research area is classified as very low quality, 647.311 km² (49%) as low quality, 576.10 km² (37.99%) as moderate, 88.28 km² (5.82%) as high, and 13.43 km² (0.88%) as very high. The very low class is dispersed throughout the research region, while the low class is located in the east. (Figure 5C). Physical degradation typically results in reduced structural attributes, affecting water infiltration and erosion susceptibility (Lal, 2015) (Supplementary Figures S4a–e).

3.7 Soil chemical index (CI)

The CI evaluation synthesizes soil salinity, soil reaction, calcium carbonate proportion, gypsum percentage, and CEC (Equation 7 in Section 2.7.4). The CI values range from 0.28 to 0.83, which reflects chemical quality grades ranging from very low to very high (Supplementary Table S7). Higher CI values indicate more favourable chemical conditions, whereas lower values reflect chemical constraints such as salinisation, alkalinisation, or reduced nutrient retention capacity (Lal, 2015). The spatial distribution encompasses 72.42km² (1.80%) as very low quality, 768.65km² (50.69%) as low quality, 554.92 km² (36.59%) as moderate quality, 122.78 km² (8.09%) as high quality, and 42.51 km² (2.80%) as very high quality soil (Figure 5D). The majority of the research area has low chemical quality, with high and very high quality situated in the northeast of the research area. The very low chemical quality class appears in small, scattered areas in the centre and east of the research area (Figure 5D).

3.8 Pearson correlation between soil characteristics and SQI

According to the results of the Pearson correlation, SQI is statistically positively well correlated with a third of the soil properties measured. The most important elements improving SQ are CEC, clay content, OM, and WHC. These results emphasize how crucial fine texture, moisture-related factors, and soil fertility are to enhancing overall soil performance (Çal and Barik, 2020; Shokr et al., 2021). Conversely, SQI shows a significant negative correlation with both pH and slope, indicating that steeper slopes and rising alkalinity are linked to deteriorating soil quality. This pattern accords with steep slopes being more susceptible to erosion processes and the detrimental effect of high pH on nutrient availability. There is a weak negative correlation between sand content and SQI, which reflects the disadvantages of sandy soils, including limited water-holding capacity and poor fertility (Bassouny and Abuzaid, 2017). For other variables, such as bulk density (BD), silt, CaCO₃, CaSO₄, and topographical features (aspect, plan), the correlation is weak or insignificant (Figure 6).

Figure 6

3.9 Relative importance of different studied indices

Despite varying measurement units, standardized coefficients enable comparison of the relative importance of various factors (Siegel, 2016). GI had the highest standardized coefficient (β = 0.605) indicating that it has the most positive impact on the final SQI (Table 4). This implies that, compared to the other variables, gains in GI are more closely linked to changes in total soil quality. The PI (β = 0.407) and FI (β = 0.365) also show notable impacts, indicating their important roles in influencing the dynamics of soil quality. In contrast, CI showed the lowest standardized impact (β = 0.176), but it was still statistically significant, indicating that it is an important contributor to the model.

4 Discussion

4.1 Soil quality index (SQI) according to GMA and WA models

The ranges of SQIs for the GMA model are 0.33 to 0.59, and for the weighted additive index model are 0.39 to 0.58 (Supplementary Table S8). These ranges showed that the SQI grades range from very low to very high (Supplementary Table S9). As a consequence of the GMA mode, which is based on parameters of GI, FI, PI, and CI (Equation 8 in Section 2.7), SQI is grouped into five categories: very low, low, moderate, high, and very high, which correspond to grid reference (GR) 45.21,692.94, 616.93,136.18, and 25 km² of the research area, respectively (Figure 7). The SQ map of Najran reveals significant spatial variance across different regions, and this variation is directly related to the area’s geographical features. These findings are significant as they originate from a distinctive dataset gathered in Najran Province, one of the least examined arid regions in Saudi Arabia. This dataset is a rare and useful in addition to our knowledge of how SQ changes in dry areas, where there is not much research done in the field. This study’s spatial patterns create a new regional reference for future studies of dryland SQ and environmental monitoring.

Figure 7

The SQ in the northwest of the research area, Areas A, B, and C correlates to geographical areas of Hadadah and Hubuna (Figure 8 and Supplementary Figure S6), in these areas the SQI is rated from “high” to “very high.” These are small wadis surrounded by mountains with natural silt deposition and erosion that improve soil fertility. These conditions encourage a wide range of agricultural crops, including the production of fruits and vegetables, consistent with the region’s long history of intensive farming methods. Topography has a significant impact on the spatial variation of soil physical properties and nutrients due to its effects on infiltration, runoff, soil erosion, and deposition processes, as well as their intensity and microclimate (Ritchie et al., 2007; Seibert et al., 2007; Schwanghart and Jarmer, 2011; Li and Pan, 2020). The top 20 cm of the soil contains most of the nutrients, so that wind and water can deplete significant amounts of nutrients (Guo and Gifford, 2002; Lal, 2019). As debris and runoff typically accumulate from higher slope locations to lower slope positions, surface soil nutrients are transported from higher eroding mountains to the lower slope and valley areas (Seibert et al., 2007; Li and Pan, 2020).

Figure 8

In contrast, Area D, in the central part of the research area corresponds to Bir Askar (Figure 8 and Supplementary Figure S6), has moderate SQI. Intensive agriculture is less suitable in this area, which is typified by a more arid desert landscape. Economic activities in Bir Askar are granite quarrying and rough grazing for livestock, rather than agricultural crop farming. The main fraction of soil particles are immature, locally weathered from the granite bedrock, and mainly sand with a smaller percentage of clay particles (Liao et al., 2022). Sandy soils are low in nutrient content and have poor water retention, making them unsuitable for intensive cultivation (Kassas, 1995). Areas E and F are connected to Wadi Najran and the Rub’ al Khali desert, in the east of the research area (Figure 8), and exhibit a mixture of moderate to low soil quality. Wadi Najran is vital to the area’s ecosystem because it carries water from the Sarawat highlands over its 290 km course and deposits nutrient-rich silt in the plains before dispersing into the Rub’ al Khali sands. Along its banks, this natural stream sustains agriculture, but as the land turns into the Rub’ al Khali, the soil becomes progressively sandier and less productive.

When comparing the WA and GMA model results, both methods had similar grades in similar spatial locations. Linear regression, widely applied in environmental studies, was used to assess their agreement. A scatterplot (Figure 9) illustrates the strong correlation between the WA scores for SQ and those from the proposed GMA model. Excellent agreement between the two methods is demonstrated by the dense clustering of data points along the regression line. The very high coefficient of determination (R² = 0.942) confirms that the GMA model explains 94% of the variance in the WA scores, strongly supporting this visual finding. The GMA is appropriate for dry places with significant data variability because it may diminish the impact of extreme results and provide a more balanced view of soil variability (Vogel, 2022).

Figure 9

For both the WA and the GMA models, the corresponding sensitivity index (SI) values were 1.48 and 1.78, respectively. Based on its higher sensitivity index (1.78 vs. 1.48 for WA), we recommend the GMA model as the more appropriate approach for dryland SQI assessment in arid regions such as Najran. This suggests that the GMA model may be more suitable for capturing the variability in soil performance under the study area’s conditions. The GMA model showed better practical utility and realism during field validation, even though the GMA and WA techniques have a significant statistical correlation. The GMA methodology demonstrates heightened sensitivity, allowing it to more effectively distinguish between differences in SQ grades seen on the ground, although the WA method still offers a reliable general estimation. The algorithm used in the GMA model seems to better reflect the multifaceted and complicated character of the soil quality, producing a classification that is both statistically sound and more in line with the complex reality seen during field investigations. For a more thorough evaluation of soil quality, the GMA model could be a more realistic and sensitive technique. The output of the GMA model, which provides a more comprehensive coverage of the “Low” and “Moderate” quality classes, may offer an improved and ecologically realistic representation of soil distribution in the hyper-arid environment of Najran, even though there is a strong statistical correlation with the WA method. These results aligns with previous studies as Biswas et al. (2019) reported that, the soil quality index generated via principal component analysis (weighted additive) with substantially loaded (> 0.75) variables had a lower connection with wheat grain yields compared to the geometric mean approach. It is concluded that, with or without additional development, the geometric mean approach for soil health score can be applied in comparable environments worldwide. Additionally, Raiesi and Kabiri (2016) showed that GMA of microbial and enzyme activity were the most significant indicators that successfully distinguished tillage regimes and could be used to track improvements in soil quality in semiarid environments.

The literature shows that each approach (GMA, and WA) has unique benefits and drawbacks. In areas like Najran, where conditions vary greatly over short distances, GMA provides a more acceptable portrayal of SQ, which can be distorted by extreme values (Mikkonen et al., 2018). However, the geometric mean’s applicability in some datasets may be limited because it cannot be computed when any indicator value is zero or negative.

It has been demonstrated that the soil quality indices produced by the weighted additive model have a strong correlation with crop yield (Mukherjee and Lal, 2014). The subjectivity of weight assignment is the main drawback of the WA method. Weight values for some indicators were “arbitrarily chosen” based on the number of representative indicators rather than independent validation, as Mukherjee and Lal (2014) stated clearly. Results from several studies may not be comparable as a result of this subjectivity.

The discussion highlights that Najran’s SQ reflects the interplay between geomorphological setting, soil fertility, and land use intensity. The higher quality in wadi systems demonstrates the potential for targeted agricultural optimization, while the lower quality in the eastern desert underlines the need for conservation and monitoring. The insights derived from this study contribute not only to regional soil management but also establish a benchmark for integrating GMA and GIS in dryland monitoring frameworks globally.

5 Best practices for most agricultural assessment and land evaluation for the study area

The following best practices are suggested for evaluating agricultural soil quality in arid regions based on the literature.

• Combining statistical and geostatistical methodologies is recommended as they are efficient and simple to use in areas with limited data availability (Abuzaid et al., 2022).

• The use of GIS and remote sensing technologies is also a best practice because they can aid with sustainable land management and land suitability studies, which will enable regional governments and decision makers support precision farming (Baroudy et al., 2020).

• Furthermore, machine learning approaches such as artificial neural network (ANN) model should be used to evaluate soil quality since it may help regional governments and decision-makers find the best ways to improve soil quality, put into practice efficient soil management techniques, and solve the problem of food security (El Behairy et al., 2024).

• Finally, In order to close the gap between output and consumption, it is crucial to regularly evaluate the quality of the soil in order to determine the elements that limit SQ and to maintain a high crop yield.

6 Limitations of the current study

This study provides valuable insights into SQ despite the limited number of soil samples. The challenging environment, which was made up of steep hills and mountainous areas, limited physical access. Additionally, it was unable to obtain samples from otherwise suitable locations due to private farms and limited properties, which resulted in a sampling network that was less dense and uniform than first planned. While the dataset forms a robust foundation, it may not fully capture the spatial variability of soil characteristics across the region, highlighting the need for additional sampling to improve representativeness and reliability. Interpolation using Inverse Distance Weighting (IDW), though computationally simple and effective for local variations, has inherent limitations. Its tendency to overly smooth data and its lack of error estimation can introduce uncertainties, particularly in areas with complex transitions or sparse data points. Advanced geostatistical methods, such as kriging or hybrid approaches, could improve interpolation accuracy in future studies. A direct validation of the SQI outputs based on crop yield was not performed due to the lack of plot-level crop yield data for the Najran region.

7 Conclusion

When seeking to map and calculate soil properties, large quantities of data need to be organised and processed, and GIS provides an effective framework for this purpose. This study represents the first effort to systematically evaluate SQ in Najran Province, a region where data availability has been notably limited. The 40 field-collected soil samples form a unique and valuable dataset for understanding soil variability in this dryland environment. Five SQ classes across the Najran area were identified using the GMA model: ≈3% of the area has a very low SQ value; ≈46% a low SQ value; ≈41% a moderate SQ value; ≈9% a high SQ value; and ≈2% has a very high SQ value. The elevated land around the small wadis leads to the deposition of nutrients and sediments into the lower lands, therefore the small wadis have both high SQ values. The drier desert landscape in the centre and east of the research area is indicative of the low and very poor SQ areas. High soil salinity, alkalisation, coarse texture, high calcium carbonate, and low organic matter content are the main limiting variables in the research area. On the basis of sensitivity analysis and cross-model validation (R² = 0.942), GMA is recommended over the weighted additive approach for soil quality assessment in arid and hyper-arid environments. This work shows that using models such as the GMA to assess SQ can enable more effective and sustainable decisions to be made in arid areas like Najran, Saudi Arabia. The geomorphological, physical, chemical, and fertility properties of soil are all considered in the GMA model, it can be used as an alternative means of measuring overall SQ in this and other similar regions, guiding decision makers in implementing policies to improve SQ. Furthermore, the integration of GMA and GIS within this framework demonstrates a robust and transferable methodology for ongoing soil assessment and possible future monitoring. This combined approach allows continuous assessment of soil degradation processes and can be adapted for regional-scale sustainability planning in other dryland systems globally. This study underlines the need for further data collection efforts to refine the SQI and improve our understanding of soil dynamics in arid regions. Expanding the dataset to include more samples across diverse landforms will provide a more comprehensive evaluation of soil health and inform sustainable land management practices. Policymakers and researchers are encouraged to build on this foundation to enhance soil conservation strategies and agricultural productivity in Najran and beyond. By addressing the data limitations and leveraging innovative methodologies, this study serves as a foundational step toward sustainable soil management in the region and the establishment of a benchmark for integrating GMA and GIS in dryland monitoring frameworks globally. To provide the most rigorous form of model validation, future studies should focus on the collection of seasonal crop yield records in the identified SQ classes.

References

• 1

  Abd El AalA.MasoudA. A. (2018). Geotechnical mapping of Najran soils for safe urban expansion, Najran region, Saudi Arabia. Geotech. Geol. Eng.36, 2003–2020. doi: 10.1007/s10706-017-0442-2

  • CrossRef
  • Google Scholar

• 2

  AbdallahA. M.JatH. S.ChoudharyM.AbdelatyE. F.SharmaP. C.JatM. L. (2021). Conservation agriculture effects on soil water holding capacity and water-saving varied with management practices and agroecological conditions: a review. Agronomy11:1681. doi: 10.3390/agronomy11091681

  • CrossRef
  • Google Scholar

• 3

  Abdel-FattahM. K.MohamedE. S.WagdiE. M.ShahinS. A.AldosariA. A.LasaponaraR.et al. (2021). Quantitative evaluation of soil quality using principal component analysis: the case study of El-Fayoum depression Egypt. Sustainability13:1824. doi: 10.3390/su13041824

  • CrossRef
  • Google Scholar

• 4

  Abdel-KaderF. H.YacoubR. K.. (2005). “Land resources assessment of landmine-affected areas, northwest of Egypt,” in Suitma Symposium; The National Information and Documentation Centre (NIDOC): Cairo, Egypt.

  • Google Scholar

• 5

  AbdellatifM. A.El BaroudyA. A.ArshadM.MahmoudE. K.SalehA. M.MoghanmF. S.et al. (2021). A GIS-based approach for the quantitative assessment of soil quality and sustainable agriculture. Sustainability13:13438. doi: 10.3390/su132313438

  • CrossRef
  • Google Scholar

• 6

  AbdelRahmanM. A.ShalabyA.MohamedE. S. (2019). Comparison of two soil quality indices using two methods based on geographic information system. Egypt. J. Remote Sens. Space Sci.22, 127–136. doi: 10.1016/j.ejrs.2018.03.001

  • CrossRef
  • Google Scholar

• 7

  AbrolI. P.YadavJ. S. P.MassoudF. I. (1988). Salt-Affected Soils and their Management (FAO Soils Bulletin No. 39). Rome: Food and Agriculture Organization of the United Nations.

  • Google Scholar

• 8

  AbuzaidA. S.AbdelatifA. D. (2022). Assessment of desertification using modified MEDALUS model in the North Nile Delta, Egypt. Geoderma405:115400. doi: 10.1016/j.geoderma.2021.115400

  • CrossRef
  • Google Scholar

• 9

  AbuzaidA. S.AbdellatifA. D.FadlM. E. (2021). Modeling soil quality in Dakahlia governorate, Egypt using GIS techniques. Egypt. J. Remote Sens. Space Sci.24, 255–264. doi: 10.1016/j.ejrs.2020.01.003

  • CrossRef
  • Google Scholar

• 10

  AbuzaidA. S.MazrouY. S.El BaroudyA. A.DingZ.ShokrM. S. (2022). Multi-indicator and geospatial based approaches for assessing variation of land quality in arid agroecosystems. Sustainability14:5840. doi: 10.3390/su14105840

  • CrossRef
  • Google Scholar

• 11

  AfzaliS. F.KhanamaniA.MaskooniE. K.BerndtssonR. (2021). Quantitative assessment of environmental sensitivity to desertification using the modified MEDALUS model in a semiarid area. Sustainability13:7817. doi: 10.3390/su13147817

  • CrossRef
  • Google Scholar

• 12

  AhadT.KanthT. A.NabiS. (2015). Soil bulk density as related to texture, organic matter content and porosity in kandi soils of district Kupwara (Kashmir Valley), India. Geography4, 198–200. doi: 10.36106/ijsr

  • CrossRef
  • Google Scholar

• 13

  AhogleA. M. A.AlladassiF. K.AkploT. M.AzontondeH. A.HoungnandanP. (2022). Assessing soil organic carbon stocks and particle-size fractions across cropping systems in the Kiti sub-watershed in Central Benin. C8:67. doi: 10.3390/c8040067

  • CrossRef
  • Google Scholar

• 14

  Al OtaibiF. A.AlghamdiS. A.Abo-ElyousrK. A. (2024). The influence of salinity on plant growth and amendment strategies. Sohag J. Sci.9, 261–267. doi: 10.21608/sjsci.2024.258471.1168

  • CrossRef
  • Google Scholar

• 15

  AlakaylehZ.ClementT. P.FangX. (2018). Understanding the changes in hydraulic conductivity values of coarse-and fine-grained porous media mixtures. Water10:313. doi: 10.3390/w10030313

  • CrossRef
  • Google Scholar

• 16

  Al-DosaryN. M. N. (2022). Evaluation of soil characteristics for agricultural machinery management and cropping requirements in AL Aflaj oasis, Saudi Arabia. Sustainability14:7991. doi: 10.3390/su14137991

  • CrossRef
  • Google Scholar

• 17

  AlghamdiA. S.MooreT. W. (2014). Analysis and comparison of trends in extreme temperature indices in Riyadh City, Kingdom of Saudi Arabia, 1985–2010. J. Climatol.2014:560985. doi: 10.1155/2014/560985

  • CrossRef
  • Google Scholar

• 18

  Al-GhamdiA. A.TadesseY.AdgabaN.AlghamdiA. G. (2021). Soil degradation and restoration in southwestern Saudi Arabia through investigation of soil physiochemical characteristics and nutrient status as indicators. Sustainability13:9169. doi: 10.3390/su13169169

  • CrossRef
  • Google Scholar

• 19

  AliR. R.MoghanmF. S. (2013). Variation of soil properties over the landforms around Idku lake, Egypt. Egypt. J. Remote Sens. Space Sci.16, 91–101. doi: 10.1016/j.ejrs.2013.04.001

  • CrossRef
  • Google Scholar

• 20

  AlmazrouiM. (2020). Changes in temperature trends and extremes over Saudi Arabia for the period 1978–2019. Adv. Meteorol.2020:8828421. doi: 10.1155/2020/8828421

  • CrossRef
  • Google Scholar

• 21

  AlmazrouiM.IslamM. N.JonesP. D.AtharH.RahmanM. A. (2012). Recent climate change in the Arabian peninsula: seasonal rainfall and temperature climatology of Saudi Arabia for 1979–2009. Atmos. Res.111, 29–45. doi: 10.1016/j.atmosres.2012.02.013

  • CrossRef
  • Google Scholar

• 22

  Al-ShantiA. M. (1993). Geology of Arabian Shield. Jeddah: Center of Scientific Research, King Abdul Aziz University.

  • Google Scholar

• 23

  BabakO.DeutschC. V. (2009). Statistical approach to inverse distance interpolation. Stoch. Environ. Res. Risk Assess.23, 543–553. doi: 10.1007/s00477-008-0226-6

  • CrossRef
  • Google Scholar

• 24

  BaroudyA. A. E.AliA. M.MohamedE. S.MoghanmF. S.ShokrM. S.SavinI.et al. (2020). Modeling land suitability for rice crop using remote sensing and soil quality indicators: the case study of the Nile delta. Sustainability12:9653. doi: 10.3390/su12229653

  • CrossRef
  • Google Scholar

• 25

  BaruahT. C.BorthakurH. P. (1997). A Textbook of soil Analysis. New Delhi: Vikas Publishing House Pvt. Ltd., 118–132.

  • Google Scholar

• 26

  BashourI. I.Al-MashhadyA. S.PrasadJ. D.MillerT.MazroaM. (1983). Morphology and composition of some soils under cultivation in Saudi Arabia. Geoderma29, 327–340. doi: 10.1016/0016-7061(83)90019-8

  • CrossRef
  • Google Scholar

• 27

  BassounyM.AbuzaidA. (2017). Impact of biogas slurry on some physical properties in sandy and calcareous soils, Egypt. Int. J. Plant Soil Sci.16, 1–11. doi: 10.9734/IJPSS/2017/33388

  • CrossRef
  • Google Scholar

• 28

  BiswasJ. C.KalraN.ManiruzzamanM.NaherU. A.HaqueM. M. (2019). Soil health assessment methods and relationship with wheat yield. Open J Soil Sci9, 189–205. doi: 10.4236/ojss.2019.99011

  • CrossRef
  • Google Scholar

• 29

  BlagaL. (2012). Aspects regarding the significance of the curvature types and values in the studies of geomorphometry assisted by GIS. Anal. Univ. Oradea Ser. Geogr22, 327–337.

  • Google Scholar

• 30

  BorgaM.VizzaccaroA. (1997). On the interpolation of hydrologic variables: formal equivalence of multiquadratic surface fitting and kriging. J. Hydrol.195, 160–171. doi: 10.1016/S0022-1694(96)03250-7

  • CrossRef
  • Google Scholar

• 31

  BogunovicI.KljakK.DuganI.GrbešaD.TelakL. J.DuvnjakM.et al. (2022). Grassland management impact on soil degradation and herbage nutritional value in a temperate humid environment. Agriculture12:921. doi: 10.3390/agriculture12070921

  • CrossRef
  • Google Scholar

• 32

  BuiQ. T.JametC.VantrepotteV.MériauxX.CauvinA.MograneM. A. (2022). Evaluation of sentinel-2/MSI atmospheric correction algorithms over two contrasted French coastal waters. Remote Sens.14:1099. doi: 10.3390/rs14051099

  • CrossRef
  • Google Scholar

• 33

  ÇalS.BarikK. (2020). Hydraulic conductivity values of soils in different soil processing conditions. Alınteri Zirai Bilimler Dergisi35, 132–138. doi: 10.28955/alinterizbd.740904

  • CrossRef
  • Google Scholar

• 34

  CelisR. A. O.GamboaC. H.PascualJ. A.RosM. (2024). Conceptual and practical challenges of assessing soil quality. Soil Use Manag.40:e13137. doi: 10.1111/sum.13137

  • CrossRef
  • Google Scholar

• 35

  ChaudhryH.VasavaH. B.ChenS.SauretteD.BeriA.GillespieA.et al. (2024). Evaluating the soil quality index using three methods to assess soil fertility. Sensors24:864. doi: 10.3390/s24030864,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 36

  ĆirićV.PrekopN.ŠeremešićS.VojnovB.PejićB.RadovanovićD.et al. (2023). The implication of cation exchange capacity (CEC) assessment for soil quality management and improvement. Poljoprivreda i Sumarstvo69, 113–134. doi: 10.17707/AgricultForest.69.4.08

  • CrossRef
  • Google Scholar

• 37

  ConantR. T.RyanM. G.ÅgrenG. I.BirgeH. E.DavidsonE. A.EliassonP. E.et al. (2011). Temperature and soil organic matter decomposition rates–synthesis of current knowledge and a way forward. Glob. Change Biol.17, 3392–3404. doi: 10.1111/j.1365-2486.2011.02496.x

  • CrossRef
  • Google Scholar

• 38

  CrapartC.AnquetinS.BlanchetJ.DiedhiouA. (2025). Global projections of aridity index for mid and long-term future based on CMIP6 scenarios. EGUsphere2025, 1–32. doi: 10.5194/hess-30-163-2026

  • CrossRef
  • Google Scholar

• 39

  DeutschC. V. (2003). “Geostatistics,” in Encyclopedia of Physical Science and Technology, ed. MeyersR. A.. 3rd ed (New York, NY, 2003: Academic Press), 697–707.

  • Google Scholar

• 40

  DexterA. R.RichardG.ArrouaysD.CzyżE. A.JolivetC.DuvalO. (2008). Complexed organic matter controls soil physical properties. Geoderma144, 620–627. doi: 10.1016/j.geoderma.2008.01.022

  • CrossRef
  • Google Scholar

• 41

  DobosE.MontanarellaL. (2005). An SRTM based Procedure to Delineate SOTER Terrain Units on 1: 1 and 1: 5 million Scales (EUR 21571 EN). Luxembourg: Office for Official Publications of the European Communities.

  • Google Scholar

• 42

  DobosE.NormanB.WorstellB. (2002). The use of DEM and satellite data for regional scale soil databases. Agrokém. Talajt.51, 263–272. doi: 10.1556/agrokem.51.2002.1-2.31

  • CrossRef
  • Google Scholar

• 43

  DoranJ. W.JonesA. J. (1996). Methods for Assessing soil Quality. Madison, WI: Soil Science Society of America.

  • Google Scholar

• 44

  El BaroudyA. A. (2016). Mapping and evaluating land suitability using a GIS-based model. Catena140, 96–104. doi: 10.1016/j.catena.2015.12.010

  • CrossRef
  • Google Scholar

• 45

  El BehairyR. A.El ArwashH. M.El BaroudyA. A.IbrahimM. M.MohamedE. S.RebouhN. Y.et al. (2024). An accurate approach for predicting soil quality based on machine learning in drylands. Agriculture14:627. doi: 10.3390/agriculture14040627

  • CrossRef
  • Google Scholar

• 46

  El BehairyR. A.El BaroudyA. A.IbrahimM. M.KheirA. M. S.ShokrM. S. (2021). Modelling and assessment of irrigation water quality index using GIS in semi-arid region for sustainable agriculture. Water Air Soil Pollut.232:352. doi: 10.1007/s11270-021-05310-0

  • CrossRef
  • Google Scholar

• 47

  El-AalA. A.ZakheraM.Al SaiariM.TolbaA. (2021). Determination of the geomechanical and chemical properties of carbonate rocks along Najran, Sharourah District, Saudi Arabia: implications for construction and industrial purposes. Arab. J. Geosci.14:1694. doi: 10.1007/s12517-021-08135-7

  • CrossRef
  • Google Scholar

• 48

  FadlM. E.AbdelRahmanM. A.El-DesokyA. I.SayedY. A. (2024). Assessing soil productivity potential in arid region using remote sensing vegetation indices. J. Arid Environ.222:105166. doi: 10.1016/j.jaridenv.2024.105166

  • CrossRef
  • Google Scholar

• 49

  FadlM. E.AbuzaidA. S.AbdelRahmanM. A.BiswasA. (2021). Evaluation of desertification severity in El-Farafra oasis, Western Desert of Egypt: application of modified MEDALUS approach using wind erosion index and factor analysis. Land11:54. doi: 10.3390/land11010054

  • CrossRef
  • Google Scholar

• 50

  FAO (2006). Guidelines for soil description. 4th Edn Rome: FAO.

  • Google Scholar

• 51

  FosterJ. E.Lopez‐CalvaL. F.SzekelyM. (2005). Measuring the distribution of human development: methodology and an application to Mexico. J. Hum. Dev.6, 5–25. doi: 10.1080/1464988052000342220

  • CrossRef
  • Google Scholar

• 52

  FuentesJ. P.FluryM.BezdicekD. F. (2004). Hydraulic properties in a silt loam soil under natural prairie, conventional till, and no‐till. Soil Sci. Soc. Am. J.68, 1679–1688. doi: 10.2136/sssaj2004.1679

  • CrossRef
  • Google Scholar

• 53

  GaoS.YanK.LiuJ.PuJ.ZouD.QiJ.et al. (2024). Assessment of remote-sensed vegetation indices for estimating forest chlorophyll concentration. Ecol. Indic.162:112001. doi: 10.1016/j.ecolind.2024.112001

  • CrossRef
  • Google Scholar

• 54

  GonzalezR. C. (2009). Digital image processing. London: Pearson Education India.

  • Google Scholar

• 55

  GrantC. D.DexterA. R.OadesJ. M. (1992). Residual effects of additions of calcium compounds on soil structure and strength. Soil Tillage Res.22, 283–297. doi: 10.1016/0167-1987(92)90045-D

  • CrossRef
  • Google Scholar

• 56

  GuoL. B.GiffordR. M. (2002). Soil carbon stocks and land use change: a meta analysis. Glob. Change Biol.8, 345–360. doi: 10.1046/j.1354-1013.2002.00486.x

  • CrossRef
  • Google Scholar

• 57

  HazeltonP.MurphyB. (2016). Interpreting soil test Results: What do all the Numbers mean?Clayton, VIC: CSIRO Publishing.

  • Google Scholar

• 58

  HornR.SmuckerA. (2005). Structure formation and its consequences for gas and water transport in unsaturated arable and forest soils. Soil Tillage Res.82, 5–14. doi: 10.1016/j.still.2005.01.002

  • CrossRef
  • Google Scholar

• 59

  HorváthB.Opara-NadiO.BeeseF. (2005). A simple method for measuring the carbonate content of soils. Soil Sci. Soc. Am. J.69, 1066–1068. doi: 10.2136/sssaj2004.0010

  • CrossRef
  • Google Scholar

• 60

  HouY.LiQ.ZhangC.LuG.YeZ.ChenY.et al. (2021). The state-of-the-art review on applications of intrusive sensing, image processing techniques, and machine learning methods in pavement monitoring and analysis. Engineering7, 845–856. doi: 10.1016/j.eng.2020.07.030

  • CrossRef
  • Google Scholar

• 61

  HuangJ.HarteminkA. E. (2020). Soil and environmental issues in sandy soils. Earth-Sci. Rev.208:103295. doi: 10.1016/j.earscirev.2020.103295

  • CrossRef
  • Google Scholar

• 62

  HutchinsonM. F. (1993). “On thin plate splines and kriging,” in Computing Science and Statistics, eds. TarterM. E.LockM. D. (Berkeley, CA: Interface Foundation of North America), 55–62.

  • Google Scholar

• 63

  IndoriaA. K.SharmaK. L.ReddyK. S. (2020). “Hydraulic properties of soil under warming climate,” in Climate change and soil Interactions, eds. PietrzykowskiM.PrasadM. N. V. (New York, NY: Elsevier), 473–508.

  • Google Scholar

• 64

  JasińskaJ.SewerniakP.MarkiewiczM. (2019). Links between slope aspect and rate of litter decomposition on inland dunes. Catena 172, 501–508. doi: 10.1016/j.catena.2018.09.025.

  • CrossRef
  • Google Scholar

• 65

  JiayingM. A.TingtingC.JieL.WeimengF. U.BaohuaF.GuangyanL.et al. (2022). Functions of nitrogen, phosphorus and potassium in energy status and their influences on rice growth and development. Rice Sci.29, 166–178. doi: 10.1016/j.rsci.2022.01.005

  • CrossRef
  • Google Scholar

• 66

  KaplanS.GarrickB. J. (1981). On the quantitative definition of risk. Risk Anal.1, 11–27. doi: 10.1111/j.1539-6924.1981.tb01350.x

  • CrossRef
  • Google Scholar

• 67

  KassasM. (1995). Desertification: a general review. J. Arid Environ.30, 115–128. doi: 10.1016/S0140-1963(05)80063-1

  • CrossRef
  • Google Scholar

• 68

  KettlerT. A.DoranJ. W.GilbertT. L. (2001). Simplified method for soil particle‐size determination to accompany soil‐quality analyses. Soil Sci. Soc. Am. J.65, 849–852. doi: 10.2136/sssaj2001.653849x

  • CrossRef
  • Google Scholar

• 69

  LalR. (2015). Restoring soil quality to mitigate soil degradation. Sustainability7, 5875–5895. doi: 10.3390/su7055875

  • CrossRef
  • Google Scholar

• 70

  LalR. (2019). Accelerated soil erosion as a source of atmospheric CO2. Soil Tillage Res.188, 35–40. doi: 10.1016/j.still.2018.02.001

  • CrossRef
  • Google Scholar

• 71

  León-MorenoC. E.Rojas-MolinaJ.Castilla-CamposC. E. (2019). Physicochemical characteristics of cacao (Theobroma cacao L.) soils in Colombia: are they adequate to improve productivity?Agron. Colomb.37, 28–38. doi: 10.15446/agron.colomb.v37n1.70545

  • CrossRef
  • Google Scholar

• 72

  LeeS. (2014). Geological application of geographic information system. Korea Inst. Geosci. Miner. Resour. 9, 109–118.

  • Google Scholar

• 73

  LiC.PanC. (2020). Overland runoff erosion dynamics on steep slopes with forages under field simulated rainfall and inflow. Hydrol. Process.34, 1794–1809. doi: 10.1002/hyp.13692

  • CrossRef
  • Google Scholar

• 74

  LiaoD.DengY.DuanX.CaiC.DingS. (2022). Variations in weathering characteristics of soil profiles and response of the Atterberg limits in the granite hilly area of South China. Catena215:106325. doi: 10.1016/j.catena.2022.106325

  • CrossRef
  • Google Scholar

• 75

  MaierS.TammA.WuD.CaesarJ.GrubeM.WeberB. (2018). Photoautotrophic organisms control microbial abundance, diversity, and physiology in different types of biological soil crusts. ISME J.12, 1032–1046. doi: 10.1038/s41396-018-0062-8,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 76

  MalenovskýZ.RottH.CihlarJ.SchaepmanM. E.García-SantosG.FernandesR.et al. (2012). Sentinels for science: potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land. Remote Sens. Environ. 120, 91–101. doi: 10.1016/j.rse.2011.09.026

  • CrossRef
  • Google Scholar

• 77

  MatherP. M.KochM. (2022). Computer Processing of Remotely-Sensed Images. New York, NY: John Wiley and Sons.

  • Google Scholar

• 78

  MaxwellK.JohnsonG. N. (2000). Chlorophyll fluorescence—a practical guide. J. Exp. Bot.51, 659–668. doi: 10.1093/jexbot/51.345.659,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 79

  MikkonenH. G.ClarkeB. O.DasikaR.WallisC. J.ReichmanS. M. (2018). Evaluation of methods for managing censored results when calculating the geometric mean. Chemosphere191, 412–416. doi: 10.1016/j.chemosphere.2017.10.038,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 80

  MinasnyB.McBratneyA. B. (2018). Limited effect of organic matter on soil available water capacity. Eur. J. Soil Sci.69, 39–47. doi: 10.1111/ejss.12475

  • CrossRef
  • Google Scholar

• 81

  MohamedE. S. (2013). Spatial assessment of desertification in North Sinai using modified MEDLAUS model. Arab. J. Geosci.6, 4647–4659. doi: 10.1007/s12517-012-0723-2

  • CrossRef
  • Google Scholar

• 82

  MohamedE. S.BaroudyA. A. E.El-BeshbeshyT.EmamM.BelalA. A.ElfadalyA.et al. (2020). Vis-nir spectroscopy and satellite landsat-8 oli data to map soil nutrients in arid conditions: a case study of the northwest coast of Egypt. Remote Sens.12:3716. doi: 10.3390/rs12223716

  • CrossRef
  • Google Scholar

• 83

  MoinetG. Y.MoinetM.HuntJ. E.RumpelC.ChabbiA.MillardP. (2020). Temperature sensitivity of decomposition decreases with increasing soil organic matter stability. Sci. Total Environ.704:135460. doi: 10.1016/j.scitotenv.2019.135460,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 84

  MooreF.SheykhiV.SalariM.BagheriA. (2016). Soil quality assessment using GIS-based chemometric approach and pollution indices: Nakhlak mining district, Central Iran. Environ. Monit. Assess.188:214. doi: 10.1007/s10661-016-5152-3,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 85

  MukherjeeA.LalR. (2014). Comparison of soil quality index using three methods. PLoS One9:e105981. doi: 10.1371/journal.pone.0105981,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 86

  NabiollahiK.Taghizadeh-MehrjardiR.KerryR.MoradianS. (2017). Assessment of soil quality indices for salt-affected agricultural land in Kurdistan Province, Iran. Ecol. Indic.83, 482–494. doi: 10.1016/j.ecolind.2017.08.001

  • CrossRef
  • Google Scholar

• 87

  NachshonU. (2018). Cropland soil salinization and associated hydrology: trends, processes and examples. Water. 10:1030. doi: 10.3390/w10081030

  • CrossRef
  • Google Scholar

• 88

  OgleS. M.AlsakerC.BaldockJ.BernouxM.BreidtF. J.McConkeyB.et al. (2019). Climate and soil characteristics determine where no-till management can store carbon in soils and mitigate greenhouse gas emissions. Sci. Rep.9:11665. doi: 10.1038/s41598-019-47861-7,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 89

  OlayaV.ConradO. (2009). Geomorphometry in SAGA. Dev. Soil Sci.33, 293–308. doi: 10.1016/S0166-2481(08)00012-3

  • CrossRef
  • Google Scholar

• 90

  ÖzkanB.DengizO.Turanİ. D. (2020). Site suitability analysis for potential agricultural land with spatial fuzzy multi-criteria decision analysis in regional scale under semi-arid terrestrial ecosystem. Sci. Rep.10:22074. doi: 10.1038/s41598-020-79105-4,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 91

  PlanchonO.DarbouxF. (2002). A fast, simple and versatile algorithm to fill the depressions of digital elevation models. Catena46, 159–176. doi: 10.1016/S0341-8162(01)00164-3

  • CrossRef
  • Google Scholar

• 92

  PrăvălieR.PatricheC.SăvulescuI.SîrodoevI.BandocG.SfîcăL. (2020). Spatial assessment of land sensitivity to degradation across Romania. A quantitative approach based on the modified MEDALUS methodology. Catena187:104407. doi: 10.1016/j.catena.2019.104407

  • CrossRef
  • Google Scholar

• 93

  RaiesiF.KabiriV. (2016). Identification of soil quality indicators for assessing the effect of different tillage practices through a soil quality index in a semi-arid environment. Ecol. Indic.71, 198–207. doi: 10.1016/j.ecolind.2016.06.061

  • CrossRef
  • Google Scholar

• 94

  RaunW. R.SolieJ. B.JohnsonG. V.StoneM. L.LukinaE. V.ThomasonW. E.et al. (2001). In‐season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J.93, 131–138. doi: 10.2134/agronj2001.931131x

  • CrossRef
  • Google Scholar

• 95

  RichardsL. A. (1954). Diagnosis and Improvement of saline and alkali Soils. Washington, DC: U.S. Department of Agriculture.

  • Google Scholar

• 96

  RitchieJ. C.McCartyG. W.VenterisE. R.KasparT. C. (2007). Soil and soil organic carbon redistribution on the landscape. Geomorphology89, 163–171. doi: 10.1016/j.geomorph.2006.07.021

  • CrossRef
  • Google Scholar

• 97

  RobertsE. A.SheleyR. L.LawrenceR. L. (2004). Using sampling and inverse distance weighted modeling for mapping invasive plants. West. N. Am. Nat.12, 312–323.

  • Google Scholar

• 98

  RouseJ. W.HaasR. H.DeeringD. W.SchellJ. A.HarlanJ. C. (1974) Monitoring the vernal Advancement and Retrogradation (green Wave effect) of natural Vegetation (No. E75-10354)

  • Google Scholar

• 99

  SableE. G. (1985). Geologic Map of the Najran Quadrangle, Sheet 17G, Kingdom of Saudi Arabia (with Explanatory Notes), Geoscience Map GM-78A. Jeddah: Ministry of Petroleum and Mineral Resources, Deputy Ministry for Mineral Resources.

  • Google Scholar

• 100

  SakinE. (2012). Organic carbon organic matter and bulk density relationships in arid-semi arid soils in Southeast Anatolia region. Afr. J. Biotechnol.11:1373. doi: 10.5897/AJB11.2297

  • CrossRef
  • Google Scholar

• 101

  SanchezP. A. (2015). En route to plentiful food production in Africa. Nat. Plants1:14014. doi: 10.1038/nplants.2014.14,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 102

  SchwanghartW.JarmerT. (2011). Linking spatial patterns of soil organic carbon to topography—a case study from South-Eastern Spain. Geomorphology126, 252–263. doi: 10.1016/j.geomorph.2010.11.008

  • CrossRef
  • Google Scholar

• 103

  SeibertJ.StendahlJ.SørensenR. (2007). Topographical influences on soil properties in boreal forests. Geoderma141, 139–148. doi: 10.1016/j.geoderma.2007.05.013

  • CrossRef
  • Google Scholar

• 104

  ShadfanH.MashhadyA.EterA.HussenA. A. (1984). Mineral composition of selected soils in Saudi Arabia. Z. Pflanzenernähr. Bodenk.147, 657–668.

  • Google Scholar

• 105

  SharmaS.LishikaB.KaushalS. (2023). Soil quality indicators: a comprehensive review. Int. J. Plant Soil Sci.35, 315–325. doi: 10.9734/ijpss/2023/v35i224139

  • CrossRef
  • Google Scholar

• 106

  ShokrM. S.AbdellatifM. A.El BaroudyA. A.ElnasharA.AliE. F.BelalA. A.et al. (2021). Development of a spatial model for soil quality assessment under arid and semi-arid conditions. Sustainability13:2893. doi: 10.3390/su13052893

  • CrossRef
  • Google Scholar

• 107

  ShokrM. S.EL BaroudyA. A.FullenM. A.El-BeshbeshyT. R.AliR. R.ElhalimA.et al. (2016). Mapping of heavy metal contamination in alluvial soils of the middle Nile Delta of Egypt. J. Environ. Eng. Landsc. Manag.24, 218–231. doi: 10.3846/16486897.2016.1184152

  • CrossRef
  • Google Scholar

• 108

  ShokrM. S.JalhoumM. E.SayedA. S.SaeedM.RebouhN. Y.MohamedE. S.et al. (2025). Assessment of soil quality in arid zones using principal component analysis and GIS-based modeling. PLoS One20:e0337063. doi: 10.1371/journal.pone.0337063,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 109

  SiegelA. F. (2016). Practical Business Statistics. 7th Edn Cambridge, MA: Academic Press.

  • Google Scholar

• 110

  SilalahiF. E. S.ArifiantiY.HidayatF. (2019). Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geosci. Lett.6:10. doi: 10.1186/s40562-019-0140-4,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 111

  SilleosN. G.AlexandridisT. K.GitasI. Z.PerakisK. (2006). Vegetation indices: advances made in biomass estimation and vegetation monitoring in the last 30 years. Geocarto Int.21, 21–28. doi: 10.1080/10106040608542399

  • CrossRef
  • Google Scholar

• 112

  Soil Survey Staff (2017). Soil Survey Manual in USDA Handbook 18. Washington, DC: USDA.

  • Google Scholar

• 113

  SoltanpourP. N. (1991). “Determination of nutrient availability and elemental toxicity by AB-DTPA soil test and ICPS,” in Advances in Soil Science: Volume 16, eds. JalotaS. K.KayB. D.KomarneniS. (New York, NY: Springer), 165–190.

  • Google Scholar

• 114

  TavakolM.WetzelA. (2020). Factor analysis: a means for theory and instrument development in support of construct validity. Int. J. Med. Educ.11, 245–247. doi: 10.5116/ijme.5f96.0f4a,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 115

  ToblerW. R. (1970). A computer movie simulating urban growth in the Detroit region. Econ. Geogr. 46, 234–240. doi: 10.2307/143141

  • CrossRef
  • Google Scholar

• 116

  van ReeuwijkL. P. (1995). Procedures for soil Analysis. 5th Edn Wageningen: International Soil Reference and Information Centre (ISRIC).

  • Google Scholar

• 117

  VogelR. M. (2022). The geometric mean?Commun. Stat. Theory Methods51, 82–94. doi: 10.1080/03610926.2020.1743313

  • CrossRef
  • Google Scholar

• 118

  Von WandruszkaR. (2006). Phosphorus retention in calcareous soils and the effect of organic matter on its mobility. Geochem. Trans.7:6. doi: 10.1186/1467-4866-7-6,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 119

  WahbaG. (1990). Spline Models for Observational data. Philadelphia, PA: Society for Industrial and Applied Mathematics.

  • Google Scholar

• 120

  WeberD.EnglundE. (1992). Evaluation and comparison of spatial interpolators. Math. Geol.24, 381–391. doi: 10.1007/BF00891270

  • CrossRef
  • Google Scholar

• 121

  YaoR. J.YangJ. S.ZhangT. J.GaoP.YuS. P.WangX. P. (2013). Short-term effect of cultivation and crop rotation systems on soil quality indicators in a coastal newly reclaimed farming area. J. Soils Sediments13, 1335–1350. doi: 10.1007/s11368-013-0739-6

  • CrossRef
  • Google Scholar

• 122

  YousifI. A.Abdel HamidA.AldabaaA. A.HassaneinS. A.RebouhN. Y.MohamedE. S.et al. (2025). Soil quality indicator-based land productivity modelling for agricultural sustainability. PLoS One20:e0316840. doi: 10.1371/journal.pone.0316840,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 123

  YoussefA. M.SabtanA. A.MaerzN. H.ZabramawiY. A. (2014). Earth fissures in Wadi Najran, Kingdom of Saudi Arabia. Nat. Hazards71, 2013–2027. doi: 10.1007/s11069-013-0991-5

  • CrossRef
  • Google Scholar

• 124

  Yu-DongC. H. E. N.Huo-YanW. A. N. G.Jian-MinZ. H. O. U.LuX. I. N. G.Bai-ShuZ. H. U.Yong-CunZ. H. A. O.et al. (2013). Minimum data set for assessing soil quality in farmland of Northeast China. Pedosphere23, 564–576. doi: 10.1016/S1002-0160(13)60050-8,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 125

  ZhangF.ZhongJ.ZhaoY.CaiC.LiuW.WangQ.et al. (2024). Urbanization-induced soil organic carbon loss and microbial-enzymatic drivers: insights from aggregate size classes in Nanchang city, China. Front. Microbiol.15:1367725. doi: 10.3389/fmicb.2024.1367725,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 126

  ZimmermanD. W. (1994). A note on the influence of outliers on parametric and nonparametric tests. J. Gen. Psychol.121, 391–401. doi: 10.1080/00221309.1994.9921213

  • CrossRef
  • Google Scholar

• 127

  ZimmermanD. W. (1995). Increasing the power of nonparametric tests by detecting and downweighting outliers. J. Exp. Educ.64, 71–78. doi: 10.1080/00220973.1995.9943796

  • CrossRef
  • Google Scholar

• 128

  ZimmermanD. W. (1998). Invalidation of parametric and nonparametric statistical tests by concurrent violation of two assumptions. J. Exp. Educ.67, 55–68. doi: 10.1080/00220979809598344

  • CrossRef
  • Google Scholar