Development of a Spatial Model for Soil Quality Assessment Under Arid and Semi-Arid Conditions

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Article Development of a Spatial Model for Soil Quality Assessment under Arid and Semi-Arid Conditions

Mohamed S. Shokr 1, Mostafa. A. Abdellatif 2, Ahmed A. El Baroudy 1, Abdelrazek Elnashar 3,4 , Esmat F. Ali 5, Abdelaziz A. Belal 2 , Wael. Attia 2, Mukhtar Ahmed 6,7 , Ali A. Aldosari 8 , Zoltan Szantoi 9,10 , Mohamed E. Jalhoum 2 and Ahmed M. S. Kheir 11,*

1 Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt; [email protected] (M.S.S.); [email protected] (A.A.E.B.) 2 National Authority for Remote Sensing and Space Science (NARSS), Cairo 11843, Egypt; [email protected] (M.A.A.); [email protected] (A.A.B.); [email protected] (W.A.); [email protected] (M.E.J.) 3 State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China; [email protected] 4 Department of Natural Resources, Faculty of African Postgraduate Studies, Cairo University, Giza 12613, Egypt 5 Department of Biology, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; [email protected] 6 Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, 90183 Umeå, Sweden; [email protected] 7 Department of Agronomy, PMAS Arid Agriculture University, Rawalpindi 46300, Pakistan 8 Citation: Shokr, M.S.; Abdellatif, Geography Department, King Saud University, Riyadh 11451, Saudi Arabia; [email protected] 9 European Commission, Joint Research Centre, 21027 Ispra, Italy; M.A.; El Baroudy, A.A.; Elnashar, A.; zoltan.szantoi@remote-sensing-biodiversity.org Ali, E.F.; Belal, A.A.; Attia, W.; 10 Department of Geography and Environmental Studies, Stellenbosch University, Ahmed, M.; Aldosari, A.A.; Szantoi, Stellenbosch 7602, South Africa Z.; et al. Development of a Spatial 11 Soils, Water and Environment Research Institute, Agricultural Research Center, Giza 12112, Egypt Model for Soil Quality Assessment * Correspondence: [email protected] under Arid and Semi-Arid Conditions. Sustainability 2021, 13, Abstract: Food security has become a global concern for humanity with rapid population growth, 2893. https://doi.org/10.3390/ requiring a sustainable assessment of natural resources. Soil is one of the most important sources su13052893 that can help to bridge the food demand gap to achieve food security if well assessed and managed. The aim of this study was to determine the soil quality index (SQI) for El Fayoum depression in the Academic Editor: Western Egyptian Desert using spatial modeling for soil physical, chemical, and biological properties Georgios Koubouris based on the MEDALUS methodology. For this purpose, a spatial model was developed to evaluate

Received: 25 January 2021 the soil quality of the El Fayoum depression in the Western Egyptian Desert. The integration between Accepted: 3 March 2021 Digital Elevation Model (DEM) and Sentinel-2 satellite image was used to produce landforms and Published: 7 March 2021 digital soil mapping for the study area. Results showed that the study area located under six classes of soil quality, e.g., very high-quality class represents an area of 387.12 km2 (22.7%), high-quality class Publisher’s Note: MDPI stays neutral occupies 441.72 km2 (25.87%), the moderate-quality class represents 208.57 km2 (12.21%), slightly with regard to jurisdictional claims in moderate-quality class represents 231.10 km2 (13.5%), as well as, a low-quality class covering an area published maps and institutional afﬁl- of 233 km2 (13.60%), and very low-quality class occupies about 206 km2 (12%). The Agricultural Land iations. Evaluation System for arid and semi-arid regions (ALESarid) was used to estimate land capability. Land capability classes were non-agriculture class (C6), poor (C4), fair (C3), and good (C2) with an area 231.87 km2 (13.50%), 291.94 km2 (17%), 767.39 km2 (44.94%), and 416.07 km2 (24.4%), respectively. Land capability along with the normalized difference vegetation index (NDVI) used for validation Copyright: © 2021 by the authors. of the proposed model of soil quality. The spatially-explicit soil quality index (SQI) shows a strong Licensee MDPI, Basel, Switzerland. signiﬁcant positive correlation with the land capability and a positive correlation with NDVI at R2 This article is an open access article 0.86 (p < 0.001) and 0.18 (p < 0.05), respectively. In arid regions, the strategy outlined here can easily distributed under the terms and be re-applied in similar environments, allowing decision-makers and regional governments to use conditions of the Creative Commons the quantitative results achieved to ensure sustainable development. Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ Keywords: spatial modeling; land capability; ALESarid; GIS; remote sensing; NDVI 4.0/).

Sustainability 2021, 13, 2893. https://doi.org/10.3390/su13052893 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 2893 2 of 15

1. Introduction Soil quality (SQ) is one of the most common concepts that has emerged over the last decades and has been used to assess soil under different systems [1–3]. For practical purposes, the judgment of SQ depends on impacts of soil on crop yield, erosion, and quality of surface and ground, food, and air [4]. The meaning of the soil quality index (SQI) is the ability of the soil to function within an ecosystem boundary, whether managed or natural, and to achieve sustainability of crop productivity while maintaining soil from degradation processes [5,6]. In many regions around the world, soil quality is declining rapidly [7], due to many reasons, including, but not limited to, changes in land use types with intensive land use [8,9]. Therefore, the assessment of SQ is considered the basis for monitoring and maintaining the sustainability of agricultural systems [10]. Evaluation of SQ is based on personal knowledge, which leads to a reward for lack of data and a lack of similarity between geographical locations. Thus, SQ evaluation should be carried out in a quantitative, documented, reproducible, and spatially-explicit approach with the lowest level of subjectivity [11]. In addition, indicators that influence soil properties are needed for soil quality assessment [12,13]. Soil fertility, physical, chemical, and biological factors have a crucial role in determining soil suitability for crop production, influencing soil quality and crop yields in turn [14]. The quantification of SQ required a minimum set of data, including a small number of accurately selected soil indicators, such as physical, chemical, and biological properties [15]. Thus, the SQ indicators could be divided into stable and dynamic soil characteristics [16]. The soil texture is considered stable soil properties that have affected the difference in crop productivity, while pH, soil depth, soil water, and nitrate concentrations are dynamic soil characteristics and need to be monitored periodically. This explains the impact of the management process on the difference in plant productivity that affects dynamic soil properties, e.g., the variable rate of application of irrigation or fertilization [1]. Therefore, monitoring of soil quality parameters will help to simplify and increase awareness of the reasons and impacts of climate change and the responses required [17]. This is because soil quality is a measure of soil capacity to function to maintain productivity, to maintain environmental quality, to limit ecosystems and land use, and to promote plant and animal health [18]. Various assessment methods of the spatial variability of soil properties include geostatistics approaches based on measurements at adjacent locations with certain weights assigned to each measurement [19,20]. For instance, classical ordinary Kriging interpolation (OK) can directly illustrate the spatial variation of soil properties [21] as one of the geostatistical methods that have been widely used to evaluate and analyze spatial correlation and spatial variability of soil properties such as physical, chemical, and biological properties [22]. On the other hand, there is a significant positive correlation between soil quality and land capability, which shows that the two approaches are related to the assessment of land for crop production [23]. Recently, several land capability models have been developed to provide a quantified procedure to match land with various actual and proposed uses, particularly for the arid and semi-arid regions, including the study area, for instance, the Agricultural Land Evaluation System for arid and semi-arid regions, (ALESarid) which has been developed by [24]. This model is integrated with GIS software to calculate land capability and could provide a reasonable solution between the accuracy, ease of application, and moderate data demand [25]. Additionally, the normalized difference vegetation index (NDVI) as a biophysical parameter can be calculated from remote sensing data that are sensitive to the dynamic change of vegetation conditions, including several factors, such as soil quality [26,27]. Thus, land capability and NDVI may have better potential to precisely validate the proposed soil quality model. Consequently, soil quality assessment using GIS and remote sensing (RS) applications is very important for land assessment, facilitating reclamation and cultivation plans, but this Sustainability 2021, 13, x FOR PEER REVIEW 3 of 17

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several factors, such as soil quality [26,27]. Thus, land capability and NDVI may have better potential to precisely validate the proposed soil quality model. approachConsequently, needs to soil be appliedquality assessment in a variety using of environments, GIS and remote particularly sensing (RS) arid applica- and semi-arid regions.tions is very The important spatial modelfor land developed assessment, infacilitating this research reclamation is expected and cultivation to be a moreplans, accurate methodologybut this approach for needs assessing to be the applied spatial in distributiona variety of environments, of soil quality, particularly as it includes arid soiland physical, semi-arid regions. The spatial model developed in this research is expected to be a more chemical, and biological indicators. accurate methodology for assessing the spatial distribution of soil quality, as it includes In the current research, a spatial model for soil quality assessment was developed to soil physical, chemical, and biological indicators. evaluateIn the soil current quality research, based a spatial on physical, model for chemical, soil quality and assessment biological was soil developed properties, to RS and GISevaluate data. soil The quality results based of theon physical, proposed chemical, model and were biological correlated soil withproperties, both landRS and capability andGIS data. the The NDVI results of theof the study proposed area. model This were study correlated could with be used both land as an capability assessment and tool to helpthe NDVI decision-makers of the study area. and This land study managers could be to used map as and an assessment assess soil tool quality to help under deci- arid and semi-aridsion-makers conditions. and land managers The concept to map of and the assess current soil study quality can under be utilizedarid and tosemi other-arid sites of a similarconditions. subject. The concept of the current study can be utilized to other sites of a similar subject. 2. Materials and Methods 2. Materials and Methods Various phases were carried out to achieve the research objective, including the deﬁnitionVarious of phases the physiographic were carried out units to achieve using the the research DEM and objective, Sentinel-2 including image, the ﬁeldworkdef- to collectinition of samples the physiographic and check units the boundaries using the DEM of theand physiographic Sentinel-2 image, units, fieldwork laboratory to col- analysis lect samples and check the boundaries of the physiographic units, laboratory analysis for for physical, chemical, and biological soil properties, and the development of a spatial physical, chemical, and biological soil properties, and the development of a spatial model modelfor soil quality for soil assessment quality assessment along with its along validation with itswith validation the land capability with the and land NDVI capability of and NDVIthe study of thearea study(Figure area 1). (Figure1).

Figure 1. 1. SummarySummary of the of themethodology methodology used in used this research, in this research, including including satellite image satellite analysis, image analysis, fieldwork, laboratory analysis, spatial model, and validation. ﬁeldwork, laboratory analysis, spatial model, and validation. 2.1. Location Location of of the the Study Study Area Area ElEl Fayoum Fayoum depression depression in in the the W Westernestern D Desertesert of of Egypt Egypt covers covers an anarea area of of approxi- approximately mately 1707 km2. It is bounded by 30°15′ and 31°06′ N latitudes and 29°10′ and 29°34′ E 1707 km2. It is bounded by 30◦150 and 31◦060 N latitudes and 29◦100 and 29◦340 E longitudes longitudes (Figure S1). The climate data of the study area showed that the mean annual (Figure S1). The climate data of the study area showed that the mean annual precipitation precipitation is 7.2 mm/year and the mean minimum and maximum annual temperatures isare 7.2 14.5 mm/year and 31.0 and°C, respectively. the mean minimum The lowest and evapotranspiration maximum annual value temperatures was 1.9 mm/day are 14.5 and 31.0 ◦C, respectively. The lowest evapotranspiration value was 1.9 mm/day recorded in January, while the highest value was 7.3 mm/day. The soil moisture and temperature regimes are Torric and thermal, respectively [28].

2.2. Digital Image Processing Sentinel-2 image acquired on 14 August 2019 under clear sky conditions was used to produce landforms and digital soil mapping with the aid of the DEM of the study area. The multi-spectral bands of Sentinel-2 image have a ten-meter spatial resolution for bands Sustainability 2021, 13, x FOR PEER REVIEW 4 of 17

recorded in January, while the highest value was 7.3 mm/day. The soil moisture and temperature regimes are Torric and thermal, respectively [28].

Sustainability 2021, 13, 2893 2.2. Digital Image Processing 4 of 15 Sentinel-2 image acquired on 14 August 2019 under clear sky conditions was used to produce landforms and digital soil mapping with the aid of the DEM of the study area. The multi-spectral bands of Sentinel-2 image have a ten-meter spatial resolution for bands 2, 3, 4, and 8, respectively. The DEM provided at 30-m spatial resolution and produced 2, 3, 4, and 8, respectively. The DEM provided at 30-m spatial resolution and produced by theby NASA the NASA Shuttle Shuttle Radar RadarTopographic Topographic Mission Mission(SRTM) (Figure (SRTM) 2). (FigureSNAP and2). SNAPENVI 5.4 and ENVI were5.4 were utilized utilized for image for imageprocessing processing,, including including spectral subset, spectral radiometric subset, radiometric calibration, at- calibration, mosphericatmospheric,, and andgeometric geometric corrections. corrections. UTM Zone UTM 36N Zone coordinate 36N coordinate system using system WGS using84 WGS datum84 datum was wasassigned assigned as a projection as a projection system. system.

Figure 2. Digital elevation map for the study area. Figure 2. Digital elevation map for the study area. 2.3. Delineation of the Landform Map 2.3. Delineation of the Landform Map The visual interpretation for the multi-spectral Sentinal-2 image dropped over the DEMThe in visual ArcScene interpretation to provide for a the 3D multi vision-spectral for the Sentinal landform-2 im units’age dropped extraction. over Thisthe method DEM in ArcScene to provide a 3D vision for the landform units’ extraction. This method revealed the clear difference in elevations found in every delineated landscape. By this revealed the clear difference in elevations found in every delineated landscape. By this method, we were able to separate the different landform units based on the visual interpre- method, we were able to separate the different landform units based on the visual inter- pretationtation of of the the satellite satellite imageimage and DEM DEM in in a a3D 3D visualization visualization mode, mode, field ﬁeld check check with the with the aid aidof previousof previous studies studies that that werewere carriedcarried out out on on this this area area trying trying to togive give the themost most appro- appropriate pnomenclatureriate nomenclature to landforms to landforms [29 [29]]. .

2.4.2.4. Field Field Survey Survey and and Laboratory Laboratory Analysis Analysis FieldField surveys surveys were were conducted conducted to dig to 40 dig soil 40 profiles soil profiles,, one hundred one hundred and twenty and repre- twenty repre- sentativesentative soil soil samples samples were were taken taken from from all soil all profile. soil profile. The depth The of depth soil profiles of soil ranges profiles ranges fromfrom 40 40 to to 110 110 cm. cm. To Tobe well be well represent represented,ed, one composite one composite sample sample was collected was collected from each from each layer in the soil profiles (Figure S2a). Land use and geology maps of the study area are shown in Figure S2b,c. Each landform was represented by several soil profiles according to their characteristics. Detailed morphological descriptions of soil profiles were elaborated on the basis outlined by [30]. Soil samples were air-dried, and the fine earth (<2 mm) particles were used for chemical analysis, based on the manual of soil survey laboratory methods [31]. Based on the morphological characteristics, physical and chemical properties of the soil, we assessed soil quality and capability in the study area. Additionally, these soils are classified as sub-large groups based on the 2014 Soil Survey Staff, the World Reference Base for Soil Resources (WRB; IUSS Working Group, 2015) [28]. Sustainability 2021, 13, 2893 5 of 15

2.5. Evaluation of Soil Quality Index (SQI) SQI were calculated according to MEDALUS methodology [32], as shown in Equation (1).

1/7 SQI = (It ∗ Is ∗ Id ∗ Idr ∗ ICaCO3∗IOM ∗ Iec) (1)

where It is soil texture index, Is is slope gradient index, Id is soil depth index, Idr is drainage condition index, ICaCO3 is the content of calcium carbonate index, IOM is soil organic matter index, and Iec is soil salinity index.

2.6. Development of a Spatial Model for Evaluating Soil Quality Index The development of a spatial model was based on the Model Builder tool in ArcGIS. Model Builder was used to automate, document selected spatial analysis and data management processes as a diagram of gathered chains, which are a sequence of geo-processing tools that use the output of one process as the input to another process. The following procedures implemented in this study to assign the weighting factor of each soil indicator for getting the final SQI map (Figure3): Step 1 different soil properties interpolated from point-based to a raster layer, step 2 different soil properties raster layers from step 1 reclassified into six classes (e.g., very low, low, slightly moderate, moderate, high, and very high), the reclassified values assigned to an evaluation scale from 1 (high quality) to 6 (low quality), step 3 different class of soil properties raster layers from step 3 assigned to an index (Table S1), step 4 feeding Equation (2) the different outputs from step 3 to map Sustainability 2021, 13, x FOR PEER REVIEW 6 of 17 SQI, and step 5 reclassify the output raster layer from step 4 based on Table S2, the final resulting raster assessed and displayed as soil quality map.

FigureFigure 3. 3.Spatial Spatial modelmodel structure structure for for assessin assessingg soil soil quality. quality.

2.7. Validation of SQI Model Based on Land Capability The land capability of the study area was evaluated by ALESarid. The outputs of this model can be shown in simple and handy maps showing the spatial distribution of land capability. The extracted land capability value was compared with the soil quality index for each mapping unit in the study area using SPSS software to obtain a correlation coef- ficient between them at a 0.05 significance level (p-value).

2.8. Calculation of Normalized Difference Vegetation Index (NDVI) The NDVI was calculated on 10 August for four years (2017, 2018, 2019, and 2020). This index is a dimensionless index that reflects the presence and density of vegetation based on the simulated red and NIR reflectance, as shown in Equation (2). 푁퐼푅 − 푅 푁퐷푉퐼 = (2) 푁퐼푅 + 푅 where NIR is the near-infrared reflectance and R is the red reflectance. Sustainability 2021, 13, 2893 6 of 15

2.7. Validation of SQI Model Based on Land Capability The land capability of the study area was evaluated by ALESarid. The outputs of this model can be shown in simple and handy maps showing the spatial distribution of land capability. The extracted land capability value was compared with the soil quality index for each mapping unit in the study area using SPSS software to obtain a correlation coefﬁcient between them at a 0.05 signiﬁcance level (p-value).

NIR − R NDVI = (2) NIR + R

Sustainability 2021, 13, x FOR PEER REVIEWwhere NIR is the near-infrared reﬂectance and R is the red reﬂectance. 7 of 17

3. Results 3.1.3. Results Physiographic of the Investigated Area 3.1. PhysiographicThe physiographic of the Investigated units ofArea the studied area are represented in Figure4 and Table S3. TheThe alluvial physiographic plain included units of the recent studied terraces area are represented and basins in covering Figure 4 and an Table area S3. of 843.43 and 218.54The alluvial km2, plain respectively. included On recent the terraces other hand, and basins recent covering terraces an were area classifiedof 843.43 and into moderately high218.54 and km2 basins, respectively. were classifiedOn the other into hand, overflow recent andterraces decantation, were classified meanwhile into moder- lacustrine plain includedately high and terraces basins (relatively were classified low, into relatively overflow high, and decantation and moderately, meanwhile high) lacustrine covering an area of 194.66plain included km2. The terraces fluvial-lacustrine (relatively low, plainrelatively included high, and old moderately terraces (relativelyhigh) covering low an and relatively 2 area of 194.66 km . The fluvial-lacustrine 2plain included old terraces (relatively low and high),relatively covering high), covering an area an of area 264.94 of 264.94 km km. 2.

Figure 4. Spatial distribution of the physiographic units. Figure 4. Spatial distribution of the physiographic units. 3.2.3.2. Spatial Spatial Distribution Distribution of Soil of SoilIndicators Indicators 3.2.1.3.2.1. Chemical Chemical Soil Soil Quality Quality Indicators Indicators (CSQI) (CSQI) The chemical soil quality indicators (CSQI) are dynamic indicators and change by the The chemical soil quality indicators (CSQI) are dynamic indicators and change by the land management process. CSQI were selected based on the sensitivity to disturbance and landsoil ecosystem management function process. performance. CSQI were CSQI selected included based (EC, on pH, the CEC, sensitivity ESP, SAR to, and disturbance and soilCaCO ecosystem3), as represented function in Figure performance. 5. The average CSQI pH included ranges from (EC, 7.62 pH, to CEC, 8.57. ESP,The s SAR,patial and CaCO3), distribution of ECe shows that the study area has a wide range of ECe values, as it ranges from 1.33 to 36.22 dS/m. The results of Cation Exchange Capacity (CEC) indicate that the lowest value of CEC 18 cmole/kg, while the highest one 53.69 cmole/kg. Exchangeable sodium percentage (ESP) and Sodium Adsorption Ratio (SAR) values range from 9.93% to 26.96% and from 7.74% to 24.28%, respectively. The CaCO3 content ranges from 3.39% to 29.73%.

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as represented in Figure5. The average pH ranges from 7.62 to 8.57. The spatial distribution of ECe shows that the study area has a wide range of ECe values, as it ranges from 1.33 to 36.22 dS/m. The results of Cation Exchange Capacity (CEC) indicate that the lowest value of CEC 18 cmole/kg, while the highest one 53.69 cmole/kg. Exchangeable sodium

Sustainability 2021, 13, x FOR PEERpercentage REVIEW (ESP) and Sodium Adsorption Ratio (SAR) values range from 9.93%8 of 16 to 26.96%

and from 7.74% to 24.28%, respectively. The CaCO3 content ranges from 3.39% to 29.73%.

(a) (b)

(e) (f)

FigureFigure 5. 5.Spatial Spatial distributiondistribution of ofchemical chemical soil properties soil properties (a) electric (a) conductivity electric conductivity (EC: dS/m), (b (EC:) soil dS/m),reaction (pH), (b) soil (c) cation reaction (pH), exchange capacity (CEC: cmole/kg), (d) exchangeable sodium percent (ESP), (e) sodium absorption ratio (SAR), and (f) calcium (c) cationcarbonate exchange percentage capacity (CaCO (CEC:3: %). cmole/kg), (d) exchangeable sodium percent (ESP), (e) sodium absorption ratio (SAR), and (f) calcium carbonate percentage (CaCO3: %). Sustainability 2021, 13, 2893 8 of 15

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3.2.2. Physical and Biological Soil Quality Indicators 3.2.2. Physical and Biological Soil Quality Indicators There are ﬁve soil texture classes in the study area (i.e., sandy clay loam, clay, sandy There are five soil texture classes in the study area (i.e., sandy clay loam, clay, sandy loam, clay loam, and loam). From Figure6, the spatial variation range of sand, clay, and silt loam, clay loam, and loam). From Figure 6, the spatial variation range of sand, clay, and percentagessilt percentages are 19.7%–70.3%, are 19.7%–70.3%, 12.2%–40.1%, 12.2%–40.1%, and 14.5%–58.5%,and 14.5%–58.5%, respectively. respectively. The effectiveThe soileffective depth ranges soil depth from ranges 50 to 120from cm, 50 to while 120 cm, organic while matter organic (OM) matter content (OM) content ranges fromranges 0.23% to 1.98%.from 0.23% to 1.98%.

(a) (b)

(e) (f)

Figure 6. Spatial distribution of some physical and biological soil properties (a) sand (%), (b) silt (%), (c) clay (%), (d) depth (cm), (e) drainage condition, and (f) soil organic matter (OM%). Sustainability 2021, 13, 2893 9 of 15

3.3. Digital Soil Map of the Study Area Digital soils map of the study area are classified according to morphological, phys- SustainabilitySustainability 2021 2021, 13, 13, x, xFOR FOR PEER PEER REVIEW REVIEW 1111 of of 17 17 ical, and chemical properties to five sub great groups namely Typic haplargids, Typic haplocalcids, Typic haplosalids, Typic torrifluvents, and Vertic torrifluvents with an area of 109.32 km2, 185.59 km2, 360.24 km2, 43 km2, 767.51 km2, and 136.20 km2, respectively (Figure(Figure7 7).). 7). InIn In addition,addition, addition, soilsoil soil classificationclassification classification basedbased based onon on thethe the WorldWorld World ReferenceReference Reference BaseBase Base (WRB)(WRB) (WRB) systemsystem system includedincluded Endo Endo calcaric calcaric luvisols, luvisols, Luvic Luvic calcisols, calcisols, Hypersalic Hypersalic haplic haplic solonchaks, solonchaks, Fluvisols, Fluvisols, andand Vertisols,Vertisols Vertisols, it,it it isis is providedprovided provided inin in FigureFigure Figure S3.S3. S3.

FigureFigure 7.7 7. .Digital Digital s soil oilsoil map map of of sub sub great great groups.groups groups. .

3.4.3.4. Soil Soil Capability Capability Index Index (CI) (CI) TheThe obtained obtained results results of of land land capability capability from from ALESarid ALESarid are are illustrated illustrated in in Figure Figure8 8aa 8a and and and TableTable S4.S4. S4. These These results results indicate indicate that that the the estimated estimated capability capability index index for for the the study study area area was was 2 nonnon-agriculturenon-agriculture-agriculture class class (C6), (C6), poor poor (C4), (C4), fairf airfair (C3),( C3)(C3), , and and goodgood good (C2)(C2) (C2) withwith with anan an areaarea area ofof of 231.87231.87 231.87 kmkm km2 2 2 2 2 (13.50%),(13.50%), 291.94 291.94 km km2 2(17%), (17%), 767.39 767.39 km km2 2(44.94%)(44.94%), (44.94%), ,and andand 416.07 416.07416.07 km kmkm2 2(24.4%) (24.4%),(24.4%), ,respectively. respectively.respectively.

(a(a) ) (b(b) )

FigureFigureFigure 8.8. 8. SpatialSpatial Spatial distributiondistribution distribution ofof of CICI CI ((a a()a) and)and and SQISQI SQI ((b b().b) )

3.5.3.5. Soil Soil Quality Quality Index Index (SQI) (SQI) TheThe results results of of the the proposed proposed model model reveal reveal that that the the study study area area located located under under six six classes classes, , e.g.e.g., , veryvery high high-quality-quality class class occupies occupies 387.12 387.12 kmkm2 2 (22.7%),(22.7%), high high-quality-quality class class occupies occupies 441.72441.72 kmkm2 2 (25.87%),(25.87%), the the moderate moderate quality quality class class represent represents s 208.57208.57 kmkm2 2 (12.21%),(12.21%), the the slightlyslightly moderate moderate-quality-quality class class represent represents s231.10 231.10 km km2 2(13 (13.5%),.5%), in in addition, addition, a a low low-quality-quality Sustainability 2021, 13, x FOR PEER REVIEW 11 of 16

Sustainability 2021, 13, 2893 10 of 15 (a) (b) d

3.5. Soil3.5. Quality Soil Quality Index (SQI) Index (SQI) The resultsThe results of the of proposed the proposed model model reveal reveal that the that study the study area located area located under under six classes, six classes, Sustainability 2021, 13, x FOR PEER REVIEW 2 12 of 17 e.g., verye.g., high-qualityvery high-quality class occupiesclass occupies 387.12 387.12 km (22.7%),km2 (22.7%), high-quality high-quality class occupiesclass occupies 2 2 441.72 km (25.87%),2 the moderate quality class represents 208.57 km (12.21%), the2 slightly 441.72 km (25.87%), the moderate qualit2 y class represents 208.57 km (12.21%), the moderate-qualityslightly moderate-quality class represents class 231.10 represents km (13.5%), 231.10 km in2addition, (13.5%), in a low-qualityaddition, a low-quality class 2 2 classcovers cover an areas an ofarea about of about 233 km 233(13.60%), km (13.60%) and2 finally, and finally the very the low very class low occupies class occupies about class2 covers an area of about 233 km (13.60%), and finally the very low class occupies 206 km (12%)2 (Figure8b and Table S5). about about206 km 206 (12%) km2 (12%)(Figure (Figure 8b and 8b Table and TableS5). S5). 3.6. The Relationship between SQI, CI, and NDVI 3.6. The3.6. Relationship The Relationship between betweenSQI, CI, andSQI, NDVI CI, and NDVI The developed spatially explicit model of soil quality was validated by the land The developedThe developed spatially spatially explicit explicit model modelof soil qualityof soil wasquality validated was validated by the land by ca-the land pabilitycapability index index (CI) (CI) from from ALESarid ALESarid and and NDVI NDVI from from remote remote sensing sensing data data over over the study area. Thecapability statistical index results (CI) showfrom ALESarid that there and is a strongNDVI significantfrom remote association sensing data between over SQIthe study area. Thearea. statistical The2 statistical result sresults show showthat there that isthere a strong is a strong significant significant association association between between SQI SQI and CI as R2 = 0.86 at p < 0.001 (Figure9a). There is also a significant relationship between and CIand as RCI =as 0.86 R2 =at2 0.86 p < at0.001 p < (Figure0.001 (Figure 9a). There 9a). Thereis also is a alsosignificant a significant relationship relationship between between NDVI and SQI as R2 = 0.18 at p < 0.05 (Figures9b and 10). NDVINDVI and SQI and as SQI R =as 0.18 R2 =at 0.18 p < at0.05 p <(Figure 0.05 (Figuress 9b and 9b 10). and 10).

(a) (b) (a) (b) Figure 9. Correlation between SQI and CI (a) and between NDVI and SQI (b). FigureFigure 9. Correlation 9. Correlation between between SQI and SQI CI and (a) andCI (a between) and between NDVI andNDVI SQI and (b ).SQI (b).

(a) (b) (a) (b)

Figure 10. Cont. Sustainability 2021, 13, 2893 11 of 15 Sustainability 2021, 13, x FOR PEER REVIEW 12 of 16

7.95

FigureFigure 10. NDVI 10. NDVI maps mapsfrom 2017 from to 2017 2020. to 2020.

4. Discussion4. Discussion To identifyTo identify areas withareas better with soilbetter quality soil quality and those and needing those needing more attention more attention because because of their vulnerabilityof their vulnerability to degradation, to degradation, the assessment the and assessment application and of theapplication method mentionedof the method in thismentioned study were in used. this study Those were areas used. with soilThose that areas provide with optimum soil that plant provide growth optimum condi- plant tions andgrowth lower conditions sensitivity and to lower erosive sensitivity processes to have erosive been processes rated as having have been higher rated quality. as having higher quality. 4.1. Physiographic and Soil Chemical, Physical and Biological Properties Physiographic4.1. Physiographic mapping and unitsSoil Chemical, could be delineated Physical and by interpreting Biological Properties satellite images that are consideredPhysiographic to be one of mapping the most commonunits could and be economically delineated advancedby interpreting mechanisms satellite [33 images]. Physiographicthat are unitsconsidered have been to identifiedbe one of and the delineated most common by landscape and interpretationeconomically fromadvanced the integrationmechanisms between [33]. Physiographic satellite image units and have DEM been with identified the assistance and delineated of various by maps landscape and fieldinterpretation studies. The from results the integration show that between the main satellite landscapes image in and the DEM area studiedwith the were assistance alluvial,of various lacustrine, maps and and river-lacustrine field studies. plains. The results The highest show that EC valuethe main is found landscapes in relatively in the area low lacustrinestudied were terraces, alluvial, which lacustrine, are situated and inriver- the Qarounlacustrine Lake plains. area. The Arid highest climate EC and value is intrusionfound of lakein relatively water are low the mainlacustrine reasons terraces, for rising which salinity are insituated low lacustrine in the Qaroun terraces. Lake [34] area. statedArid that climate in arid andand semi-aridintrusion conditionsof lake water where are precipitationthe main reasons is low for and rising sporadic salinity and in low evaporationlacustrine is high, terraces. most [34] salinized stated that soils in are arid present. and semi-arid A suggested conditions management where precipitation plan is, is therefore,low requiredand sporadic for high-salt and evaporation soil by leaching is high, salts mo fromst salinized soil using soils high-quality are present. irrigation A suggested watermanagement [35]. However, plan more is, therefore, than 30% required of the studiedfor high-salt area soil is non-saline by leaching (EC salts < from 4 dS/m), soil using confirminghigh-quality the importance irrigation ofwater SQI in[35]. identifying However, agricultural more than areas.30% of Thethe resultsstudied of area CEC is non- have asaline wide (EC range, < 4 baseddS/m), on confirming the percentage the importance of clay and of organic SQI in matter identifying in the agricultural soil, as there areas. are significantThe results positive of CEC correlations have a wide between range, based CEC, clay,on the and percentage organic matter of clay [ 36and]. Theorganic high matter sodiumin the percentage soil, as there can negatively are significant affect positive soil properties, correlations such between as soil structureCEC, clay, and and soil organic hydrology,matter reducing [36]. The crop high productivity. sodium percentage Soil more can than nega 15tively ESP affect has become soil prop alkalierties, soil such and as soil can bestructure reclaimed and by soil adding hydrology, gypsum reducing to the soil crop [37]. productivity. Areas close to Soil Qaroun more Lake than had 15 theESP has highest value of CaCO that could be due to shell fragments. The highest CaCO value become alkali soil3 and can be reclaimed by adding gypsum to the soil [37]. Areas3 close to in some soils can lead to very hard layers impermeable to water and crop roots and the Qaroun Lake had the highest value of CaCO3 that could be due to shell fragments. The fixation of P fertilizer when added to calcareous soils. [38] found a strong P retention in highest CaCO3 value in some soils can lead to very hard layers impermeable to water and calcareous soils, although the application of OM contributed to its mobility. The soil of the crop roots and the fixation of P fertilizer when added to calcareous soils. [38] found a study area has a wide range of texture, physical, chemical, hydrological, and biological strong P retention in calcareous soils, although the application of OM contributed to its properties. Consequently, soil fertility may be affected by such changes in soil properties mobility. The soil of the study area has a wide range of texture, physical, chemical, that affect the availability and storage of nutrients. The physical soil quality indicator hydrological, and biological properties. Consequently, soil fertility may be affected by (PSQI) is mainly affected by soil texture as a variation affecting the physical properties such changes in soil properties that affect the availability and storage of nutrients. The of soils, such as water retention and stability of soil aggregates. The drainage conditions physical soil quality indicator (PSQI) is mainly affected by soil texture as a variation Sustainability 2021, 13, 2893 12 of 15

of the study area range from poor to good drainage, which may have an impact on soil properties. The proposed management plan for improving poor drainage in the area under study can be carried out using two methods of tile drainage and surface ditching. The main objective of the tile drainage and ditch systems is to lower the water table in the soil below the root zone of the plant [39]. The soil organic matter (OM) content is a very important indicator for the biological soil quality indicator (BSQI). Furthermore, OM could be considered as a sole indicator of soil degradation [40], conﬁrming its importance in soil quality assessment. OM percentage is important for maintaining a perfect soil structure, increasing the availability of nutrients that increase soil fertility and maintain the balance of the agro-ecosystem. The OM content is relatively low in the study area as the arid and semi-arid climatic conditions have a negative effect on the OM content due to the high- temperature increase in the rate of decomposition of organic material in the soil [41,42].

4.2. CI Status of the Study Area Using soil properties, the land capability index for the area under investigation has been estimated using ALESarid. The results showed that most of the study area located under fair class representing 44.9% of the total area, which means that these soils are suitable for cultivation over a long period, but they have some hazards and limitations (i.e., high salinity, high ESP, and high CaCO3) of some units, which cause an increase in soil reaction in addition to the sandy texture of some mapping units. All these hazards reduce plant choice or require moderate, easy-to-use conservation practices. Similar studies have determined SQI using soil properties in semi-arid ecosystems but used different approaches with GIS and RS such as Multi-Criteria Decision Analysis (MCDA) [43,44], PCA, geostatistic, AHP-Fuzzy [45], and integration of type-2 fuzzy sets with AHP [46].

4.3. SQI Using Geospatial Techniques The spatial trend of soil quality increases from north to east and this trend is consistent with current conditions, as low soil quality in the north may be due to a high percentage of calcium carbonate, a high percentage of sodium exchangeable, high salinity, and low concentration of organic matter and intensive mismanagement practices in agriculture [47] have shown that organic matter, clay, EC, and CEC are the most inﬂuential factors in the determination of SQI, conﬁrming the importance of using these parameters in the current study. Developing soil quality classes can minimize agricultural management costs, such as improving soil quality for a slightly moderate class, which may need improvement compared to low-quality soils, and two types of soils need improvement.

4.4. Validation of Soil Quality Index Model There is a significantly high correlation between the SQI and CI correlations (R2 = 0.86, p < 0.001), which means that this study produces an accurate soil quality assessment model for the study area. SQI and CI are two methods for assessing the potential of land for a specific type of use. It is a newly developed method, while land evaluation has been in use since 1961 [48]. They found a highly significant correlation between the SQI rating and CI classes. NDVI is helping in mapping and predicting the extent of land degradation [49] and also allowing farmers, traders, and insurers to make well-informed agricultural decisions on time for achieving precision agriculture. There is a positive correlation (R2 = 0.18, p < 0.05) between NDVI and SQI, therefore, it is suggested that the sample sizes should be increased to ensure the correlation between them in future studies.

5. Conclusions GIS is a useful tool for storing, retrieving, and manipulating a large amount of data needed to calculate and map different soil parameters. The production map of spatial distributions for soil properties is the most important step in the assessment of SQI. The results show that the study area located under six soil quality classes (e.g., very high-quality class: 387.12 km2 (22.7%), high-quality class: 441.72 km2 (25.87%), moderate quality class: Sustainability 2021, 13, 2893 13 of 15

208.57 km2 (12.21%), slightly moderate-quality class: 231.10 km2 (13.5%), low-quality class: 233 km2 (13.60%), and very low class: 206 km2 (12%)). The results of the SQI spatial model developed were accepted with the current situation in the study area and were highly correlated with the results of the land capability. It is very important to assess soil quality periodically to identify agricultural practices that cause soil growth and crop productivity. In conclusion, the spatial model proposed in this study could be a more accurate methodology for assessing the spatial distribution of soil quality by including soil physical, chemical, and biological indicators.

Supplementary Materials: The following are available online at https://www.mdpi.com/2071-1 050/13/5/2893/s1, Figure S1: Location for the study area, Figure S2: Map of sampling points to each landform (a), geological map (b), and land use map (c), Figure S3: Digital soil map to show the distribution of sub great groups of the study area based on WRB system, Table S1: Classes and factors assigned weighting index affecting soil quality index in the study area according to MEDALUS methodology, Table S2: Soil quality index of the study area, Table S3: Physiographic units of the study area, Table S4: Areas of land capability, Table S5: Areas of soil quality. Author Contributions: Conceptualization, M.S.S. and A.M.S.K.; methodology, M.A.A., A.A.E.B., and W.A.; software, A.E.; validation, M.A.; formal analysis, A.A.A., A.A.B. and Z.S.; investigation, A.A.B.; data curation, E.F.A.; writing—original draft, E.F.A.; writing—review and editing, M.A., M.E.M.J., A.M.S.K., and A.E.; visualization, M.A.A.; supervision, A.A.E.B.; project administration, A.A.B.; funding acquisition, E.F.A. and A.A.A. All authors have read and agreed to the published version of the manuscript. Funding: The authors are thankful to Taif University Researchers Supporting Project number (TURSP- 2020/65), Taif University, Saudi Arabia for the financial support and research facilities. Data Availability Statement: The data presented in this study are available in the main manuscript or Supplementary File. Acknowledgments: Authors are grateful to Peng Liu, CAS, China for his review and editing of the first draft version of the manuscript. The authors would like to extend their high appreciation to the Deanship of Scientific Research at King Saud University for its funding of this research through the Research Group Project no. RGP- 275. Conflicts of Interest: The authors declare no conflict of interest.

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