sustainability 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.; [email protected] 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 geo- statistics approaches based on measurements at adjacent locations with certain weights assigned to each measurement [19,20]. For instance, classical ordinary Kriging interpo- lation (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 biologi- cal 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 differ- ence 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 Sustainability 2021, 13, 2893 3 of 15 several factors, such as soil quality [26,27]. Thus, land capability and NDVI may have bet- ter 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.