A GIS-Based Approach for the Quantitative Assessment of Soil Quality and Sustainable Agriculture

Abstract

Assessing soil quality is considered one the most important indicators to ensure planned and sustainable use of agricultural lands according to their potential. The current study was carried out to develop a spatial model for the assessment of soil quality, based on four main quality indices, Fertility Index (FI), Physical Index (PI), Chemical Index (CI), and Geomorphologic Index (GI), as well as the Geographic Information System (GIS) and remote sensing data (RS). In addition to the GI, the Normalized Difference Vegetation Index (NDVI) parameter were added to assess soil quality in the study area (western part of Matrouh Governorate, Egypt) as accurately as possible. The study area suffers from a lack of awareness of agriculture practices, and it depends on seasonal rain for cultivation. Thus, it is very important to assess soil quality to deliver valuable data to decision makers and regional governments to find the best ways to improve soil quality and overcome the food security problem. We integrated a Digital Elevation Model (DEM) with Sentinel-2 satellite images to extract landform units of the study area. Forty-eight soil profiles were created to represent identified geomorphic units of the investigated area. We used the model builder function and a geostatistical approach based on ordinary kriging interpolation to map the soil quality index of the study area and categorize it into different classes. The soil quality (SQ) of the study area, classified into four classes (i.e., high quality (SQ2), moderate quality (SQ3), low quality (SQ4), and very low quality (SQ5)), occupied 0.90%, 21.87%, 22.22%, and 49.23% of the total study area, respectively. In addition, 5.74% of the study area was classified as uncultivated area as a reference. The developed soil quality model (DSQM) shows substantial agreement (0.67) with the weighted additive model, according to kappa coefficient statics, and significantly correlated with land capability R2 (0.71). Hence, the model provides a full overview of SQ in the study area and can easily be implemented in similar environments to identify soil quality challenges and fight the negative factors that influence SQ, in addition to achieving environmental sustainability.

Insights into Efficient Irrigation of Urban Landscapes: Analysis Using Remote Sensing, Parcel Data, Water Use, and Tiered Rates

“…We used the Excel app from Microsoft 365 along with SAS JMP Pro 16.1.0 for graphing and statistical analysis. Because of the large spatial scales involved, analysis of extensive landscapes often relies on remote sensing and GIS [45][46][47]. Using 4-band imagery, plant health can be assessed using the Normalized Difference Vegetation Index (NDVI) [48].…”

“…Because healthy vegetation with more chlorophyll reflects higher levels of light in the NIR and absorbs light in the red band, the NDVI distinguishes between areas of thick, healthy plant life vs. unhealthy and/or sparse plant life and has been used to both monitor plant health and drought conditions [49][50][51]. Researchers have shown that NDVI is directly related to the photosyn- Because of the large spatial scales involved, analysis of extensive landscapes often relies on remote sensing and GIS [45][46][47]. Using 4-band imagery, plant health can be assessed using the Normalized Difference Vegetation Index (NDVI) [48].…”

Integration of Geostatistical and Sentinal-2AMultispectral Satellite Image Analysis for Predicting Soil Fertility Condition in Drylands

“…Miocene and Quaternary deposits are the main geological units [44,45]. The main geomorphologic units in the study area are plains (3453.68 ha), wadi (733 ha), terraces (7149.65 ha), basins (399.27 ha), pavement plain, and reference unit (11064.76 ha) units, according to [46]. Figure 4 shows the three classes of land use and land cover (LULC) highlighted in the investigated area: agriculture, urban, and bare soils.…”

“…The sample points number was set at 400, and the iteration number was set at 50,000 as seen in Figures 1 and 5.The selection of the samples (400 points) was based on hydrological and morphometric parameters, i.e., digital elevation model (DEM), slope, curvature, profile curvature, plan curvature, stream power index, slope length, and ISO data classification of the S2A images (Supplementary Materials: Figures S1 and S2) [49], and urban areas were excluded. All soil sample locations were checked in the field; forty-eight locations were chosen to represent different landforms of the study area according to [46] (Figure 6). The soil samples were collected from the study area and transferred to the relevant soil, water, and plant laboratories within the Faculty of Agriculture, Tanta University in accordance with ISO/IEC 17,025:2017 requirements for analysis.…”

“…curvature, stream power index, slope length, and ISO data classification of the S2A images (Supplementary Materials: Figures S1 and S2) [49], and urban areas were excluded. All soil sample locations were checked in the field; forty-eight locations were chosen to represent different landforms of the study area according to [46] (Figure 6). The soil samples were collected from the study area and transferred to the relevant soil, water, and plant laboratories within the Faculty of Agriculture, Tanta University in accordance with ISO/IEC 17,025:2017 requirements for analysis.…”