article · Sustainability
A spatial modelling approach was developed to evaluate agricultural soil quality in the western part of Matrouh Governorate, Egypt, an area reliant on seasonal rainfall. The method integrates geographic information systems and remote sensing, including Sentinel-2 satellite images and digital elevation models, with physical data collected from forty-eight soil profiles. The assessment calculates fertility, physical, chemical, and geomorphological indices alongside vegetation index data. Geostatistical interpolation mapped the region into four distinct quality grades: high-quality soil accounted for under one percent, moderate quality covered roughly twenty-two percent, low quality occupied twenty-two percent, and very low quality made up over forty-nine percent of the land. The resulting model showed strong statistical agreement with traditional additive models and land capability metrics, providing a scalable tool to guide sustainable agricultural planning in similar dryland environments.
In regions that depend on seasonal rain, understanding land quality is vital for safeguarding food security. By categorising soil potential through combined satellite data and ground sampling, regional authorities can identify severely degraded areas, guide local farming practices, and implement land management strategies that protect natural resources.
The work presents an applied, field-tested mapping methodology rather than a commercial product. It could be adopted by regional agricultural authorities, land-use planners, and environmental consultancies seeking spatial tools to evaluate land capability. While shown to be functional in a specific dryland region, deploying it as an off-the-shelf software service or commercial planning tool would require further development.
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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.
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DOI: 10.3390/su132313438
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