Novel spatial models for appraising arable land resources using data processing techniques
can increase insight into agroecosystem services. Hence, the principal component analysis (PCA),
hierarchal cluster analysis (HCA), analytical hierarchy process (AHP), fuzzy logic, and geographic
information system (GIS) were integrated to zone and map agricultural land quality in an arid
desert area (Matrouh Governorate, Egypt). Satellite imageries, field surveys, and soil analyses were
employed to define eighteen indicators for terrain, soil, and vegetation qualities, which were then
reduced through PCA to a minimum data set (MDS). The original and MDS were weighted by AHP
through experts’ opinions. Within GIS, the raster layers were generated, standardized using fuzzy
membership functions (linear and non-linear), and assembled using arithmetic mean and weighted
sum algorithms to produce eight land quality index maps. The soil properties (pH, salinity, organic
matter, and sand), slope, surface roughness, and vegetation could adequately express the land quality.
Accordingly, the HCA could classify the area into eight spatial zones with significant heterogeneity.
Selecting salt-tolerant crops, applying leaching fraction, adopting sulfur and organic applications,
performing land leveling, and using micro-irrigation are the most recommended practices. Highly
significant (p < 0.01) positive correlations occurred among all the developed indices. Nevertheless, the
coefficient of variation (CV) and sensitivity index (SI) confirmed the better performance of the index
developed from the non-linearly scored MDS and weighted sum model. It could achieve the highest
discrimination in land qualities (CV > 35%) and was the most sensitive (SI = 3.88) to potential changes.
The MDS within this index could sufficiently represent TDS (R2 = 0.88 and Kappa statistics = 0.62),
reducing time, effort, and cost for estimating the land performance. The proposed approach would
provide guidelines for sustainable land-use planning in the studied area and similar regions