Modelling land degradation vulnerability (LDV) in the newly-reclaimed desert oases is
a key factor for sustainable agricultural production. In the present work, a trial for using remote
sensing data, GIS tools, and Analytic Hierarchy Process (AHP) was conducted for modeling and
evaluating LDV. The model was then applied within 144,566 ha in Farafra, an inland hyper-arid
Western Desert Oases in Egypt. Data collected from climate conditions, geological maps, remote
sensing imageries, field observations, and laboratory analyses were conducted and subjected to AHP
to develop six indices. They included geology index (GI), topographic quality index (TQI), physical
soil quality index (PSQI), chemical soil quality index (CSQI), wind erosion quality index (WEQI), and
vegetation quality index (VQI).Weights derived from the AHP showed that the effective drivers of
LDV in the studied area were as follows: CSQI (0.30) > PSQI (0.29) > VQI (0.17) > TQI (0.12) > GI
(0.07) > WEQI (0.05). The LDV map indicated that nearly 85% of the total area was prone to moderate
degradation risks, 11% was prone to high risks, while less than 1% was prone to low risks. The
consistency ratio (CR) for all studied parameters and indices were less than 0.1, demonstrating the
high accuracy of the AHP. The results of the cross-validation demonstrated that the performance
of ordinary kriging models (spherical, exponential, and Gaussian) was suitable and reliable for
predicting and mapping soil properties. Integrated use of remote sensing data, GIS, and AHP would
provide an effective methodology for predicting LDV in desert oases, by which proper management
strategies could be adopted to achieve sustainable food security.