Land degradation and desertification are major environmental challenges threatening ecosystems, food production, and socio-economic stability. This study investigates the sensitivity to desertification in the Balat region, El-Dakhla Oasis, Egypt, to support informed land management. Desertification processes were characterized and mapped using an integrated approach combining remote sensing (RS) and field-based methods. Thirty soil profiles were sampled and analyzed for physicochemical properties to compute five quality indices soil (SQI), climate (CQI), management (MQI), vegetation (VQI), and erosion (EQI) that together defined the Desertification Sensitivity Index (DSI). Landsat 8 imagery was processed to classify land use/land cover (LULC) types, and the Normalized Difference Vegetation Index (NDVI) and surface albedo were derived to assess susceptibility classes. The ground-based assessment delineated four susceptibility classes (low, moderate, high, and very high), while the RS-based NDVI-albedo model identified five classes: non-susceptible (23.55 %), low (31.18 %), moderate (17.96 %), high (14.84 %), and very high (12.47 %). As expected from literature, NDVI and albedo exhibited an inverse relationship, confirming that higher albedo values correspond to reduced vegetation cover and increased desertification sensitivity. Validation using random forest (RF) and support vector regression (SVR) showed strong consistency between RS and field data, with SVR achieving superior performance (R2 = 0.864, RMSE = 0.018, RPD = 2.08). GIS-based visualization highlighted distinct spatial variability across the study area. Principal component analysis (PCA) identified three soil groups: Group 1 most sensitive, with high EQI, VQI, and DSI values; Group 2 associated with poor fertility and high salinity (sand, gypsum, CaCO3, EC); and Group 3 characterized by finer textures (clay, silt) that enhance moisture retention and lower sensitivity. Integrating RS and field datasets provides a robust framework for assessing desertification sensitivity in arid regions, supporting sustainable land management and conservation. Future research should focus on real-time monitoring and adaptive management strategies to mitigate ongoing land degradation.