In El-Tina plain soil salinity is the major soil limitation factor for agricultural production. Spatial information on soil salinity is increasingly needed, particularly for better soil management in this area. To explore spatial variability of soil salinity in the study area grid sampling scheme (800 × 800 m) consisting of 96 sample points (81 soil profiles and 15 augers) and 41 observation points was established. Electrical conductivity was determined by laboratory analysis from 1:2.5 soil-water suspensions (EC2.5). Spatial trend and semivariogram were computed and spatial distribution of field salinity status was further visualized and quantified. Two types of kriging were used: Ordinary Kriging (OK) and Uni-versal Kriging (UK) with three semivariogram models (circular, spherical and ex¬ponential). Mean Prediction Errors (MPE), Mean Standardized Prediction Errors (MSPE) and Root-Mean-Square Standardized Prediction Errors (RMSSPE) were used to evaluate the models. The results suggest that the best model to generate soil salinity map was Ordinary Kriging with spherical semivariogram model (MPE and MSPE close to 0, and RSSPE close to 1). The selected model was used to generate salinity map based on standard soil salinity classification.