The assessment of soil salinization risk at the field scale requires modeling of the spatial variability of soil salinity. This paper presents a probabilistic approach to estimate and map a risk index using all available auxiliary information. A probabilistic methodology is proposed to estimate the conditional probability of exceeding the assigned threshold value of a generic indicator of soil salinity. A geostatistical non-parametric technique, probability kriging, was used to assess the risk of soil salinization and delineate different hazard zones within a field. The technique relies on indicator coding of information. The approach was applied to soil electrical conductivity measurements collected in an experimental field located in the Nile Delta region in Egypt, and submitted over time to trials with different fertilization treatments. The application of the method allowed delineation of a north-eastern zone in the field with a high risk of soil salinization due to its lack of cultivation for a long time and nearness to buildings that prevent water infiltration. The method proved to be quite promising from the perspective of precision agriculture and it is easily extendable to any sort of remote and proximal sensing auxiliary information, including information on the deepest layers of soil.