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Soil salinity is one of the great problems in and and semi-arid environments. The estimation and prediction of spatial soil salinity may be considered as a stochastic process, observed at irregular locations in space. Environmental variables usually show spatial dependence among observations which is an important drawback to traditional statistical methods. Geostatistical techniques that analyse and describe the spatial dependence and quantify the scale and intensity of the spatial variation, provides spatial information for local estimation of soil salinity. In this paper we propose a Gaussian Spatial Linear Mixed Model (GSLMM), which involves a non-parametric term, accounting for a deterministic trend given by exogenous variables, and a parametric component defining the purely spatial random variation, possibly due to latent spatial processes. We focus here on the analysis of the relationship between soil electrical conductivity as a parameter related directly to soil salinity as well as sodium content (sodicity) to identify spatial variations in these parameters. This kind of methodology is demonstrated as a useful tool for environmental land management. Copyright (C) 2007 John Wiley & Sons, Ltd.