Geostatistical methods to identify and map spatial variations of soil salinity

The problem of estimating and predicting spatial distribution of a spatial stochastic process, observed at irregular locations in space, is considered in this paper. Environmental variables usually show spatial dependencies among observations, with lead one to use geostatistical methods to model the spatial distributions of those observations. This is particularly important in the study of soil properties and their spatial variability. In this study geostatistical techniques were used to describe the spatial dependence and to quantify the scale and intensity of spatial variations of soil properties, which provide the essential spatial information for local estimation. In this contribution, we propose a spatial Gaussian linear mixed model that involves (a) a non-parametric term for accounting deterministic trend due to exogenous variables and (b) a parametric component for defining the purely spatial random variation due possibly to latent spatial processes. We focus here on the analysis of the relationship between soil electrical conductivity and Na content to identify spatial variations of soil salinity. This analysis can be useful for agricultural and environmental land management.

Research Highlights
► The methodology applied seems to be adequate to predict the soil characteristics in this region.
► Relationships between soil salinity parameters in a medium scale map has been displayed.
► The method used reveal saline areas linked with discharges of saline waters or saline aquifers.
► This study has elucidated the spatial correlations and variations in salinity soil measures.
► Results can be applied in making decisions, land management and planning.