This study attempts to define the various geochemical processes responsible for the phreatic groundwater chemical evolution, using statistical methods and hydrochemical approaches. The phreatic aquifer in the plain of Sidi Bouzid is located in central Tunisia, the latter is characterized by an semi-arid to arid climate and irregular rainfall. The chemical water classification shows the dominance of a chlorinated calcium and magnesium sulfate type facies. The mineral-water interaction is at the expense of sulphates and to a lesser extent with carbonates. A statistical analysis of the physico-chemical data has been performed by the principal component analysis, a total of three components has been extracted, as it represents 87.040% of the total variance of the all data. The projection of the variables on the factorial designs shows two groups of individuals: the first grouping is where the highest concentrations of total dissolved solids, potassium, chlorides and sodium were observed, the second grouping consists of individuals where the water is less mineralized located upstream of the basin. The evaluation of the suitability of water for irrigation is provided by the calculation of various parameters. According to the results obtained 48.75% of the area of the plain is excellent, 26.71% is good, 13.60% is permissible and 10,94% unsuitable for irrigation. The areas of these zones are respectively 312; 171; 87 and 70 km2

Conventional analyses of soil characteristic are expensive and time-consuming. Hyperspectral remote sensing has become useful tool for quantitative analysis of soil properties particularly in area where soil surface is permanently or temporarily exposed, as in Mediterranean region. Some multivariate statistical methods have been successful in soil spectrometry but they seem to have some limitations. The aim of this work is to identify properties of soils by using an unmixing method, the Support Vectors Machines (SVMs), from Hyperion hyperspectral remote sensing data. The approach consists in i) selection of Hyperion spectra of "extreme" soils among a Hyperion spectra for which soil properties are knows, ii) the application of the SVM to the Hyperion hyperspectral image to classify the pixels. The overall accuracies obtained for the soil characteristic classification are 87,95% (for clay), 73,81% (calcium carbonate, CaCO3) and the Kappa indexes are 0,82 (clay) and 0,60 (CaCO3). Finally, this work has showed that the SVM provides an important and promising perspective in soil science.