Arid and semi-arid regions are characterized by low rainfall high evaporation, and restricted leaching. They are also characterized by degraded soils due to high salinity which decline soil quality and a lack of their fertility (Sidike et al., 2014; Makinde & Oyelade, 2019).
So the quality of soils of these regions mainly depends on the presence of the amount and types of salts such as anhydrite (CaSO4), calcite (CaCO3), gypsum(CaSO4.2H2O), Halite (NaCl), and dolomite (CaMg(CO3)2).
Land degradation caused by soil salinity has been a global issue in dry regions (Qadir et al., 2006) either in Tunisia. However, more than 8% of the Tunisia surface is already affected by salinization to different degrees (Antipolis, 2003).
So it seems important to characterize and monitor the evolution of affected soils, in order to control the new saline distributions that these various interventions may induce and preserve these particularly sensitive environments from possible degradation.
The soil salinization is manifested as one of the main factors limiting the development of plants, reduction of arable land and degradation of soil quality by deteriorating the physico-chemical and biological properties of soil and groundwater, which threatens the ‟Food balance” mainly in arid to semi-arid regions.
However, the physico-chemical parameters of the soil are characterized by their evolution both in time and in space. This evolution of the landscape poses a large number of problems and difficulties for soil scientists in monitoring the parameters. But, the use of the traditional tools (laboratory analysis, field) doesn’t allow the monitoring of the speed of spatial and temporal evolution of this parameter causing the land degradation.
This has led us in recent years to explore more rapid and fairly reliable investigation methods such as spatial remote sensing which is of paramount importance for the mapping and monitoring of environmental problems. This approach, based on the study of natural surfaces by satellite images, through the intermediary of spectral surface properties, mainly linked to soil properties.
Remote sensing techniques using intelligent algorithms can predict surface salinity at various time intervals in large-scale regions (Metternicht & Zinck, 2003; Bouaziz et al., 2011; Sidike et al., 2014) such as artificial neural network (Seyam & Mogheir, 2011; Phonphan et al., 2014; Naderi et al., 2017; Ghimire et al., 2019; Li & Wang, 2019; Wang et al., 2018)., classification and regression tree, fuzzy logic, Bayesian analysis, geostatistics, multivariate statistical technique (Principal Components Analysis (PCA), cluster analysis) (bouaziz et al., 2018), were studied to map soil salinity in the past decades (Hihi et al., 2019).
In southern Tunisia, many studies were developed. Bouaziz et al. (2018) showed that in the arid region we can apply the regression model because it is characterized by its efficiency and rapidity on predicting soil salinity. Hihi et al (2019), developed a multiple regression model relate electrical conductivity, sentinel_2 bands and salinity index. The objective of this study is to highlight the relationship between the results of soil analysis by traditional means (laboratory analysis) and those given from satellite images (spectral indices), in order to extract spatial variability of soil salinity in Tataouine region which is characterized as extremely arid zone, in the majority basins, the soils are covered by calcareous and / or gypsum crusts. So it’s threatened by salinity.