Abstract. The study aims to analyze the ability of the most popular and widely used vegetation indices (VI’s), including NDVI, SAVI, EVI and TDVI, to discriminate and map soil salt contents compared to the potential of evaporite mineral indices such as SSSI and NDGI. The proposed methodology leverages on two complementary parts exploiting simulated and imagery data acquired over two study areas, i.e. Kuwait-State and Omongwa salt-pan in Namibia. In the first part, a field survey was conducted on the Kuwait site and 100 soil samples with various salinity levels and contents were collected; as well as, herbaceous vegetation cover canopy (alfalfa and forage plants) with various LAI coverage rates. In a Goniometric-Laboratory, the spectral signatures of all samples were measured and transformed using the continuum removed reflectance spectrum (CRRS) approach. Subsequently, they were resampled and convolved in the solar-reflective spectral bands of Landsat-OLI, and converted to the considered indices. Meanwhile, soil laboratory analyses were accomplished to measure pHs, electrical conductivity (EC-Lab), the major soluble cations and anions; thereby the sodium adsorption ratio was calculated. These elements support the investigation of the relationship between the spectral signature of each soil sample and its salt content. Furthermore, on the Omongwa salt-pan site, a Landsat-OLI image was acquired, pre-processed and converted to the investigated indices. Mineralogical ground-truth information collected during previous field work and an accurate Lidar DEM were used for the characterization and validation procedures on this second site. The obtained results demonstrated that regardless of the data source (simulation or image), the study site and the applied analysis methods, it is impossible for VI's to discriminate or to predict soil salinity. In fact, the spectral analysis revealed strong confusion between signals resulting from salt-crust and soil optical properties in the VNIR wavebands. The CRRS transformation highlighted the complete absence of salt absorption features in the blue, red and NIR wavelengths. As well as the analysis in 2D spectral-space pointed-out how VI’s compress and completely remove the signal fraction emitted by the soil background. Moreover, statistical regressions (p ˂ 0.05) between VI's and EC-Lab showed insignificant fits for SAVI, EVI and TDVI (R2 ≤ 0.06), and for NDVI (R2 of 0.35). Although the Omongwa is a natural flat salt playa, the four derived VI’s from OLI image are completely unable to detect the slightest grain of salt in the soil. Contrariwise, analyses of spectral signatures and CRRS highlighted the potential of the SWIR spectral domain to distinguish salt content in soil regardless of its optical properties. Likewise, according to Kuwait spectral data and EC-Lab analysis, NDGI and SSSI incorporating SWIR wavebands have performed very well and similarly (R2 of 0.72) for the differentiation of salt-affected soil classes. These statistical results were also corroborated visually by the maps derived from these evaporite indices over the salt-pan site, as well as by their consistency with the validation points representing the ground truth. However, although both the indices have mapped the salinity patterns almost similarly, NDGI further highlights the gypsum content. While the SSSI show greater sensitivity to salt crusts present in the pan area that are formed from different mineral sources (i.e., halite, gypsum, etc.).