The data collection with Multi Spectral Instrument (MSI) onboard Sentinel-2 satellites and the Operational Land Imager (OLI) installed on Landsat-8 satellite enhance significantly the Earth observation and monitoring with medium spatial resolutions and very high temporal frequency. However, although these instruments are designed to be similar, they have different spectral, spatial and radiometric resolutions. Moreover, relative spectral response profiles characterizing the filters responsivities of the both instruments are not identical between the homologous bands, so some differences are probably expected in the recorded land-surface reflectance values and, therefore, their data probably cannot be reliably used together. This paper analyse and compare the difference between the reflectance in the homologous spectral bands of MSI and OLI sensors, visible-near-infrared (VNIR) and shortwave infrared (SWIR), for high temporal frequency monitoring of soil salinity dynamic in an arid landscapes. In addition, their conversion in term of Soil Salinity and Sodicity Index (SSSI) and in term of Semi-Empirical Predictive Model (SEPM) for soil salinity mapping were compared, and their sensor differences were quantified. To achieve these, analyses were performed on simulated data and on two pairs of images acquired over the same area in July 2015 and August 2017 with one day difference between each pair. For simulated data, a field survey was organized and 160 soil samples were collected with various degrees of soil salinity classes (i.e., extreme, very high, high, moderate, low, and non-saline). The bidirectional reflectance factor was measured above each soil sample in a Goniometric-Laboratory using an Analytical Spectral Devices (ASD) FieldSpec-4 Hi-Res (high resolution) spectroradiometer. Then, these measurements were resampled and convolved in the solar-reflective bands of SMI and OLI using the Canadian Modified Simulation of a Satellite Signal in the Solar Spectrum (CAM5S) radiative transfer code and the relative spectral response profiles characterizing the filters of these instruments. Furthermore, the used pairs of images were not cloudy, or cirrus contaminated, and without shadow effects. They were radiometrically and atmospherically corrected, and the differences related to Bidirectional Reflectance Distribution Function (BRDF) were normalized. To generate data for analysis, similarly to OLI, MSI images were resampled systematically in 30 m by 30 m pixel size considering UTM projection and WGS84 datum. The comparisons of the surface reflectance, and derived SSSI and SEPM were undertaken in the same way for simulated and images data using regression analysis, coefficient of determination (R2), and Root Mean Square Difference (RMSD). The results obtained demonstrate that the statistical fits between SMI and OLI simulated surface reflectance over a wide range of soil samples with different salinity degrees reveals an excellent linear relationship (R2 of 0.99) for all bands, as well as for SSSI and SEPM. The RMSD values are null between the NIR and SWIR homologous bands, and are insignificant for the other bands (i.e., 0.003 for coastal and 0.001 for the blue, green, and red bands). Moreover, the SSSI show an RMSD of 0.0007 and the SEPM express an excellent RMSD around 0.5 dS.m-1 (electrical conductivity unit) reflecting a relative error that varies between 0.001 and 0.05 for salinity classes varying between 2.5 dS.m-1 (non-saline) and 600 dS.m-1 (extreme salinity), respectively. Likewise, the two used pairs of images exhibited very significant fits: R2 of 0.93 for the costal and R2 ≥ 0.96 for the other bands of land surface reflectance, and R2 of 0.95 for SSSI and SEPM. Excellent consistency was also observed between the derived products of the two sensors, yielding a RMSD values less than 0.029 (reflectance units) for the bands and less than 0.004 for SSSI. While, the calculated RMSD for the SEPM fluctuate between 0.12 and 2.65 dS.m-1, respectively, of non-saline and extreme salinity classes, which means that the relative errors varies between 0.005 and 0.03 for the considered soil salinity classes. Therefore, in the light of these results obtained, we can conclude that the MSI and OLI sensors can be used jointly to characterize and to monitor accurately the soil salinity and it’s dynamic in time and space in arid landscape, provided that rigorous preprocessing issues (sensor calibration, atmospheric corrections, and BRDF normalization) must be addressed before.