Soil salinization is one of the main environmental issues especially in semi-arid and arid regions of the world typically known by low precipitation rate, high temperature and evaporation values. Salt accumulation in soil constrains soil fertility in arable lands and diminishes sustainable crop production. Rapid population growth, and in turn, necessity to supply sufficient food has increased attention of decision- makers and soil scientists on monitoring and mapping soil salinity to mitigate with the loss of agricultural lands. To prevent soil salinization and promote management of saline soil, information on the spatial distribution of soil salinization is required. Monitoring salinization by traditional methods will require periodical and systematic field sampling which is usually a costly and time consuming process. Advances in remote sensing technology with new satellites and methods have provided fast, accurate and economic alternatives for detecting soil salinity in various field scales. Appropriate methods to derive soil salinity parameters from remote sensing data that can be used for various environments and climate regimes are still in progress in parallel to developments in satellite technology. This study aims to map soil salinity by correlating Electrical Conductivity (EC) field measurements with three soil salinity indices derived from Landsat-8 OLI satellite image over the Bonab County of East Azerbaijan Province in Iran suffering from soil salinity. Totally 77 samples were acquired from the top 25cm of surface soil in Autumn 2014. After removing 22 outliers, 45 samples were used for modelling and the remaining 10 samples were utilized for validation. Linear regression analyses were conducted to correlate the field EC data with the corresponding three soil salinity spectral indices of the same sampling stations. The results demonstrated that all the three soil salinity indices derived from visible and near infrared bands of Landsat -8 OLI image predi
