Soil salinity map preparation using spectral analysis of OLI sensor and field data (Case study: Southern parts of Malayer plain)

Soil salinity in arid and semi-arid lands is one of the most important limiting factors which changes vegetation types and biomass due to natural resourcesproduction reduction. The Landsat 8 satellite images were used in this research to select the best satellite indices for soil salinity evaluation. For achieving this purpose, the distribution maps of sodium, potassium, calcium, EC and pH were prepared based on 77 measurement points. After that, the correlations between the field observation and produced maps by remote sensing have been investigated by using spatial regression. The soil salinity and alkalinity maps of study area have been prepared by using the distribution maps of sodium, potassium, calcium, EC and pH and based on suitable regression model. The results showed that SIand M-SI were the best indicators for detecting soil EC and soil sodium. other measured elements of soil in the study area. However, the best indicators for detecting soil distribution of magnesium, calcium and potassium was NDSI. There was a weak correlation between field observations and soil alkalinity map.Generally, NDSI was the best index for assessing the studied elements. According to the regression equation between derived indices and prepared maps, the optimal models for mapping soil salinity were determined and calibrated. Based on satellite images models which obtained in this study are good estimators to estimate the above mentioned elements. The results of this study could have importance in land zoning accuracy, using satellite images without the need for field sampling in the future.