Monitoring and Evaluation of Spatial Variations in Soil Electrical Conductivity Using Remote Sensing

Soil salinity is an important environmental issue that reduces soil productivity. For optimal management of soil resources, quantitative monitoring of soil salinity, temporal changes and spatial analysis of the factors affecting it are necessary. The purpose of this study is to extract the soil salinity distribution map and its spatial analysis after more than normal rainfall in the rainy year of 1997-98 in western Iran. Using Landsat satellite imagery and GDVI index and algorithm written in Google Engine system, soil electrical conductivity map was extracted and classified into five salinity classes. The results showed that in general, soil salinity has decreased in the study area. Areas with high salinity that are in the low altitude class have not changed. If the precipitation factor in this study period is the most important factor in changes in salinity distribution, this factor could not have a great effect on the salinity class, but the medium salinity class had the most changes. Move the soil surface of this class down. For the ellipse, three times the standard spatial deviation of the northwest to the southeast was obtained, which shows that more than 99% of the salinity dispersion follows the spatial arrangement of altitudes, precipitation and dispersion of soil categories in this direction. Statistics of 0.4566 of Moran index and P_Value showed 0.00 spatial salinity of soil salinity in the west of the country. The hot-spot map also showed that the surface salinity of the soil is clustered in the northwest and southeast directions at altitudes of less than 1200 meters. Hot-spot analysis also showed that soil salinity to the east and inland has found more clustering pattern. The results and methods of this research can easily and quickly identify areas that are exposed to soil salinity and could be used in environmental planning to implement preventive measures. The results of this research are also used and its outputs are also utilized to identify soil salinity centers in agricultural planning and allocation of facilities.