Salinity is a major problem affecting crop production; almost 20% of cultivated land and 33% of irrigated land are salt-affected and degraded in the world. Adverse irrigation applications together with natural formations and activities like salt lakes and seawater intrusion in coastal areas lead to salinization of agricultural fields and the environment. Therefore, such factors should be identified, monitored and tackled for sustainable and effective management of the affected land. Remote sensing systems have great potential for investigating, detecting and monitoring these problems in the long term. Unlike traditional methods, medium resolution satellites such as Landsat and Sentinel provide efficient and economic information regarding the degree of soil salinity with their rapid data collection and temporal monitoring capabilities. Urmia Lake Basin located in Iran is one of these significant regions of the world suffering from soil salinity due to both natural causes and anthropogenic activities. As such, it is highly important to detect and map saline soils in the basin in order to diminish the loss of fertile soils spared as agricultural land. The aim of this study is to compare the performance of different soil salinity indices derived from visible and infrared bands of Sentinel 2 satellite for mapping the soil salinity at Urmia Lake Basin. Almost 70 soil samples from an area of 18 km2 in the western side of the lake were collected in October 2016 and soil salinity values were determined. Field measurements and remote sensing data were used for mapping salinity distribution. Soil salinity indices (G2+R2+NIR2)0.5, (B*R/G), (R*NIR)0.5 were derived from visible and infrared bands of Sentinel 2A. Linear regression analyses were further applied for correlating the ground-based data and satellite-based indices. As a result, it was observed that the soil salinity indices calculated from the visible spectral bands provided better results compared to salinity indices c
