Soil moisture takes an important part in involving climate, vegetation and drought. This paper explains that how to calculate the soil moisture index and the role of soil moisture. The objective of this study is to assess the amount of moisture content in soil and soil moisture mapping by using remote sensing data, in the selected study area. We applied the remote sensing technique with the purpose of relies on the use of soil moisture index (SMI) which in its algorithm uses the data obtained from satellite sensors. The relation between land surface temperature (LST) and normalized difference vegetation index (NDVI) are based on experimental parameterization for Soil moisture index. Multispectral satellite data (visible, NIR and TIRS) were utilized for assessment of Land Surface Temperature (LST) and make vegetation indices map. GIS and image processing software utilized to determine the LST & NDVI. NDVI and LST are considered as essential data to obtain SMI calculation. The statistical regression analysis of NDVI and LST were shown in standardized regression coefficient. NDVI values are within range -1 to 1 where negative values present loss of vegetation or contaminated vegetation, whereas positive values explain that healthy and dense vegetation. LST values are the surface temperature in °C. SMI is categorized into classes from no drought to extreme drought to quantitatively assess drought. The final result is obtainable with the values range from 0 to 1, where values near 1 are the regions with a low amount of vegetation and surface temperature and present a higher level of soil moisture. The values near 0 are the areas with a high amount of vegetation and surface temperature and present the low level of soil moisture. The results indicate that this method can efficiently applied to estimation of soil moisture from multi-temporal Landsat images, which is valuable for monitoring agricultural drought and flood disasters assessment.