Land Surface Temperature (LST) is an important factor in global change studies. The information about LST has an important role in the range of issues and themes in earth sciences such as global environment change, human-environment interaction and more specifically to urban climatology. This research has been undertaken to analyze the potential of multispectral satellite data for retrieving the biophysical parameters to estimate the land surface temperature in the Tehran semi-arid area of Iran. To gain this, multi-temporal ASTER data were used, Minimum Noise Fraction (MNF) was performed to reduce the data redundancy and correlation between spectral bands, emissivity and LST values were estimated using Temperature and Emissivity Separation (TES) algorithm and vegetation density was extracted using the NDVI and Vegetation Fraction Cover (FVC). The results show that the classification results improve using MNF components in comparison to using of the MLC, LULC has high impact on surface temperature regimes, high dense built up area, bare lands and fallow lands exhibit a higher surface temperature, NDVI and FVC with LST were found to be closely correlated in several LULC categories, especially in Vegetation areas.