Spectral Vegetation Indices (SVIs) have been used to examine variations in vegetation formation and phenology. Lately, researchers and agricultural practitioners have utilized SVIs to examine various soil properties for instance moisture and nutrients. From our review of the literature, there were few comprehensive studies conducted to know whether it is possible or not to predict landscape function indices by using remote sensing technology, and which spectral vegetation index is the best predictor. It has been shown that landscape function indices can be accurately predicted by Normalized Different Vegetation Index (NDVI). Therefore, we attempted to test the ability of selected SVIs to predict landscape function indices (LFA-SSA) in the Mediterranean steppes of Al-Jabal Al-Akhdar, northeast Libya. We used data collected between May and October of 2006 and 2014. A total of 28 sites were chosen to collect the data for both SVIs and LFA-SSA. Simple linear regression was applied between LFA-SSA and SVIs. The results demonstrated that there is a positive linear relationship between LFA-SSC and the selected SVIs. The findings revealed that the Normalized Different Vegetation Index (NDVI) and Modified Soil Adjusted Vegetation Index (MSAVI) acquired from the Landsat Enhanced Thematic Mapper Plus (ETM+) could be utilized in predicting the variability of significant structural and functional qualities of soil and vegetation in the Mediterranean climate.