Among the various desertification processes and vegetation degradation are the main processes in rangelands and it often starts with the reduction of vegetation cover. This can result from single or combined effects of overgrazing, rainfall deficits. The effects of these are the appearance of barren soils and an increased susceptibility to wind and water erosion. Vegetation cover is one of the most important components of the earth's surface. It strongly influences evapotrairation, infiltration, runoff and soil erosion. Vegetation cover is also the principal factor limiting stocking rates in managed grazing lands. It has been widely recognized as one of the best indicators for determining land condition. Therefore, land condition can be assessed and monitored according to vegetation cover and its variations in time and space. This component is often used as the key indicator in the remote sensing of land condition. Assessment and monitoring is an important and essential step for rangeland management. Nowadays, satellite images are used in different scales to study vegetation cover. Usually, data vegetation changes are evaluated with studying vegetation spectral characteristic. This study aimed to compare and evaluate pixel (vegetation indices, VIs) and sub-pixel (spectral mixture analysis, SMA) methods in mapping vegetation cover in Semirom region, Isfahan province, using Landsat TM data. For this purpose, Landsat TM image of the region in 2009 were prepared and processed. First, the image was geometrically with a RMSe 0/5 pixel and topographically corrected. The percentage of canopy cover was determined using step-point technique in radial direction (6000 point per site). The collected field data was correlated with different groups of vegetation indices, including slop-based (NDVI), distance-based (PD54), orthogonal transformations (GVI and SBI) and plant-water sensitive (STVI-1) and also SMA. To evaluate the performance of VIs and SMA techniques, vegetation cover data was relationships with field vegetation cover data ( =0.52-0.45 and p 0.05). The GVI and STVI corelated the best in group 25-50 vegetation cover percentage. The NDVI index of the distance-based vegetation indices showed the strongest relationship with cover data ( =0.60 and P 0.01) where the percentage of vegetation cover was higher than 50%. In general, results showed that relationships between SMA and field cover data decreased with increasing vegetation cover and among the pixel and sub-pixel techniques, the PD54 and SMA appear to be the most suitable methods in arid environments with sparse vegetation cover. The results also suggested that NDVI is a useful index for general cover assessment and monitoring regardless of soil and vegetation variation, especially in dense vegetation areas. Because of the broad extent of rangelands, land condition monitoring and assessment using ground-based methods is limited in relation to the information they can provide. Results of this research showed remote sensing techniques, including VIs and SMA could be used as complementary approaches. Keywords: SMA , NDVI , PD54 , GVI , SBI , STVI-1, Isfahan