Soil colour is an important property of soils that reflects many of the soils properties and so is used widely in classifying soils. It is a potentially useful parameter for soil studies at a regional scale. Soil colour is the most commonly determined by visually matching a soil sample with standard Munsell charts. The Munsell system is based on three coordinates: hue, which refers to the dominant wavelength; chroma, which expresses the saturation of the colour; and value, which represents the overall brightness. Munsell hue, value and chroma of soil surfaces were found to be the most useful, easily determinate soil properties in the evaluation of various soil types. All soil processes that greatly affect the soil surface may be detected by colour modifications. Generally, eroded soil show higher Munsell values, as a consequence of the removal of top soil and subsequent decrease in organic matter. In more severe cases, the top soil can be totally removed and other soil material will appear at the surface, with a colour significantly different from that of non-eroded soil. Nevertheless, traditional methods to observe the soil colour is based upon visual and spectral assessments, although important and irreplaceable for field studies, can not provide a suitable solution for large areas. Modern satellite and aircraft remote sensors can advantageously contribute to this end. Up to now, several researchers and scientists have carried out many studies to show significant relationship between soil colour parameters and multi spectral reflectance values in the wavebands of commercial orbiting satellites. Therefore, in this paper, it is tried to determine a relationship between soil colour and normalized difference vegetation index (NDVI). If relationship between Munsell colour and NDVI can be developed, then there is a great potential for using National Oceanic and Atmospheric Administration (NOAA)/ Advanced Very High Resolution Radiometer (AVHRR) data at coarse spatial resolution of 1.1 Km to study and map oil over large areas and also helpful to predict the erosion process. The basic approach behing to correlate NDVI with soil colour is that, because NDVI is often considered primarily a function of climate, terrain, vegetation/ecosystem, and soil variables. Therefore, we assumed NDVI is a function of soil colour and surface temperature at specific location. Here, we have taken surface temperature and soil colour together, because surface temperature is greatly affected by soil colour. Using this approach, we have developed an linear empirical model to correlate the NDVI with Munsell soil colour (i.e., hue, value and chroma). A good correlation between NDVI and hue and NDVI and chroma have been obtained. It was also found that NDVI is highly dependent on hue and chroma in comparison to value. This type of study is helpful to develop a relationship between NDVI and soil colour to assess the soil colour remotely using satellite date. If, we shall able to assess the soil colour remotely than we can also predict the erosion process in the particular area using the satellite data.