Our understanding of the soil and assessment of its quality and function has been gained through routine soil chemical and physical laboratory analysis (Viscarra Rossel et al., 2006). Soil Health Card (SHC) in India was issued in 2015 to farmers to increase productivity and earn more profit. For this purpose soil survey is carried out to record the crop limiting factors by evaluation of soil fertility based on soil analysis (Govind Goyal, 2016). There is a global thrust towards the development of more time- and cost-efficient methodologies for soil analysis as there is a great demand for larger amounts of good quality, inexpensive soil data to be used in environmental monitoring, modeling and precision agriculture (CIA, 2016). Hyperspectral remote sensing, provides a good alternative that may be used to enhance or replace conventional methods of soil analysis, as it overcomes some of their limitations. This modern technique is rapid, timely, less expensive, non-destructive, straight forward and sometimes more accurate than conventional analysis. Furthermore, a single spectrum allows for simultaneous characterization of various soil properties and the techniques are adaptable for on-the-go field use (Dematte et al., 2004). The majority of soil properties can be estimated used hyperspectral remote sensing which occur in spectra range of 0.4 to 25 µm (Ben-Doret al., 2009). Digital Soil Mapping (DSM) is the creation and population of spatial soil information systems by numerical models inferring the spatial and temporal variations of soil types and soil properties from soil observation and knowledge and from related environmental variables (Lagacherie et al, 2015). To produce digital soil map, there are some steps should be followed such, collection of data after detection soil covariates, producing calibration, validation and prediction models, hardware and software analysis then generating and presenting the digital soil map and assessment it’s quality. Partial least-square regression (PLSR) as a common statistical technique used to find a correlation between the spectral data and selected soil properties. Dematte et al. (2015) used UV-Vis-NIR Spectroscopy for assessment some soil properties and they found that Fe2O3, Al2O3 and clay had predictability with R2 > 0.80 and may be applied to a different database than the one that was used to generate the equations of prediction models of quantitatively determined soil parameters. Garfagnoli et al. (2013) generated a complete mapping procedure using the 2000-2450 nm spectral region band hyperspectral air-borne images for estimation of clay content which is considered as an important factor of soils sensitive to erosion or degradation. They found correlation between the observed and the predicted values of soil clay content (R2=0.73). Several soil properties e.g., N, P, K, Fe, Mn, Cu, Zn, CaCO3, SOC, pH and EC were quantitatively predicted using the spectral range of 0.4 to 2.5µm, for which R2 ranged from 0.66 to 0.93, in a study carried out in Punjab, India, by Kadupitiya et al., (2010). Another study which was carried out in USA, soil properties viz Texture, OM, K, Ca, Mg, Fe, Zn, Mn, Cu, B, Al, S, C and N can be characterized and mapped using airborne-HRS with R2 between 0.6 and 0.8 of some soil properties (Hively et al. 2011). Soil Clay and CaCO3 contents were identified and mapped using HRS in similar study in France (Lagacherie et al, 2015). From the available literature it was found that accuracy of statistical approaches such as PLSR predictions, in integration with spectral and laboratory data varied considerably amongst soil properties. By the application of HRS, there is a possibility for cost, labour savings and increased efficiency over conventional laboratory analysis also for producing semi-accurate digital soil map.