Beginning with a discussion of reflectance spectroscopy, this article attempts to provide a review on fundamental concepts of reflectance spectroscopic techniques. Their applications as well as exploring the role of Near-infrared reflectance spectroscopy that would be used for monitoring and mapping soil characteristics. This technique began to be used in the second half of the 20th century for industrial purposes. Moreover, this article explores the potentiality of predicting soil properties based on spectroscopic measurements .Quantitative prediction of soil properties such as; salinity, organic carbon, soil moisture and heavy metals can be conducted using various calibration models – such models were developed depending on the measured soil laboratory analyses data and soil reflectance spectra thereby resampled to satellite images - to predict soil properties. The most common used models are stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), multivariate adaptive regression splines (MARS), principal component regression (PCR) and artificial neural networks (ANN). Those methods are required to quickly and accurately measure soil characteristics at field to improve soil management and conservation at local and regional scales. Visable-Near Infra Red (VIS-NIR) has been recommended as a quick tool for mapping soil properties. Furthermore, VIS-NIR reflection spectroscopy reduces the cost and time, therefore has a wonderful ability and potential use as a rapid soil analysis for both precision soil management and assessing soil quality. Keywords: Near infrared spectroscopy, Soil salinity, Soil moisture, Soil organic carbon, Soil surface features and soil contamination