Sustainable land management and land use planning require reliable information about the spatial distribution of the physical and chemical soil properties affecting both landscape processes and services. Spatial prediction with the presence of spatially dense ancillary variables has attracted research in pedometrics. The main objective of this research is to enhance prediction of soil properties such electrical conductivity (ECe), exchangeable sodium percentage (ESP), available phosphorus (P), organic matter (OM), total nitrogen (TN) and pH by making use of the ancillary variables as covariates. Methods that was used for this purpose may be divided into two groups: (i) those that use only a single variable in the prediction process (simple linear regression (SLR), ordinary kriging (OK)) and (ii) another that make use of additional variables as a part of prediction (simple kriging with a locally varying mean (SKLVM)). LISS­III data from Indian remoter sensing satellite (IRS­P6) were used as secondary data with SKLVM method. Mean square error (MSE) was used to evaluate the performance of the map prediction quality. It was concluded that SKLVM method provided the most accurate predictions based on the summary statistics of prediction errors from cross­validation for mapping OM, pH and ECe. Maps from these kriged estimates showed that a combination of geostatistical techniques and digital data from LISS­III receiver could improve the prediction quality of soil management zones, which is the first step for site­specific soil management.