Existing local and regional soil datasets provide relatively coarse spatial resolution, with poor constraint on both spatial and absolute variation of soil properties. Advancing remote sensing products, such as Interferometric Synthetic Aperture Radar (IFSAR) elevation data, coupled with new digital soil mapping techniques allow the quantitative prediction of soil and landscape attributes and provide a means to capture soil-landscape relationships that might otherwise be missed, thereby improving soil information. Our objectives were to develop a data driven soil prediction model for estimating physical and hydraulic soil properties in a semiarid ecosystem (~6070 ha) using auxiliary data. An iterative data reduction approach using principal component analysis was used to determine the auxiliary data (surface reflectance and elevation indices) that quantified the most variance in the study area. These data were used to develop a statistically robust sampling design using a conditioned Latin hypercube method. Results indicate that 50 – 100 samples adequately represented auxiliary data layers in this study area. We sampled 53 locations in the field by genetic horizon to 30 cm and measured particle size distribution, Munsell color, and organic matter. Initial field validation suggests Munsell color and particle size can be predicted across the landscape by utilizing auxiliary data for interpolation techniques. Prediction of organic matter proved more challenging likely due to the generally low levels across the study area. Confidence maps produced for each soil property highlighted areas with high prediction error and provide insight to how models could be improved. This research combines a novel approach for identifying important auxiliary data with relatively new digital soil mapping techniques for improving soil prediction. This approach can overcome limitations of regionally specific predictive mapping, allow incorporation of multiple data types, and provide accurate quantitative prediction of individual soil properties for improved land management decisions and ecosystem and hydrologic models.