Digital soil mapping is a rapidly growing area of soil research. Many successful studies have shown that geospatial data can be successfully used to represent soil-forming factors to quantitatively model the distribution of soil properties and classes. The application of this research has great potential for advancing soil survey activities and knowledge of quantitative soil-landscape relationships. In the research presented here, predictive soil property and class models will be generated of soil depth, drainage class, presence/absence of fragipans, and soil series, using multivariate statistical analyses. The unit of study is a watershed of approximately 82,500 acres in West Virginia, on the Monongahela National Forest. To assess the impact of digital elevation model (DEM) resolution on model performance all terrain attributes will be calculated at multiple scales, using a second-order finite difference solution applied to a 3x3 grid. All terrain calculations will be done on a 3-meter DEM generated from West Virginia’s Statewide Addressing and Mapping Board Elevation Conversion Project. A design-based, stratified-random sampling design will be used to quantitatively sample the soils in the watershed. The stratifying variables will be geology, elevation, and stream power index. Within the intersection of these variables on the landscape 90+ soil pits will be hand dug and described to a depth of at least 140 cm or bedrock. One of the goals of this study is to test a quantitative spatial procedure that is applicable to soil survey's objectives and methods.