Filling in the Gaps: A Technique for Creating Spatially Continuous
Soil Data from the Discontinuous SSURGO Dataset

Mapping and modeling of landscape attributes requires that input data have continuous spatial coverage – meaning that data gaps are unacceptable. Specifically concerning vegetation mapping, soil characteristics are sometimes a key driver in restricting/permitting the distribution of plant species and communities. Because the LANDFIRE project is chartered with producing spatially comprehensive vegetation maps across the entire United States, “wall-to-wall” soil data is a necessary input. However, the only spatially continuous soil dataset that covers the contiguous United States, STATSGO, is too coarse for LANDFIRE’s intended mid-scale resolution. Conversely, the other national soil dataset, SSURGO, while at a fairly fine scale, is not spatially continuous over broad extents. We developed methods that utilize GIS data, Landsat imagery and nearest neighbor imputation for predicting SSURGO soil characteristics in areas where SSURGO data do not exist. This process creates a spatially continuous, fine-scale soil dataset that has been useful in mapping vegetation across broad extents.