Bayesian kriging is a useful tool for estimating spatial distributions of metals; however, estimates are generally only verified statistically. In this study surface soil samples were collected on a uniform grid and analyzed for As, Cr, Pb, and Hg. The data were interpolated at individual locations by Bayesian kriging. Estimates were validated using a leave-one-out cross validation (LOOCV) statistical method which compared the measured and LOOCV predicted values. Validation also was carried out using additional field sampling of soil metal concentrations at points between original sampling locations, which were compared to kriging prediction distributions. LOOCV results suggest that Bayesian kriging was a good predictor of metal concentrations. When measured internode metal concentrations and estimated kriged values were compared, the measured values were located within the 5th – 95th percentile prediction distributions in over half of the internode locations. Estimated and measured internode concentrations were most similar for As and Pb. Kriged estimates did not compare as well to measured values for concentrations below the analytical minimum detection limit, or for internode samples that were very close to the original sampling node. Despite inherent variability in metal concentrations in soils, the kriged estimates were validated statistically and by in situ measurement.