Workflow to predict geochemical distributions of soil

Mapping soil geochemistry across large spatial extents is essential for understanding mineral distributions and their environmental implications. However, rasters of soil geochemical distributions for the United States are limited. We present a Bayesian modeling framework for generating predictive geochemical distribution maps using integrated nested Laplace approximation in R (R-INLA).