Generating geochemical and mineralogy distributions of soil in the conterminous United States using Bayesian hierarchical spatial models

TL;DR

This paper introduces a Bayesian modeling approach to predict soil geochemistry and mineral distributions across the United States, using environmental data to improve accuracy.

Contribution

The novel contribution is a Bayesian hierarchical spatial modeling workflow using INLA for generating geochemical and mineral soil maps in the conterminous U.S.

Findings

A Bayesian workflow using INLA was developed to model soil geochemistry and mineralogy distributions.

The model incorporates environmental covariates like soil properties, topography, climate, and land cover.

Predictive maps for trace elements and minerals relevant to ecological and agricultural applications were generated.

Abstract

Characterizing geochemical and mineralogical soil distributions across large spatial extents is essential for understanding mineral resources, ecosystem processes, and environmental risks. Rasters of soil geochemical distributions for the conterminous United States, however, are limited. We present a Bayesian modeling workflow and tool for generating predictive geochemical and mineralogy distribution maps for the conterminous United States using integrated nested Laplace approximation (INLA) with the stochastic partial differential equation approach. By modeling soil geostatistical data with environmental covariates (soil properties, topography, climate, and land cover), we generate predictive distributions of soil geochemistry that can be mapped or extracted for further analyses. As an example, we model the spatial distribution of trace elements in soil relevant to vertebrate health (cobalt, copper, iron, manganese, selenium, and zinc) and provide a workflow that can be used to generate and visualize predictive distributions of 39 other major and trace elements and 21 minerals of the soil survey, supporting a variety of ecological, environmental, and agricultural applications.

Bayesian Modeling: Uses R-INLA to predict soil geochemistry across large spatial extents.

Covariate Integration: Incorporates environmental variables to increase predictive accuracy.

Raster Generation: Produces continuous geospatial layers of element and mineral distributions of the conterminous United States for a variety of applications.

Image, graphical abstract

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