High-resolution digital soil mapping: kriging for very large datasets

The ability to take many observations at precisely known spatial locations has given birth to precision agriculture and transformed traditional agriculture into a spatial science. An important aspect of precision agriculture is its intersection with pedometrics. Maps of soil properties are in great demand, but there is a point at which datasets from proximal soil sensors can, when very large, overload and 'break' the algorithms designed for production of the statistically optimal (kriging) maps. In this research, we present a geostatistical method that relies on highly flexible, nonstationary spatial covariances, for which exact kriging can be carried out for very large datasets (on the order of tens of thousands to hundreds of thousands of elements). The methodology is applied to total counts obtained from gamma radiometer readings in several fields of Nowley Farm, New South Wales, Australia.