Science Wednesday: the shapes of soils

One of our key science challenges over the course of the Digiscape Future Science Platform has been to characterise Australia’s agricultural landscapes with sparsely-measured data: climate, soils, vegetation and management. Soil moisture characteristic data are in particularly short supply.

Historically, spatial inference of soil attributes for modelling analyses has taken one of two paths:

• continuous spatial interpolation, attribute-by-attribute (e.g. GlobalSoilMap and its Australian implementation, the Soil and Landscape Grid of Australia); or
• classifying each location to a modestly-sized set of categories, each of which has a “representative” (and often manually-selected) measured soil profile assigned to it.

There is a tradeoff here between the need to reduce the uncertainty in the estimates of the individual attributes describing a soil profile and the need to ensure that the estimates of the different attributes are coherent with one another.

A recent paper in Geoderma by Dan Gladish, Di He and Enli Wang has opened up a path to intermediate – and perhaps more effective – solutions to that tradeoff. By carrying out pattern analysis of the shapes and sizes of a library of measured soil moisture characteristics, they derived a small set of classes or “patterns” for which a rigorously-defined representative profile can be described.

This better way of defining the “representative” soil profiles raises the possibility of profile-centric digital soil maps, in which the landscape is mapped to the soil “patterns” and then continuously-interpolated maps of widely-measured attributes such as clay content and bulk density can be used to adjust the reference profile to improve the accuracy of the attribute estimates at each depth.