Applying Geochronology in Predictive Digital Mapping of Soils

Abstract

We review various recent approaches to making digital soil maps based on geographic information systems (GIS) data layers, note some commonalities and propose a generic framework for the future. We discuss the various methods that have been, or could be, used for fitting quantitative relationships between soil properties or classes and their 'environment'. These include generalised linear models, classification and regression trees, neural networks, fuzzy systems and geostatistics. We also review the data layers that have been, or could be, used to describe the 'environment'. Terrain attributes derived from digital elevation models, and spectral reflectance bands from satellite imagery, have been the most commonly used, but there is a large potential for new data layers. The generic framework, which we call the scorpan-SSPFe (soil spatial prediction function with spatially autocorrelated errors) method, is particularly relevant for those places where soil resource information is limited. It is based on the seven predictive scorpan factors, a generalisation of Jenny's five factors, namely: (1) s: soil, other or previously measured attributes of the soil at a point; (2) c: climate, climatic properties of the environment at a point; (3) o: organisms, including land cover and natural vegetation; (4) r: topography, including terrain attributes and classes; (5) p: parent material, including lithology; (6) a: age, the time factor; (7) n: space, spatial or geographic position. Interactions (*) between these factors are also considered. The scorpan-SSPFe method essentially involves the following steps: (i) Define soil attribute(s) of interest and decide resolution ρ and block size β. (ii) Assemble data layers to represent Q. (iii) Spatial decomposition or lagging of data layers. (iv) Sampling of assembled data (Q) to obtain sampling sites. (v) GPS field sampling and laboratory analysis to obtain soil class or property data. (vi) Fit quantitative relationships (observing Ockham's razor) with autocorrelated errors, (vii) Predict digital map. (viii) Field sampling and laboratory analysis for corroboration and quality testing. (ix) If necessary, simplify legend or decrease resolution by returning to (i) or improve map by returning to (v). Finally, possible applications, problems and improvements are discussed. © 2003 Elsevier B.V. All rights reserved.