^a School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia

^b School of Resources, Environment and Society, Faculty of Science, Australian National University, Canberra 0200, Australia

The two methods used are a feed-forward, back propagation neural network, applied globally, and moving window regression, applied locally. These methods are applied to five regolith properties from a field site at Weipa, Queensland, Australia. The properties considered are the proportions of oxides of aluminum, iron, silica and titanium present in samples, as well as depth to the base of the bauxite layer. These are inferred using a set of surface measurable features, consisting of Landsat data, geomorphometric indices, and distances from streams and swamps.

The moving window regression results show a much stronger relationship than do those from the spatially global neural networks. The implication is that the scale of the analysis required for environmental correlation is of the order of hundreds of metres, and that spatially global analyses may incur an automatic reduction in accuracy by not modelling geographically local relationships. In this case, this effect is up to 45% error at a tolerance near half of a standard deviation.