The aim of this research is to determine to what extent properties of the regolith may be inferred using only features easily measured from the surface. To address this research question, a set of regolith properties from Weipa, Queensland, Australia, are analysed. The set contains five variables, oxides of Aluminium, Iron, Silica and Titanium, as well as Depth to Ironstone. This last represents the depth of the layer from which the oxides are sampled.¶ The research question is addressed in two ways. First, locations where the properties are related to modern surface hydrology are assessed using spatially explicit analyses. This is done by comparing the results of spatial association statistics using geometric and watershed-based spatial samples. Second, correlations are sought for between the regolith properties and geomorphometric indices of land surface morphology and Landsat Thematic Mapper spectral response. This is done using spatially implicit Artificial Neural Networks (ANN) and spatially explicit Geographically Weighted Regression (GWR). The results indicate that the degree to which regolith properties are related to surface measurable features is limited and spatially variable.¶ Most locations in the Weipa landscape exhibit some degree of modern hydrological control of the oxide variables at lateral distances of 120 m. This control rarely extends beyond 300 m laterally, although such locations occupy distinct positions in the landscape. Conversely, there is an extensive part of the landscape where Depth to Ironstone is under hydrological control. This occupies most of the lower elevations in the study area. Depth to Ironstone represents the depth to the redox front where iron is precipitated, but may in some parts of the landscape control the distribution of the watertable by being impermeable.¶ For the correlation analyses, the highest correlations are found with those oxides most mobile in solution. The spatially local GWR results also consistently outperform the spatially global ANN results, commonly having accuracies 40% higher at the error tolerance used. Much of this can be attributed to the localized effects of landscape evolution. Comparison of the GWR results against the local sample mean indicate that there is a relationship between regolith properties and surface measurable features at 10-15% of sample locations for the oxide variables, and 22% for Depth to Ironstone.¶ The implications of these results are significant for anyone intending to generate spatial datasets of regolith properties. If there is a low spatial density of sample data, then the effects of landscape evolution can reduce the utility of any analysis results. Instead, spatially dense, direct measurements of subsurface regolith properties are needed. While these may not be a direct measurement of the property of interest, they may provide useful additional information by which these may be inferred.