This paper uses examples from Australia to argue for a new approach to integrative research in the Earth's near surface environment where the pedosphere, atmosphere, hydrosphere, and biosphere interact, the so-called 'Critical Zone'. In Australia, for around 25years, environmental data layers presented through Geographical Information Systems software have been combined with field-based measurements and observations to produce spatially explicit predictive models for digitally mapping soils and soil properties. The availability of spatially extensive datasets representing different factors of landscape evolution and their exploration with machine learning and rule induction techniques also allow the evaluation of emergent patterns against existing domain knowledge, which in turn can lead to new insights and can facilitate their extrapolation over large areas. Thus the data-driven approach is complementary to the hypothesis-driven scientific inquiry in Critical Zone observatories.