Digitally mapping the information content of visible–near infrared spectra of surficial Australian soils

We can use soil mapping to gain a better understanding of the soil and how it varies in the landscape. Good quality data sets that represent the survey area are important to develop quantitative spatial models for soil mapping and to evaluate their outputs. Over the past three decades, scientists have become interested in rapid, non-destructive measurements of the soil using visible–near infrared (vis–NIR) (400–2500nm) and mid infrared (mid-IR) (2500–25,000nm) diffuse reflectance spectra. These spectra provide an integrative technique that measures the fundamental characteristics and composition of the soil, including colour, iron oxide, clay and carbonate mineralogy, organic matter content and composition, the amount of water present and particle size. If adequately summarised and exhaustively available over large areas, this information might be useful in situations where reliable, quantitative soil information is needed, such as agricultural, environmental and ecological modelling, or for digital soil mapping. The aims of this paper are to summarise the information content of vis–NIR spectra of Australian soils and to use a predictive spatial modelling approach to digitally map this information across Australia on a 3-arc second grid (around 90m). We measured the spectra of 4606 surface soil samples from across Australia using a vis–NIR spectrometer. The soil information content of the spectra was summarised using a principal component analysis (PCA). We used model trees to derive statistical relationships between the scores of the PCA and 31 predictors that were readily available and we thought might best represent the factors of soil formation (climate, organisms, relief, parent material, time and the soil itself). The models were validated and subsequently used to produce digital maps of the information content of the spectra, as summarised by the PCA, with estimates of prediction error at 3-arc seconds pixel resolution. The most frequently used predictors at the continental scale were factors related to climate, parent material (and time), while at landscape and more local scales, they were factors related to relief, organisms and the soil. Finally, we use our maps for pedologic interpretations of the distribution of soils in Australia. Our results might be useful in situations requiring high-resolution, quantitative soil information e.g. in agricultural, environmental and ecologic modelling and for soil mapping and classification.