The development of proximal soil sensors to collect fine-scale soil information for environmental monitoring, modelling and precision agriculture is vital. Conventional soil sampling and laboratory analyses are time-consuming and expensive. In this paper we look at the possibility of calibrating hyperspectral γ-ray energy spectra to predict various surface and subsurface soil properties. The spectra were collected with a proximal, on-the-go γ-ray spectrometer. We surveyed two geographically and physiographically different fields in New South Wales, Australia, and collected hyperspectral γ-ray data consisting of 256 energy bands at more than 20 000 sites in each field. Bootstrap aggregation with partial least squares regression (or bagging-PLSR) was used to calibrate the γ-ray spectra of each field for predictions of selected soil properties. However, significant amounts of pre-processing were necessary to expose the correlations between the γ-ray spectra and the soil data. We first filtered the spectra spatially using local kriging, then further de-noised, normalized and detrended them. The resulting bagging-PLSR models of each field were tested using leave-one-out cross-validation. Bagging-PLSR provided robust predictions of clay, coarse sand and Fe contents in the 0–15 cm soil layer and pH and coarse sand contents in the 15–50 cm soil layer. Furthermore, bagging-PLSR provided us with a measure of the uncertainty of predictions. This study is apparently the first to use a multivariate calibration technique with on-the-go proximal γ-ray spectrometry. Proximally sensed γ-ray spectrometry proved to be a useful tool for predicting soil properties in different soil landscapes.