The geomechanical characterization of bedrock on a regional scale is important for assessing, monitoring, and modelling rock slope instability and mass movements. However, these properties and their spatial distribution are not always readily available, which leads to either resource-intensive, site-specific investigations, or broad assumptions that do not account for local variability. Remote sensing can quickly acquire data over large (c. 104-107 km2) spatial extents, and thereby complement field and laboratory data collection. Spectroscopy collects reflected light from hundreds of narrow and contiguous spectral bands in the visible, near- and shortwave infrared wavelengths, capable of identifying mineral compositions via the material’s unique electromagnetic fingerprint. Correlation of geomechanical properties with spectroscopy measurements has great potential for translating readily obtainable information (i.e. reflectance signatures via aerial or satellite remote sensing) into more difficult to obtain physical properties (e.g. elasticity, strength via laboratory testing). As a first step towards estimating properties on a regional scale, we analyze the linear Pearson-type correlation between the physical and mechanical rock properties and the reflected light signatures collected from a hand-held spectroradiometer that records 2151 spectral bands between 350 and 2500 nm. The rock samples are from a suite of varyingly weathered and hydrothermally altered andesites hand samples from Mt Ruapehu volcano in New Zealand. Many properties, including magnetic susceptibility, elastic wave velocities, uniaxial compressive strength, and elastic parameters, correlate strongly to the visible spectrum, which we attribute to the formation of iron oxides through weathering and alteration. Additionally, porosity and permeability have a strong correlation in the near infrared and shortwave infrared regions, which we attribute to hydrothermal alteration and the presence of clays. Several properties show high Pearson’s R correlation values to spectral reflectance at various wavelengths, including P-wave seismic velocity (Pearson’s R of 0.84 at 371 nm), density (R = 0.76 at 356 nm), and porosity (R = 0.74 at 370 nm). Statistically predicting rock geotechnical properties using this approach can contribute to large-scale mapping, modeling, and hazard mitigation efforts.