The identification of spatial and temporal patterns of soil properties and moisture structures is an important challenge in environmental and soil monitoring as well as for soil landscape model approaches. This work examines the use of hyperspectral remote sensing techniques for quantifying geophysical parameters from the hyperspectral reflectance of the vegetation canopy. These can be used as proxies of the underlying soil and soil water conditions. Different spectral index derivatives, single band reflectance, and spectral indices from the airborne hyperspectral sensor AISA were quantified and tested in univariate and multivariate regression models for their correlation with geophysical measurements with electromagnetic induction (EMI) and gamma-ray spectrometry. The best univariate models for predicting electrical conductivity based on spectral information were based on the vertical dipole of an EM38DD with an R2 = 0.54 with the spectral index Normalized Pigments Reflectance Index (NPCI) as well as for the horizontal dipole of an EM38DD with an R2 = 0.65 with the spectral index NPCI. For predicting soil characteristics measured with gamma-ray spectrometry we received the best model results for γTh with an R2 = 0.55 with the spectral index NPCI and γK with an R2 = 0.44 with the spectral index Triangular Vegetation Index (TVI) and NPCI. The combination of variables including the geographical elevation was tested as the input for a multivariate regression analysis. For EMI and gamma-ray measurements, the “elevation” was found to be the most predictive variable and an integration of spectral indices into the elevation-based model led to only a slight improvement in the predictive power for EMI. An improvement could be made to explain the variance of gamma-ray measurement signals by combining elevation and spectral information.