  The frequency dependence of dielectric material properties of water saturated
and unsaturated porous materials such as soil is not only disturbing in
applications with high frequency electromagnetic (HF-EM) techniques but also
contains valuable information of the material due to strong contributions by
interactions between the aqueous pore solution and mineral phases. Hence,
broadband HF-EM sensor techniques enable the estimation of soil
physico-chemical parameters such as water content, texture, mineralogy, cation
exchange capacity and matric potential. In this context, a multivariate (MV)
machine learning approach (principal component regression, partial least
squares regression, artificial neural networks) was applied to estimate the
Soil Water Characteristic Curve (SWCC) from experimentally determined
dielectric relaxation spectra of a silty clay soil. The results of the
MV-approach were compared with results obtained from empirical equations and
theoretical models as well as a novel hydraulic/electromagnetic coupling
approach. The applied MV-approach gives evidence, (i) of a physical
relationship between soil dielectric relaxation behavior and soil water
characteristics as an important hydraulic material property and (ii) the
applicability of appropriate sensor techniques for the estimation of
physico-chemical parameters of porous media from broadband measured dielectric
spectra.
