The solution to inverse problems is an application shared by mathematicians,
scientists, and engineers. For this work, a set of shallow soil temperatures measured
at eight depths between 0 and 30 cm and sampled every five minutes over 24 hours is
used to determine the diffusivity of the soil. Thermal diffusivity is a modeling
parameter that impacts how heat flows through soil. In particular, it is not known in
advance if the subsurface region is homogeneous or heterogeneous, which means the
thermal diffusivity may or may not depend on depth. To this end, it is not clear
which assumptions may apply to represent the physical system embedded within the
parameter estimation problem. Analytic methods and a simulation based
least-squares approach to approximate the diffusivity are compared to fit
the temperature profiles to different heat flow models. The simulation is
based on a spatially dependent, nonsteady-state discretization to a partial
differential equation. To complete the work, a statistical sensitivity study
using analysis of variance is used to understand how errors that arise in
the modeling phase impact the final model. We show that for the analytic
methods, errors in the initial fitting of the temperature data to sinusoidal
boundary conditions can have a strong impact on the thermal diffusivity
values. Our proposed framework shows that this soil sample is heterogeneous
and that modeling depends significantly on data used as top and bottom
boundary conditions. This work offers a protocol to determine the soil type
and model sensitivities using analytic, numerical, and statistical approaches
and is applicable to modifications of the classic heat equation found across
disciplines.