  Ground Penetrating Radar (GPR) has been widely studied as a tool for
extracting soil parameters relevant to agriculture and horticulture. When
combined with Machine-Learning-based (ML) methods, high-resolution Stepped
Frequency Countinuous Wave Radar (SFCW) measurements hold the promise to give
cost effective access to depth resolved soil parameters, including at
root-level depth. In a first step in this direction, we perform an extensive
field survey with a tractor mounted SFCW GPR instrument. Using ML data
processing we test the GPR instrument's capabilities to predict the apparent
electrical conductivity (ECaR) as measured by a simultaneously recording
Electromagnetic Induction (EMI) instrument. The large-scale field measurement
campaign with 3472 co-registered and geo-located GPR and EMI data samples
distributed over ~6600 square meters was performed on a golf course. The
selected terrain benefits from a high surface homogeneity, but also features
the challenge of only small, and hence hard to discern, variations in the
measured soil parameter. Based on the quantitative results we suggest the use
of nugget-to-sill ratio as a performance metric for the evaluation of
end-to-end ML performance in the agricultural setting and discuss the limiting
factors in the multi-sensor regression setting. The code is released as open
source and available at
https://opensource.silicon-austria.com/xuc/soil-analysis-machine-learning-stepped-frequency-gpr.
