  Soil moisture sensing through biomass or vegetation canopy has challenged
researchers, even those who use SAR sensors with penetration capabilities. This
is mainly due to the imposed extra time and phase offsets on Radio Frequency
(RF) signals as they travel through the canopy. These offsets depend on the
vegetation canopy moisture and height, both of which are typically unknown in
agricultural and forest fields. In this paper, we leverage the mobility of an
unmanned aerial system (UAS) to collect spatially-diverse radar measurements,
enabling the joint estimation of soil moisture, above-ground biomass moisture,
and biomass height, all without assuming any calibration steps. We leverage the
changes in time-of-flight (ToF) and angle-of-arrival (AoA) measurements of
reflected radar signals as the UAS flies above a reflector buried under the
soil. We demonstrate the effectiveness of our algorithm by simulating its
performance under realistic measurement noises as well as conducting lab
experiments with different types of above-ground biomass. Our simulation
results conclude that our algorithm is capable of estimating volumetric soil
moisture to less than 1% median absolute error (MAE), vegetation height to
11.1cm MAE, and vegetation relative permittivity to 0.32 MAE. Our experimental
results demonstrate the effectiveness of the proposed method in practical
scenarios for varying biomass moistures and heights.
