  In this paper, we investigate the potential of estimating the soil-moisture
content based on VNIR hyperspectral data combined with LWIR data. Measurements
from a multi-sensor field campaign represent the benchmark dataset which
contains measured hyperspectral, LWIR, and soil-moisture data conducted on
grassland site. We introduce a regression framework with three steps consisting
of feature selection, preprocessing, and well-chosen regression models. The
latter are mainly supervised machine learning models. An exception are the
self-organizing maps which combine unsupervised and supervised learning. We
analyze the impact of the distinct preprocessing methods on the regression
results. Of all regression models, the extremely randomized trees model without
preprocessing provides the best estimation performance. Our results reveal the
potential of the respective regression framework combined with the VNIR
hyperspectral data to estimate soil moisture measured under real-world
conditions. In conclusion, the results of this paper provide a basis for
further improvements in different research directions.
