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In this paper, we investigate the potential of estimating the soil-moisturecontent based on VNIR hyperspectral data combined with LWIR data. Measurementsfrom a multi-sensor field campaign represent the benchmark dataset whichcontains measured hyperspectral, LWIR, and soil-moisture data conducted ongrassland site. We introduce a regression framework with three steps consistingof feature selection, preprocessing, and well-chosen regression models. Thelatter are mainly supervised machine learning models. An exception are theself-organizing maps which combine unsupervised and supervised learning. Weanalyze the impact of the distinct preprocessing methods on the regressionresults. Of all regression models, the extremely randomized trees model withoutpreprocessing provides the best estimation performance. Our results reveal thepotential of the respective regression framework combined with the VNIRhyperspectral data to estimate soil moisture measured under real-worldconditions. In conclusion, the results of this paper provide a basis forfurther improvements in different research directions.

Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data