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

Abstract

In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with IR data. Measurements from a multi-sensor field campaign represent the benchmark dataset which contains measured hyperspectral, IR, and soil-moisture data. 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 are a combination of 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. In conclusion, the results of this paper provide a basis for further improvements in different research directions.

Publication
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences