Machine Learning for Soil Moisture Prediction Using Hyperspectral and Multispectral Data

Abstract

Soil moisture content is a key component in terrain characterization for site selection and trafficability assessment. It is laborious and time-consuming to determine soil moisture content using traditional in situ soil moisture sensing methods and may be infeasible for large or dangerous sites. By employing remote sensing techniques, soil moisture content can be determined in a safe and efficient manner. In this work, the results of Keller et al. [1] are expanded upon by reducing the dimensionality of a hyperspectral dataset, resulting in an increase in soil moisture content prediction accuracy. Ten models were developed to predict soil moisture – two machine learning models, support vector machine (SVM) and extremely randomized trees (ET), were trained on 5 input variables. The results indicated that soil moisture content could be predicted with greater accuracy by reducing the dimensionality of a hyperspectral dataset to resemble a standard multispectral dataset. The validity of this method is confirmed by creating a multispectral dataset and concatenating it to the reduced dimensionality (RD) set for an accuracy increase. The ET model’s estimates of soil moisture content outperformed the baseline hyperspectral dataset: obtaining an increase of 1.3% and 5.4% in R-squared values (with a corresponding decrease of .13 and .22 in mean absolute error MAE) when trained on RD and concatenated multispectral (CM) datasets, respectively.