@inproceedings{679f3fb6f21c4c3bb15c32f6709edfb9,
title = "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.",
keywords = "Hyperspectral, Machine learning, Multispectral, Remote sensing, Soil moisture content, Trafficability",
author = "Michaela Lobato and Norris, {William Robert} and Rakesh Nagi and Ahmet Soylemezoglu and Dustin Nottage",
note = "Publisher Copyright: {\textcopyright} 2021 International Society of Information Fusion (ISIF).; 24th IEEE International Conference on Information Fusion, FUSION 2021 ; Conference date: 01-11-2021 Through 04-11-2021",
year = "2021",
language = "English (US)",
series = "Proceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021",
address = "United States",
}