Record Details
Field | Value |
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Title | Estimation of Soil Moisture at Different Soil Levels Using Machine Learning Techniques and Unmanned Aerial Vehicle (UAV) Multispectral Imagery |
Creator | Aboutalebi, Mahyar Allen, L. Niel Torres-Rua, Alfonso F. McKee, Mac Coopmans, Calvin |
Description | Soil moisture is a key component of water balance models. Physically, it is a nonlinear function of parameters that are not easily measured spatially, such as soil texture and soil type. Thus, several studies have been conducted on the estimation of soil moisture using remotely sensed data and data mining techniques such as artificial neural networks (ANNs) and support vector machines (SVMs). However, all models developed based on these techniques are limited to site-specific applications... |
Date | 2019-05-14T07:00:00Z |
Type | text |
Format | application/pdf |
Identifier | https://digitalcommons.usu.edu/aggieair_pubs/27 info:doi/10.1117/12.2519743 https://digitalcommons.usu.edu/context/aggieair_pubs/article/1028/viewcontent/AGAIRcenter2019AboutalebiAllenTorres_Rua_EstimationSoilMoisture.pdf |
Rights | Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact the Institutional Repository Librarian at digitalcommons@usu.edu. |
Source | AggieAir Publications |
Publisher | Hosted by Utah State University Libraries |
Contributor | International Society for Optical Engineering |
Subject | Soil science Unmanned aerial vehicles Machine learning Computer programming Data modeling Multispectral imaging Aviation |