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High Resolution Multi-Spectral Imagery and Learning Machines in Precision Irrigation Water Management

The current study has been conducted in response to the growing problem of water scarcity and the need for more effective methods of irrigation water management. Remote sensing techniques have been used to match spatially and temporally distributed crop water demand to water application rates. Remote sensing approaches using Landsat imagery have been applied to estimate the components of a soil water balance model for an agricultural field by determining daily values of surface/root-zone soil moisture, evapotranspiration rates, and losses and by developing a forecasting model to generate optimal irrigation application information on a daily basis. Incompatibility of coarse resolution Landsat imagery (30m by 30m) with heterogeneities within the agricultural field and potential underestimation of field variations led the study to its main objective, which was to develop models capable of representing spatial and temporal variations within the agricultural field at a compatible resolution with farming management activities. These models support establishing real-time management of irrigation water scheduling and application. The
AggieAirTM Minion autonomous aircraft is a remote sensing platform developed by the Utah Water Research Laboratory at Utah State University. It is a completely autonomous airborne platform that captures high-resolution multi-spectral images in the visual, near infrared, and thermal infrared bands at 15cm resolution. AggieAir flew over the study area on four dates in 2013 that were coincident with Landsat overflights and provided similar remotely sensed data at much finer resolution. These data, in concert with state-of-the-art supervised learning machine techniques and field measurements, have been used to model surface and root zone soil volumetric water content at 15cm resolution. The information provided by this study has the potential to give farmers greater precision in irrigation water allocation and scheduling.

Identiferoai:union.ndltd.org:UTAHS/oai:digitalcommons.usu.edu:etd-5512
Date01 May 2015
CreatorsHassan-Esfahani, Leila
PublisherDigitalCommons@USU
Source SetsUtah State University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceAll Graduate Theses and Dissertations
RightsCopyright 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 Andrew Wesolek (andrew.wesolek@usu.edu).

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