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    Estimation of Spatially Distributed Evapotranspiration Using Remote Sensing and a Relevance Vector Machine

    Source: Journal of Irrigation and Drainage Engineering:;2014:;Volume ( 140 ):;issue: 008
    Author:
    Roula Bachour
    ,
    Wynn R. Walker
    ,
    Andres M. Ticlavilca
    ,
    Mac McKee
    ,
    Inga Maslova
    DOI: 10.1061/(ASCE)IR.1943-4774.0000754
    Publisher: American Society of Civil Engineers
    Abstract: With the development of surface energy balance analyses, remote sensing has become a spatially explicit and quantitative methodology for understanding evapotranspiration (ET), a critical requirement for water resources planning and management. Limited temporal resolution of satellite images and cloudy skies present major limitations that impede continuous estimates of ET. This study introduces a practical approach that overcomes (in part) the previous limitations by implementing machine learning techniques that are accurate and robust. The analysis was applied to the Canal B service area of the Delta Canal Company in central Utah using data from the 2009–2011 growing seasons. Actual ET was calculated by an algorithm using data from satellite images. A relevance vector machine (RVM), which is a sparse Bayesian regression, was used to build a spatial model for ET. The RVM was trained with a set of inputs consisting of vegetation indexes, crops, and weather data. ET estimated via the algorithm was used as an output. The developed RVM model provided an accurate estimation of spatial ET based on a Nash-Sutcliffe coefficient (
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      Estimation of Spatially Distributed Evapotranspiration Using Remote Sensing and a Relevance Vector Machine

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    http://yetl.yabesh.ir/yetl1/handle/yetl/73240
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    • Journal of Irrigation and Drainage Engineering

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    contributor authorRoula Bachour
    contributor authorWynn R. Walker
    contributor authorAndres M. Ticlavilca
    contributor authorMac McKee
    contributor authorInga Maslova
    date accessioned2017-05-08T22:11:47Z
    date available2017-05-08T22:11:47Z
    date copyrightAugust 2014
    date issued2014
    identifier other39368448.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/73240
    description abstractWith the development of surface energy balance analyses, remote sensing has become a spatially explicit and quantitative methodology for understanding evapotranspiration (ET), a critical requirement for water resources planning and management. Limited temporal resolution of satellite images and cloudy skies present major limitations that impede continuous estimates of ET. This study introduces a practical approach that overcomes (in part) the previous limitations by implementing machine learning techniques that are accurate and robust. The analysis was applied to the Canal B service area of the Delta Canal Company in central Utah using data from the 2009–2011 growing seasons. Actual ET was calculated by an algorithm using data from satellite images. A relevance vector machine (RVM), which is a sparse Bayesian regression, was used to build a spatial model for ET. The RVM was trained with a set of inputs consisting of vegetation indexes, crops, and weather data. ET estimated via the algorithm was used as an output. The developed RVM model provided an accurate estimation of spatial ET based on a Nash-Sutcliffe coefficient (
    publisherAmerican Society of Civil Engineers
    titleEstimation of Spatially Distributed Evapotranspiration Using Remote Sensing and a Relevance Vector Machine
    typeJournal Paper
    journal volume140
    journal issue8
    journal titleJournal of Irrigation and Drainage Engineering
    identifier doi10.1061/(ASCE)IR.1943-4774.0000754
    treeJournal of Irrigation and Drainage Engineering:;2014:;Volume ( 140 ):;issue: 008
    contenttypeFulltext
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