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    Time series from hyperion to track productivity in pivot agriculture in saudi arabia

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    Type
    Conference Paper
    Authors
    Houborg, Rasmus cc
    McCabe, Matthew cc
    Angel, Yoseline cc
    Middleton, Elizabeth M.
    KAUST Department
    Biological and Environmental Sciences and Engineering (BESE) Division
    Environmental Science and Engineering Program
    Water Desalination and Reuse Research Center (WDRC)
    Date
    2017-12-13
    Online Publication Date
    2017-12-13
    Print Publication Date
    2017-07
    Permanent link to this record
    http://hdl.handle.net/10754/626592
    
    Metadata
    Show full item record
    Abstract
    The hyperspectral satellite sensing capacity is expected to increase substantially in the near future with the planned deployment of hyperspectral systems by both space agencies and commercial companies. These enhanced observational resources will offer new and improved ways to monitor the dynamics and characteristics of terrestrial ecosystems. This study investigates the utility of time series of hyperspectral imagery, acquired by Hyperion onboard EO-1, for quantifying variations in canopy chlorophyll ($Chl_{c}$), plant productivity, and yield over an intensive farming area in the desert of Saudi Arabia. $Chl_{c}$ is estimated on the basis of predictive multi-variate empirical models established via a machine learning approach using a training dataset of in-situ measured target variables and explanatory hyperspectral indices. Resulting time series of $Chl_{c}$ are translated into Gross Primary Productivity (GPP) and Yield based on semi-empirical relationships, and evaluated against ground-based observations. Results indicate significant benefit in utilizing the full suite of hyperspectral indices over multi-spectral indices constructible from Landsat-8 and Sentinel-2.
    Citation
    Houborg R, McCabe MF, Angel Y, Middleton EM (2017) Time series from hyperion to track productivity in pivot agriculture in saudi arabia. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Available: http://dx.doi.org/10.1109/IGARSS.2017.8127641.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
    DOI
    10.1109/IGARSS.2017.8127641
    Additional Links
    http://ieeexplore.ieee.org/document/8127641/
    ae974a485f413a2113503eed53cd6c53
    10.1109/IGARSS.2017.8127641
    Scopus Count
    Collections
    Conference Papers; Biological and Environmental Science and Engineering (BESE) Division; Environmental Science and Engineering Program; Water Desalination and Reuse Research Center (WDRC)

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