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    Monte carlo–based quantification of uncertainties in determining ocean remote sensing reflectance from underwater fixed-depth radiometry measurements

    79248.pdf (4.953Mb)
    Access Status
    Open access
    Authors
    Białek, A.
    Vellucci, V.
    Gentil, B.
    Antoine, David
    Gorroño, J.
    Fox, N.
    Underwood, C.
    Date
    2020
    Type
    Journal Article
    
    Metadata
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    Citation
    Białek, A. and Vellucci, V. and Gentil, B. and Antoine, D. and Gorroño, J. and Fox, N. and Underwood, C. 2020. Monte carlo–based quantification of uncertainties in determining ocean remote sensing reflectance from underwater fixed-depth radiometry measurements. Journal of Atmospheric and Oceanic Technology. 37 (2): pp. 177-196.
    Source Title
    Journal of Atmospheric and Oceanic Technology
    DOI
    10.1175/JTECH-D-19-0049.1
    ISSN
    0739-0572
    Faculty
    Faculty of Science and Engineering
    School
    School of Earth and Planetary Sciences (EPS)
    URI
    http://hdl.handle.net/20.500.11937/79146
    Collection
    • Curtin Research Publications
    Abstract

    A new framework that enables evaluation of the in situ ocean color radiometry measurement uncertainty is presented. The study was conducted on the multispectral data from a permanent mooring deployed in clear open ocean water. The uncertainty is evaluated for each component of the measurement equation and data processing step that leads to deriving the remote sensing reflectance. The Monte Carlo method was selected to handle the data complexity such as correlation and nonlinearity in an efficient manner. The results are presented for a prescreened dataset that is suitable for system vicarious calibration applications. The framework provides uncertainty value per measurement taking into consideration environmental conditions present during acquisition. A summary value is calculated from the statistics of the individual uncertainties per each spectral channel. This summary value is below 4% (k 5 1) for the blue and green spectral range. For the red spectral channels, the summary uncertainty value increases to approximately 5%. The presented method helps to understand the significance of various uncertainty components and to provide a way of identifying major contributors. This can be used for efficient system performance improvement in the future.

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