Monte Carlo–Based Quantification of Uncertainties in Determining Ocean Remote Sensing Reflectance from Underwater Fixed-Depth Radiometry Measurements.
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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 uncertaint.....
JournalJournal of Atmospheric and Oceanic Technology
Sustainable Development Goals (SDG)14.a
Maturity LevelPilot or Demonstrated
Spatial CoverageSouth Pacific Ocean
CitationBiałek, A., Vellucci, V., Gentil, B., Antoine, D., Gorroño, J., 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, pp.177–196. DOI: https://doi.org/10.1175/JTECH-D-19-0049.1
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