Monte Carlo-Based Quantification of Uncertainties in Determining Ocean Remote Sensing Reflectance from Underwater Fixed-Depth Radiometry Measurements
|Author(s)||Bialek Agnieszka1, Vellucci Vincenzo2, Gentili Bernard3, Antoine David3, 4, Gorrono Javier1, Fox Nigel1, Underwood Craig5|
|Affiliation(s)||1 : Natl Phys Lab, Teddington, Middx, England.
2 : Sorbonne Univ, CNRS, Inst Mer Villefranche, IMEV, Villefranche Sur Mer, France.
3 : UPMC Univ Paris 06, Sorbonne Univ, INSU CNRS, Lab Oceanog Villefranche, Villefranche Sur Mer, France.
4 : Curtin Univ, Sch Earth & Planetary Sci, Remote Sensing & Satellite Res Grp, Perth, WA, Australia.
5 : Univ Surrey, Surrey Space Ctr, Guildford, England.
|Source||Journal Of Atmospheric And Oceanic Technology (0739-0572) (Amer Meteorological Soc), 2020-02 , Vol. 37 , N. 2 , P. 177-196|
|WOS© Times Cited||4|
|Keyword(s)||Ocean, In situ oceanic observations, Quality assurance, control, Error analysis|
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 = 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.