FN Archimer Export Format
PT J
TI Monte Carlo-Based Quantification of Uncertainties in Determining Ocean Remote Sensing Reflectance from Underwater Fixed-Depth Radiometry Measurements
BT 
AF BIALEK, Agnieszka
   VELLUCCI, Vincenzo
   GENTILI, Bernard
   ANTOINE, David
   GORRONO, Javier
   FOX, Nigel
   UNDERWOOD, Craig
AS 1:1;2:2;3:3;4:3,4;5:1;6:1;7:5;
FF 1:;2:;3:;4:;5:;6:;7:;
C1 Natl Phys Lab, Teddington, Middx, England.
   Sorbonne Univ, CNRS, Inst Mer Villefranche, IMEV, Villefranche Sur Mer, France.
   UPMC Univ Paris 06, Sorbonne Univ, INSU CNRS, Lab Oceanog Villefranche, Villefranche Sur Mer, France.
   Curtin Univ, Sch Earth & Planetary Sci, Remote Sensing & Satellite Res Grp, Perth, WA, Australia.
   Univ Surrey, Surrey Space Ctr, Guildford, England.
C2 NATL PHYS LAB, UK
   UNIV PARIS 06, FRANCE
   UNIV PARIS 06, FRANCE
   UNIV CURTIN, AUSTRALIA
   UNIV SURREY, UK
IF 2.075
TC 11
UR https://archimer.ifremer.fr/doc/00640/75202/75334.pdf
LA English
DT Article
CR BOUSSOLE
DE ;Ocean;In situ oceanic observations;Quality assurance;control;Error analysis
AB 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. 
PY 2020
PD FEB
SO Journal Of Atmospheric And Oceanic Technology
SN 0739-0572
PU Amer Meteorological Soc
VL 37
IS 2
UT 000513216100002
BP 177
EP 196
DI 10.1175/JTECH-D-19-0049.1
ID 75202
ER

EF