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Band shifting for ocean color multi-spectral reflectance data
Author(s) -
Fréderic Mélin,
G. Sclep
Publication year - 2015
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.23.002262
Subject(s) - spectral bands , optics , remote sensing , radiative transfer , wavelength , ocean color , satellite , approximation error , spectral envelope , atmospheric radiative transfer codes , physics , computer science , geology , algorithm , astronomy
An approach to perform band shifting applied to multi-spectral ocean remote sensing reflectance RRS values in the visible spectral range is presented. The band-shifting scheme aims at expressing RRS at a wavelength not originally part of the spectrum from data at neighboring bands. The scheme relies on the determination of inherent optical properties (IOPs) by a bio-optical model, the calculation of the IOPs at the target wavelength using the spectral shapes assumed for each IOP, and the operation of the bio-optical model in forward mode to express RRS at the target wavelength. The performance of the band-shifting scheme applied to bands typical of satellite missions is assessed with hyper-spectral data sets obtained from radiative transfer simulations or from field measurements. The relative error ε on the conversion factors from 488 to 490 nm is mostly within 1%. Analogous results are obtained for conversions in the red spectral domain (665, 667 and 670 nm) only for synthetic data sets. The range of ε for conversions between green bands (547, 555 and 560 nm) is within 2% to 5% depending on the data set considered. Similar results are obtained when RRS values are computed at 510 nm from data at 488 and 531 nm. In the case of the assessment with simulated data, all band-shifting operations are characterized by an ε range within 2% for all conversions when the concentration of chlorophyll-a is lower than 1 mg m(-3). Applied to satellite data, the band-shifting scheme noticeably improves the agreement between RRS data from different missions.

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