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25 March 2015 Ocean color measurements with the Operational Land Imager on Landsat-8: implementation and evaluation in SeaDAS
Bryan A. Franz, Sean W. Bailey, Norman Kuring, P. Jeremy Werdell
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Journal of Applied Remote Sensing, Vol. 9, Issue 1, 096070 (March 2015). https://doi.org/10.1117/1.JRS.9.096070
Abstract
The Operational Land Imager (OLI) is a multispectral radiometer hosted on the recently launched Landsat­8 satellite. OLI includes a suite of relatively narrow spectral bands at 30 m spatial resolution in the visible to shortwave infrared, which makes it a potential tool for ocean color radiometry: measurement of the reflected spectral radiance upwelling from beneath the ocean surface that carries information on the biogeochemical constituents of the upper ocean euphotic zone. To evaluate the potential of OLI to measure ocean color, processing support was implemented in Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), which is an open-source software package distributed by NASA for processing, analysis, and display of ocean remote sensing measurements from a variety of spaceborne multispectral radiometers. Here we describe the implementation of OLI processing capabilities within SeaDAS, including support for various methods of atmospheric correction to remove the effects of atmospheric scattering and absorption and retrieve the spectral remote sensing reflectance (Rrs; sr−1). The quality of the retrieved Rrs imagery will be assessed, as will the derived water column constituents, such as the concentration of the phytoplankton pigment chlorophyll a.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Bryan A. Franz, Sean W. Bailey, Norman Kuring, and P. Jeremy Werdell "Ocean color measurements with the Operational Land Imager on Landsat-8: implementation and evaluation in SeaDAS," Journal of Applied Remote Sensing 9(1), 096070 (25 March 2015). https://doi.org/10.1117/1.JRS.9.096070
Published: 25 March 2015
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KEYWORDS
Sensors
Earth observing sensors
Landsat
Atmospheric corrections
Aerosols
Signal to noise ratio
Imaging systems
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