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Sensors 2012, 12(9), 12545-12561; doi:10.3390/s120912545

Sunglint Detection for Unmanned and Automated Platforms

1,* , 1
1 Institute for Chemistry and Biology of the Marine Environment—Terramare, Carl von Ossietzky University of Oldenburg, Schleusenstraβe 1, Wilhelmshaven 26382, Germany 2 Royal Netherlands Institute for Sea Research, Physical Oceanography, Marine Optics & Remote Sensing, P.O. Box 59, Den Burg, Texel 1790AB, The Netherlands
* Author to whom correspondence should be addressed.
Received: 14 July 2012 / Revised: 20 August 2012 / Accepted: 29 August 2012 / Published: 13 September 2012
(This article belongs to the Section Remote Sensors)
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We present an empirical quality control protocol for above-water radiometric sampling focussing on identifying sunglint situations. Using hyperspectral radiometers, measurements were taken on an automated and unmanned seaborne platform in northwest European shelf seas. In parallel, a camera system was used to capture sea surface and sky images of the investigated points. The quality control consists of meteorological flags, to mask dusk, dawn, precipitation and low light conditions, utilizing incoming solar irradiance (ES) spectra. Using 629 from a total of 3,121 spectral measurements that passed the test conditions of the meteorological flagging, a new sunglint flag was developed. To predict sunglint conspicuous in the simultaneously available sea surface images a sunglint image detection algorithm was developed and implemented. Applying this algorithm, two sets of data, one with (having too much or detectable white pixels or sunglint) and one without sunglint (having least visible/detectable white pixel or sunglint), were derived. To identify the most effective sunglint flagging criteria we evaluated the spectral characteristics of these two data sets using water leaving radiance (LW) and remote sensing reflectance (RRS). Spectral conditions satisfying ‘mean LW (700–950 nm) < 2 mW∙m−2∙nm−1∙Sr−1’ or alternatively ‘minimum RRS (700–950 nm) < 0.010 Sr−1’, mask most measurements affected by sunglint, providing an efficient empirical flagging of sunglint in automated quality control.
Keywords: sunglint; empirical quality control; ocean colour; coastal and shelf seas; hyperspectral sensing sunglint; empirical quality control; ocean colour; coastal and shelf seas; hyperspectral sensing
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Garaba, S.P.; Schulz, J.; Wernand, M.R.; Zielinski, O. Sunglint Detection for Unmanned and Automated Platforms. Sensors 2012, 12, 12545-12561.

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