Sunglint Detection for Unmanned and Automated Platforms

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 (E-S) 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 (L-W) and remote sensing reflectance (R-RS). Spectral conditions satisfying 'mean L-W (700-950 nm) < 2 mW.m(-2).nm(-1).Sr- 1' or alternatively 'minimum R-RS (700-950 nm) < 0.010 Sr-1', mask most measurements affected by sunglint, providing an efficient empirical flagging of sunglint in automated quality control.