Versatile time-dependent spatial distribution model of sun glint for satellite-based ocean imaging

Guanhua Zhou; Wujian Xu; Chunyue Niu; Kai Zhang; Zhongqi Ma; Jiwen Wang; Yue Zhang

doi:10.1117/1.JRS.11.016020

3 February 2017 Versatile time-dependent spatial distribution model of sun glint for satellite-based ocean imaging

Guanhua Zhou, Wujian Xu, Chunyue Niu, Kai Zhang, Zhongqi Ma, Jiwen Wang, Yue Zhang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 1, 016020 (February 2017). https://doi.org/10.1117/1.JRS.11.016020

Abstract

We propose a versatile model to describe the time-dependent spatial distribution of sun glint areas in satellite-based wave water imaging. This model can be used to identify whether the imaging is affected by sun glint and how strong the glint is. The observing geometry is calculated using an accurate orbit prediction method. The Cox-Munk model is used to analyze the bidirectional reflectance of wave water surface under various conditions. The effects of whitecaps and the reflectance emerging from the sea water have been considered. Using the moderate resolution atmospheric transmission radiative transfer model, we are able to effectively calculate the sun glint distribution at the top of the atmosphere. By comparing the modeled data with the medium resolution imaging spectrometer image and Feng Yun 2E (FY-2E) image, we have proven that the time-dependent spatial distribution of sun glint areas can be effectively predicted. In addition, the main factors in determining sun glint distribution and the temporal variation rules of sun glint have been discussed. Our model can be used to design satellite orbits and should also be valuable in either eliminating sun glint or making use of it.

Citation Download Citation

Guanhua Zhou, Wujian Xu, Chunyue Niu, Kai Zhang, Zhongqi Ma, Jiwen Wang, and Yue Zhang "Versatile time-dependent spatial distribution model of sun glint for satellite-based ocean imaging," Journal of Applied Remote Sensing 11(1), 016020 (3 February 2017). https://doi.org/10.1117/1.JRS.11.016020

Received: 18 March 2016; Accepted: 10 January 2017; Published: 3 February 2017

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