Optimization of Cox and Munk sun-glint model using ADEOS/II GLI data and SeaWinds data

Liping Li; Hajime Fukushima; Kazunori Suzuki; Naoya Suzuki

doi:10.1117/12.732779

5 October 2007 Optimization of Cox and Munk sun-glint model using ADEOS/II GLI data and SeaWinds data

Liping Li, Hajime Fukushima, Kazunori Suzuki, Naoya Suzuki

Proceedings Volume 6680, Coastal Ocean Remote Sensing; 668006 (2007) https://doi.org/10.1117/12.732779
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

Abstract

Effect of sun glint reflectance to the measurement of ocean color sensors is generally observed, even some of the sensors are equipped with tilt mechanisms to avoid sun glint. The traditional Cox-Munk model has been widely used to estimate the sun glint reflectance together with objective analysis wind data. To reevaluate the sun glint model at the condition of satellite viewing geometry, ADEOS-II/GLI data are analyzed jointly with SeaWinds microwave scatterometer data which provides the concurrent wind data with the ocean color observation. The probability density of the wave slope is then estimated using GLI data of 865nm band after carefully masking the cloud-contaminated pixels and removing the aerosol effects, the latter being estimated from the SeaWiFS Level 3 daily aerosol data set. The satellite-retrieved probability density functions are then analyzed as a function of wind speed and wave slope angle, by fitting the satellite retrieved probability density of slope to the anisotropic model. Modified model parameters is given and applied into GLI processing. Results are compared with the original anisotropic Cox-Munk model, as well as other published results. Differences in the slope distributions are discussed, which is mostly found at very weak speed or very strong wind speed.

Citation Download Citation

Liping Li, Hajime Fukushima, Kazunori Suzuki, and Naoya Suzuki "Optimization of Cox and Munk sun-glint model using ADEOS/II GLI data and SeaWinds data", Proc. SPIE 6680, Coastal Ocean Remote Sensing, 668006 (5 October 2007); https://doi.org/10.1117/12.732779

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