Optimization of a Semi- analytical Ocean Color Algorithm for Optically-Complex (Case II) Waters Tihomir Kostadinov 11.03.2003 15:17 Geog 200B, Dr. Clarke.

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Presentation transcript:

Optimization of a Semi- analytical Ocean Color Algorithm for Optically-Complex (Case II) Waters Tihomir Kostadinov :17 Geog 200B, Dr. Clarke UCSB

Structure of Talk Introduction to Ocean Color Remote Sensing What are Case II waters? Why we care The GSM Semi-Analytical Algorithm Local Tuning

What is Ocean Color?

SeaWiFS Daily Coverage

What are Case II Ocean Waters? Based on the relative contribution of each of three components to an optical property, for example a(440nm). This criterion is wavelength dependent. Excludes pure water contribution in classification. Bottom effect can have influence in optically shallow waters. Optical Property Phytoplankton (P) Yellow Substances (Y) Suspended Materials (S)

Why do we care?

Toole and Siegel (N = 251) Outliers indicate Sediments Mean and Std curves are not similar in shape, therefore Constituents’ concentrations vary independently.

The GSM Semi-Analytical Model

Local Tuning

Acknowledgements & References NASA UCSB Dr. David Siegel, ICESS, UCSB Gordon, H. R., O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, and D. K. Clark, “A semianalytic radiance model of ocean color,” J. Geophys. Res. 93 D9, 10909– Maritorena, S., D. Siegel, A. Peterson. "Optimization of a semi-analytical ocean color model for global-scale applications,” Applied Optics (2002): "Remote Sensing of Ocean Colour in Coastal and Other Optically-Complex Waters." Reports of the International Ocean-Colour Coordinating Group Ed. Sathyendranath, Shubha Toole, Dierdre A. and Siegel, David A.. "Modes and mechanisms of ocean color variability in the Santa Barbara Channel." Journal of Geophysical Research 160.C11 (2001):