1 / 12

Optimization of a Semi-analytical Ocean Color Algorithm for Optically-Complex (Case II) Waters

This presentation discusses the optimization of a semi-analytical algorithm for analyzing ocean color in optically complex (Case II) waters. It introduces the importance of understanding ocean color remote sensing, differentiating Case II waters, and the local tuning of the GSM Semi-Analytical Algorithm. The impact of water constituents, such as sediments and bottom effects, on optical properties is examined. The research emphasizes the significance of accurate ocean color models for global-scale applications in environmental monitoring and management.

roth-pittman
Télécharger la présentation

Optimization of a Semi-analytical Ocean Color Algorithm for Optically-Complex (Case II) Waters

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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 UCSB

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

  3. What is Ocean Color? http://www.gamonline.com/catalog/colortheory/spectrum.gif

  4. SeaWiFS Daily Coverage http://seawifs.gsfc.nasa.gov/SEAWIFS/SEASTAR/seawifs_daily_coverage.mpeg

  5. 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.

  6. Why do we care?

  7. Toole and Siegel 2001 • 1996-1999 (N = 251) • Outliers indicate Sediments • Mean and Std curves are not similar in shape, therefore • Constituents’ concentrations vary independently.

  8. The GSM Semi-Analytical Model

  9. The GSM Semi-Analytical Model

  10. Local Tuning

  11. 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, 10909–10924. 1988. • Maritorena, S., D. Siegel, A. Peterson. "Optimization of a semi-analytical ocean color model for global-scale applications,” Applied Optics 41.15 (2002): 2705-2714. • "Remote Sensing of Ocean Colour in Coastal and Other Optically-Complex Waters." Reports of the International Ocean-Colour Coordinating Group Ed. Sathyendranath, Shubha 2000. • 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): 26985-27000.

More Related