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Impact of Sea Ice on Bio-Optical Properties in High Latitudes

Explore how sea ice affects the retrieval of water surface bio-optical properties in high latitudes and its implications for remote sensing. Study includes modeling, observations, and simulations of sea ice contamination. Results show challenges in chlorophyll estimation and the influence of adjacency effect. Suggestions for algorithm improvement and future research directions are discussed.

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Impact of Sea Ice on Bio-Optical Properties in High Latitudes

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  1. Ocean Color remote sensing at high latitudes: Impact of sea ice on the retrieval of bio-optical properties of water surface Simon Bélanger1 Jens Ehn Marcel Babin 1 Laboratoire d’Océanographie de Villefranche-sur-Mer, France

  2. Outline • Introduction: Arctic and the global warming • Context of the study • Modeling and observations of sea ice contamination • Summary and perspectives

  3. The Arctic Ocean and the Global Warming - I ~20% reduction in the last 25 years! From Arctic Climate Impact Assessment (ACIA) report, 2005

  4. The Arctic Ocean and the Global Warming - II www.acia.uaf.edu (2005)

  5. Strong influence of riverine discharge of CDOM and detritus Canadian Arctic Shelf Exchange Study (CASES) Summer Chlorophyll as seen by SeaWiFS • Presence of sea ice

  6. 2. Sub-pixel contamination 1. Adjacency effect Sea ice:a limitation at High Latitude To quantify the error introduced by sea ice on the retrieval of: • Water-leaving reflectance, rw • Chlorophyll a concentration, CHL

  7. Early Season CASES 1. The adjacency effect

  8. 1. The adjacency effect • Simulation of rTOA using 6S : • Environment is fresh snow with a spectrally neutral albedo of ~94% • Target is a high Chl water • Radius of the open water area from 0 to 30 km • Two concentrations of maritime aerosols • Application of Atmospheric Correction and blue-to-green ratio Chlorophyll (e.g. SeaWiFS) • Can AC remove part of adjacency effect?

  9. Results:Adjacency effect

  10. Results:Adjacency effect

  11. MERIS observations

  12. SeaWiFS observations

  13. Adjacency effect? From Arrigo & Van Dijken, GRL, 2004

  14. 2. Sub-pixel contamination Sea ice:a limitation at High Latitude

  15. 2. Sub-pixel contamination

  16. MOMO RT code Maritime aerosols RH=50%, 90% ta(560)=0.03, 0.1 2. Sub-pixel contamination Simulations of rTOA s= the fraction of a pixel occupied by sea ice

  17. Results: Sub-pixel contamination BLUE GREEN • Negative bias on [rw]N • Effect more pronounced in the blue • Vary as function of ice type: more important with melting snow and ice

  18. Results: Sub-pixel contamination Effect on chlorophyll concentration

  19. Case2_S Case2_Anom MERIS observation

  20. SeaWiFS observation Late summer

  21. Summary • Adjacency effect enhances the water-leaving reflectance toward the shorter wavelength, leading to an underestimation of Chlorophyll • Sub-pixel contamination by sea ice depends on the type and age of sea ice. It tends to be seen as an aerosol resulting in overcorrection in the blue and consequently, an overestimation of the Chlorophyll

  22. Implications and Perspectives • Actual algorithms do not detect and remove the adjacency effect • Used of 400-450nm region for flagging? • e.g. rw(412)<rw(443) • Sub-pixel contamination raised the Turbid flag • Can we distinguish with real Turbid waters? • Cal/Val activities • Data fusion? • Spatio-temporal resolution issue (Passive Microwave, SAR, High res. Optical, SPOT, Landsat)

  23. Conclusions • A flag for adjacency effect is needed and can be develop using the simple spectral test in the blue region of the spectra • Sub-pixel contamination is already flagged by turbid water test • Sea ice does not appear to be the major limitation for Ocean Color in high latitudes

  24. Thank you Acknowledgements: Drs Pierre Larouche, Dave Barber, Louis Fortier,Fabrizio d’Ortenzio, Yannick Huot, and CCGS Amundsen crew. Fond Québécois pour la Recherche sur la Nature et les Technologies (FQRNT).

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