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Ocean Color remote sensing at high latitudes: Impact of sea ice on the retrieval of bio- optical properties of water surface Simon Bélanger 1 Jens Ehn.

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Presentation on theme: "Ocean Color remote sensing at high latitudes: Impact of sea ice on the retrieval of bio- optical properties of water surface Simon Bélanger 1 Jens Ehn."— Presentation transcript:

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

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

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

4 The Arctic Ocean and the Global Warming - II (2005)

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

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,  w –Chlorophyll a concentration, CHL

7 1. The adjacency effect Early Season CASES

8 1. The adjacency effect Simulation of  TOA 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

11 MERIS observations

12 SeaWiFS observations

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

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

15 2. Sub-pixel contamination

16

17 Simulations of  TOA   = the fraction of a pixel occupied by sea ice MOMO RT code Maritime aerosols RH=50%, 90%  a (560)=0.03, 0.1

18 Results: Sub-pixel contamination BLUEGREEN Negative bias on [  w ] N Effect more pronounced in the blue Vary as function of ice type: more important with melting snow and ice

19 Results: Sub-pixel contamination Effect on chlorophyll concentration

20 Case2_S MERIS observation Case2_Anom

21 SeaWiFS observation Late summer

22 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

23 Implications and Perspectives –Actual algorithms do not detect and remove the adjacency effect Used of nm region for flagging? –e.g.  w (412)<  w (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)

24 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

25 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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