Ocean Color remote sensing at Ocean Color remote sensing at high latitudes: high latitudes:

Impact of sea ice on the retrieval of bio-Impact of sea ice on the retrieval of bio-optical properties of water surfaceoptical properties of water surface

Simon Bélanger1

Jens Ehn

Marcel Babin 1 Laboratoire d’Océanographie de Villefranche-sur-Mer, France

• Introduction: Arctic and the global warming

• Context of the study

• Modeling and observations of sea ice contamination

• Summary and perspectives

Outline

The Arctic Ocean and the Global Warming - I

From Arctic Climate Impact Assessment (ACIA) report, 2005

~20% reduction in the last 25 years!

The Arctic Ocean and the Global Warming - II

www.acia.uaf.edu (2005)

Summer Chlorophyll as seen by SeaWiFS

•Strong influence of riverine discharge of CDOM and detritus

•Presence of sea ice

Canadian ArcticShelf ExchangeStudy (CASES)

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

1. The adjacency effect

Early Season

CASES

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?

Results: Adjacency effect

Results: Adjacency effect

MERIS observations

SeaWiFS observations

From Arrigo & Van Dijken, GRL, 2004

Adjacency effect?

2. Sub-pixel contamination

Sea ice: a limitation at High Latitude

2. Sub-pixel contamination

2. Sub-pixel contamination

Simulations of TOA

icewraarTOA t )1(

= the fraction of a pixel occupied by sea ice

MOMO RT code

Maritime aerosols RH=50%, 90%a(560)=0.03, 0.1

Results: Sub-pixel contamination

BLUE GREEN•Negative bias on [w]N

•Effect more pronounced in the blue•Vary as function of ice type: more important with melting snow and ice

Results: Sub-pixel contamination

Effect on chlorophyll concentration

Case2_S

MERIS observation

Case2_Anom

SeaWiFS observation

Late summer

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

Implications and Perspectives

– Actual algorithms do not detect and remove the adjacency effect

• Used of 400-450nm 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)

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

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).