KaraX – Remote Sensing of Artic Sea Ice and Snow
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Project consortium: |
Finnish Meteorological Institute |
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Aalto University |
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AARI (Arctic and Antarctic Research Institute) |
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Aker Arctic Inc |
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Finnish Ministry of Transport and Communications |
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Actimar (SDag, Shtokman Development Agency) |
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Norwegian Meteorological Institute |
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Canadian Ice Service |
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Project funding: |
Tekes |
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Consortium |
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Project duration: |
1 September 2008 – 31 November 2011 |
Introduction
The shrinking of sea ice extent has been observed in the Barents, Pechora and Kara Seas, although the rate of decrease has not yet been as dramatic as in the Chukchi and Beaufort Seas. It is expected that this is a continual, or even increasing, trend due to the global warming. In the Kara Sea area the ice is mainly first-year ice, and multi-year ice is observed as transported from the higher Arctic. The ice thickness distributions vary substantially according to the location and the latitude, and the ice cover is highly dynamic and mobile. Ice conditions have a major impact on ships, navigation and other sea operations. In the Barents, Pechora and Kara Sea area new oil and gas fields will be opened in the near future and existing marine transportation will increase. There exists a great economical interest.
The goal of the KaraX project was to create high-resolution ice charts, including information on sea ice type, thickness of ice and snow depth on top of ice, for operational use in the Arctic Ocean, similar to the one already employed in the Baltic Sea. The multisource product is based on the application of a thermodynamic snow/ice model and on the optimized use of several satellite data sets including
- C-band SAR images (RADARSAT-1/2, ENVISAT)
- radiometer data (AMSR-E)
- spectrometer data (MODIS)
In the KaraX -project Aalto University was mainly responsible for pre-processing of the satellite data and creating a time series of backscattering coefficients at the selected test area and testing the identification of thin ice areas with radiometer data. We also studied backscattering of layered snow pack utilizing matrix-doubling method combined with discrete dipole approximation.
Research
We examined the incidence angle dependence of backscattering coefficient using Envisat ASAR images. The results can be utilized for the creation of time series of angle-corrected backscattering coefficient data. We selected two test areas at the Kara Sea; the first was east of Yamal Peninsula and the second was between Yamal Peninsula and Novaya Zemlya (Figure 1) and defined the angle dependence separately for level and deformed ice types.
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Δσ⁰ = –0.09–0.257Δθ, R2 = 0.82, N = 1281. |
Figure 1. Top: The first site, east of Yamal marked as black rectangle, is characteristically presenting the landfast, level ice type. Bottom: Regression analysis for level ice at the first test site. N is the number of samples.
For marine industry identification of thin ice areas is essential for the operational planning and navigation. Hence, this topic was also important for the operational sea ice product development done by the KaraX consortium. In this task thin ice areas in the Kara Sea were identified with Aqua AMSR-E radiometer data and compared to MODIS spectrometer defined ice thickness chart. We processed AMSR-E and MODIS data for winters 2008-2010 and studied the behavior of different parameters such as polarization ratio or gradient ratio of different channels compared to MODIS values. We observed that the vertical spectral gradient ratio of the channels 89 GHz and 36 GHz
GR = (TB,89V – TB,36V)/(TB,89V + TB,36V)
is sensitive to ice thickness values and we were able to find a suitable threshold value to define the thin ice areas (Figure 2). The drawback of utilizing radiometer data is the coarse resolution, as with AMSR-E the pixel is size of 12.5 x 12.5 km2. The underlying idea was to combine the radiometer defined ice thickness model with Envisat Wide Swath Mode (WSM) ASAR radar images to create a reliable ice thickness chart. This research work is still ongoing after the KaraX project was officially completed.
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Figure 2. Comparison between MODIS and AMSR-E results for identification of thin ice and open water areas. Top: AMSR-E spectral gradient; middle: MODIS defined ice thickness; and bottom: the pixels identified as thin ice from AMSR-E are set black in the MODIS image.