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Estimating sea ice motion from synthetic aperture radar (SAR) imagery at C-band is the most reliable approach because of its high spatial resolution and ever increasing temporal resolution given the multiple current and upcoming SAR platforms. However, there is still uncertainty in SAR derived sea ice motion depending on the ice type and its thermodynamic state. There have been suggestions (mostly theoretical) that use of L-band SAR and its inherent longer wavelength (15–30 cm) and subsequent increased penetration capability could be beneficial for estimating sea ice motion, especially during the melt season. Here, we estimate and analyze sea ice motion for 9 pairs of C- and L-band SAR imagery from RADARSAT-2, PALSAR-1 and PALSAR-2 located in the Canadian Arctic over a variety of sea ice types at different thermodynamic states. Results show that the increased signal penetration of L-band SAR into multi-year ice (MYI) during the melt season facilitates the detection of more motion vectors with stronger cross-correlation coefficients compared to C-band SAR. Over newly formed ice and dry first-year ice, the reduced sensitivity to surface scattering and richer texture from L-band SAR imagery facilitates the detection of more motion vectors with stronger cross-correlation coefficients compared to C-band SAR. Over dry MYI, L-band provided stronger cross-correlation coefficients but C-band detected more motion vectors with a more representative spatial distribution. With Arctic sea ice continuing shift from a multi-year to first-year dominated icescape, coupled with an increasing melt season length, L-band SAR's ability to provide improved sea ice motion estimates during both the melt and freeze-up time periods could prove even more useful in the coming decades. [ABSTRACT FROM AUTHOR]

Our ability to simulate and understand oceanic conditions of the Gulf of St Lawrence (GSL) has significantly increased in the last decade with the development of regional ocean models combining scientific knowledge, oceanographic data, and computing techniques. This has created opportunities for their integration with meteorological forecast models where the importance of sea ice-ocean dynamics in air-sea exchanges was recently demonstrated. In order to produce accurate initial conditions for coupled weather forecasts, a GSL ice-ocean 'pseudo' analysis (IOPA) system has been developed and implemented operationally at the Canadian Meteorological Centre (CMC). Based on prognostic ice-ocean model solutions the IOPA system has sufficient skill to produce realistic daily fields without flux correction or data assimilation of temperature and salinity. Using only a simple assimilation method for sea-ice data, daily oceanic fields are obtained and can be used to initialize ice-ocean or coupled atmospheric forecasting systems. A 3-year retrospective evaluation of this operational method is presented focusing on sea-surface temperature (SST) and sea-ice conditions. Model estimates are compared with in situ and satellite-derived data. Results show that the IOPA system has an accuracy equivalent to or better than the current operational SST and ice analyses produced at the CMC. Together with preceding modelling efforts, this work lays the foundation for the first state-of-the-art GSL ocean analysis and forecast systems combining ocean modelling and real-time three-dimensional data assimilation. Coupled atmosphere-ice-ocean forecasts produced with the same ice-ocean model and initialized with the IOPA system are presented in a companion paper. Copyright © 2012 Crown in the right of Canada. Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

The article discusses research done on the relationship between the divergent movements of the Pacific walrus Odobenus rosmarus divergens and sea ice in the northern Bering Sea. Concerns related to the conservation of the Pacific walrus resulted from the possibility of the occurrence of sea ice reduction in the Arctic region by mid-century. Linear mixed-effects models were used for the analysis of several factors that included the ice floe movement vector, the walrus movement vector and the distance between such ice floes and walruses.

During a ship voyage from Tasmania to Antarctica in summer 2000/01, radiative and meteorological measurements were continuously made, from which the surface energy budget was calculated. Sea conditions throughout the voyage ranged from open water to broken pack and finally to snow-covered unbroken sea ice in McMurdo Sound. The global radiation increased on average during the trip (to higher latitudes) as we travelled poleward. The net radiation, which was positive (toward the surface) on average, decreased however, mostly due to the increase in surface albedo. For open water, most of the net radiation is used for evapouration (61%), while for broken sea-ice conditions, nearly all energy is used for melting of the sea ice or heating of the ocean (96%). For unbroken snow-covered sea ice, the net radiation lies close to zero, due to the high surface albedo, which reached a mean value of 0.81. The sensible heat flux becomes the largest heat source and nearly all the energy is used for warming of the surface. Finally, a Radarsat image, on which the ship track was visible, was used to compare the ship observations with satellite derived ice types. [ABSTRACT FROM AUTHOR]

A multisensor data fusion algorithm based on a multilayer neural network is presented for sea ice classification in the winter period. The algorithm uses European Remote Sensing (ERS), RADARSAT synthetic aperture radar (SAR), and low-resolution television camera images and image texture features. Based on a set of in situ observations made at the Kara Sea, a neural network is trained, and its structure is optimized using a pruning method. The performance of the algorithm with different combinations of input features (sensors) is assessed and compared with the performance of a linear discriminant analysis (LDA)-based algorithm. We show that for both algorithms a substantial improvement can be gained by fusion of the three different types of data (91.2% for the neural network) as compared with single-source ERS (66.0%) and RADARSAT (70.7%) SAR image classification. Incorporation of texture increases classification accuracy. This positive effect of texture becomes weaker with increasing number of sensors (from 8.4 to 6.4 percent points for the use of two and three sensors, respectively). In view of the short training time and smaller number of adjustable parameters, this result suggests that semiparametric classification methods can be considered as a good alternative to the neural networks and traditional parametric statistical classifiers applied for the sea ice classification. [ABSTRACT FROM AUTHOR]

The Special Sensor Microwave Imager (SSM/I) microwave satellite radiometer and its predecessor SMMR are primary sources of information for global sea ice and climate studies. However, comparisons of SSM/I, Landsat, AVHRR, and ERS-1 synthetic aperture radar (SAR) have shown substantial seasonal and regional differences in their estimates of sea ice concentration. To evaluate these differences, we compared SSM/I estimates of sea ice coverage derived with the NASA Team and Bootstrap algorithms to estimates made using RADARSAT, and OKEAN-01 satellite sensor data. The study area included the Barents Sea, Kara Sea, Laptev Sea, and adjacent parts of the Arctic Ocean, during October 1995 through October 1999. Ice concentration estimates from spatially and temporally near-coincident imagery were calculated using independent algorithms for each sensor type. The OKEAN algorithm implemented the satellite''s two-channel active (radar) and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter. The RADARSAT algorithm utilized a segmentation approach of the measured radar backscatter, and the SSM/I ice concentrations were derived at National Snow and Ice Data Center (NSIDC) using the NASA Team and Bootstrap algorithms. Seasonal and monthly differences between SSM/I, OKEAN, and RADARSAT ice concentrations were calculated and compared. Overall, total sea ice concentration estimates derived independently from near-coincident RADARSAT, OKEAN-01, and SSM/I satellite imagery demonstrated mean differences of less than 5.5% (S.D.<9.5%) during the winter period. Differences between the SSM/I NASA Team and the SSM/I Bootstrap concentrations were no more than 3.1% (S.D.<5.4%) during this period. RADARSAT and OKEAN-01 data both yielded higher total ice concentrations than the NASA Team and the Bootstrap algorithms. The Bootstrap algorithm yielded higher total ice concentrations than the NASA Team algorithm. Total ice concentrations derived from OKEAN-01 and SSM/I satellite imagery were highly correlated during winter, spring, and fall, with mean differences of less than 8.1% (S.D.<15%) for the NASA Team algorithm, and less than 2.8% (S.D.<13.8%) for the Bootstrap algorithm. Respective differences between SSM/I NASA Team and SSM/I Bootstrap total concentrations were less than 5.3% (S.D.<6.9%). Monthly mean differences between SSM/I and OKEAN differed annually by less than 6%, with smaller differences primarily in winter. The NASA Team and Bootstrap algorithms underestimated the total sea ice concentrations relative to the RADARSAT ScanSAR no more than 3.0% (S.D.<9%) and 1.2% (S.D.<7.5%) during cold months, and no more than 12% and 7% during summer, respectively. ScanSAR tended to estimate higher ice concentrations for ice concentrations greater than 50%, when compared to SSM/I during all months. ScanSAR underestimated total sea ice concentration by 2% compared to the OKEAN-01 algorithm during cold months, and gave an overestimation by 2% during spring and summer months. Total NASA Team and Bootstrap sea ice concentration estimates derived from coincident SSM/I and OKEAN-01 data demonstrated mean differences of no more than 5.3% (S.D.<7%), 3.1% (S.D.<5.5%), 2.0% (S.D.<5.5%), and 7.3% (S.D.<10%) for fall, winter, spring, and summer periods, respectively. Large disagreements were observed between the OKEAN and NASA Team results in spring and summer for estimates of the first-year (FY) and multiyear (MY) age classes. The OKEAN-01 algorithm and data tended to estimate, on average, lower concentrations of young or FY ice and higher concentrations of total and MY ice for all months and seasons. Our results contribute to the growing body of documentation about the levels of disparity obtained when seasonal sea ice concentrations are estimated using various types of satellite data and algorithms. [Copyright &y& Elsevier]