scholarly journals A comprehensive view of Kara Sea polynya dynamics, sea-ice compactness and export from model and remote sensing data

2005 ◽  
Vol 32 (15) ◽  
Author(s):  
S. Kern
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Remote Sensing Data ◽  
Kara Sea ◽  
Sensing Data ◽  
Comprehensive View

scholarly journals Data assimilation in sea-ice monitoring

2000 ◽  
Vol 31 ◽  
pp. 327-332 ◽  
Author(s):  
Ronald L. S. Weaver ◽  
Konrad Steffen ◽  
John Heinrichs ◽  
James A. Maslanik ◽  
Gregory M. Flato
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Sea Ice ◽  
Global Climate ◽  
Remote Sensing Data ◽  
The Arctic ◽  
Physical Processes ◽  
Error Statistics ◽  
Sensing Data ◽  
Ice Model

AbstractThe detection of small changes in concentration or thickness in the Arctic or Antarctic ice cover is an important topic in the current global-climate-change debate. Change detection using satellite data alone requires rigorous error analysis for their derived ice products, including inter-satellite validation for long time series. All models of physical processes are only approximations, and the best models of complicated physical processes have errors and uncertainties. A promising approach is data assimilation, combining model, in situ data and satellite remote-sensing data. Sea-ice monitoring from satellite, ice-model estimates, and the potential benefit of combining the two are discussed in some detail. In a case-study we demonstrate how the sea-ice backscatter for the Beaufort Sea region was derived using a backscattering model in combination with an ice model. We conclude that, for data assimilation, the first steps include the use of simple models, moving, with success at this level, to progressively more complex models. We also recommend reconfiguring the current remote-sensing data to include precise time tags with each pixel. For example, the current Special Sensor Microwave Imager data might be reissued in a time-tagged orbital (or gridded) format as opposed to the currently available daily averaged gridded data. Finally, error statistics and quality-control information also need to be readily available in a form useful for assimilation. The effectiveness of data-assimilation techniques is directly linked to the availability of data error statistics.

scholarly journals Snow microstructure on sea ice: Importance for remote sensing applications

2021 ◽  
Author(s):  
Amy R. Macfarlane ◽  
Stefanie Arndt ◽  
Ruzica Dadic ◽  
Carolina Gabarró ◽  
Bonnie Light ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Remote Sensing Data ◽  
High Arctic ◽  
First Year ◽  
Sensing Data ◽  
Snow Properties ◽  
Multiyear Ice ◽  
Snow Samples

<p>Snow plays a key role in interpreting satellite remote sensing data from both active and passive sensors in the high Arctic and therefore impacts retrieved sea ice variables from these systems ( e.g., sea ice extent, thickness and age). Because there is high spatial and temporal variability in snow properties, this porous layer adds uncertainty to the interpretation of signals from spaceborne optical sensors, microwave radiometers, and radars (scatterometers, SAR, altimeters). We therefore need to improve our understanding of physical snow properties, including the snow specific surface area, snow wetness and the stratigraphy of the snowpack on different ages of sea ice in the high Arctic.</p><p>The MOSAiC expedition provided a unique opportunity to deploy equivalent remote sensing sensors in-situ on the sea ice similar to those mounted on satellite platforms. To aid in the interpretation of the in situ remote sensing data collected, we used a micro computed tomography (micro-CT) device. This instrument was installed on board the Polarstern and was used to evaluate geometric and physical snow properties of in-situ snow samples.  This allowed us to relate the snow samples directly to the data from the remote sensing instruments, with the goal of improving interpretation of satellite retrievals. Our data covers the full annual evolution of the snow cover properties on multiple ice types and ice topographies including level first-year (FYI), level multi-year ice (MYI) and ridges.</p><p>First analysis of the data reveals possible uncertainties in the retrieved remote sensing data products related to previously unknown seasonal processes in the snowpack. For example, the refrozen porous summer ice surface, known as surface scattering layer, caused the formation of a hard layer at the multiyear ice/snow interface in the winter months, leading to significant differences in the snow stratigraphy and remote sensing signals from first-year ice, which has not experienced summer melt, and multiyear ice. Furthermore, liquid water dominates the extreme coarsening of snow grains in the summer months and in winter the temporally large temperature gradients caused strong metamorphism, leading to brine inclusions in the snowpack and large depth hoar structures, all this significantly influences the signal response of remote sensing instruments.</p>

Limited use of sea ice by the Ross seal (Ommatophoca rossii), in Amundsen Sea, Antarctica, using telemetry and remote sensing data

Polar Biology ◽  
2014 ◽  
Vol 38 (4) ◽  
pp. 445-461 ◽  
Author(s):  
Anna Arcalís-Planas ◽  
Signe Sveegaard ◽  
Olle Karlsson ◽  
Karin C. Harding ◽  
Anna Wåhlin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Remote Sensing Data ◽  
Amundsen Sea ◽  
Sensing Data
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