Declassified high-resolution visible imagery for Arctic sea ice investigations: An overview

2014 ◽  
Vol 142 ◽  
pp. 44-56 ◽  
Author(s):  
Ron Kwok
2016 ◽  
Vol 10 (3) ◽  
pp. 1161-1179 ◽  
Author(s):  
Alek A. Petty ◽  
Michel C. Tsamados ◽  
Nathan T. Kurtz ◽  
Sinead L. Farrell ◽  
Thomas Newman ◽  
...  

Abstract. We present an analysis of Arctic sea ice topography using high-resolution, three-dimensional surface elevation data from the Airborne Topographic Mapper, flown as part of NASA's Operation IceBridge mission. Surface features in the sea ice cover are detected using a newly developed surface feature picking algorithm. We derive information regarding the height, volume and geometry of surface features from 2009 to 2014 within the Beaufort/Chukchi and Central Arctic regions. The results are delineated by ice type to estimate the topographic variability across first-year and multi-year ice regimes. The results demonstrate that Arctic sea ice topography exhibits significant spatial variability, mainly driven by the increased surface feature height and volume (per unit area) of the multi-year ice that dominates the Central Arctic region. The multi-year ice topography exhibits greater interannual variability compared to the first-year ice regimes, which dominates the total ice topography variability across both regions. The ice topography also shows a clear coastal dependency, with the feature height and volume increasing as a function of proximity to the nearest coastline, especially north of Greenland and the Canadian Archipelago. A strong correlation between ice topography and ice thickness (from the IceBridge sea ice product) is found, using a square-root relationship. The results allude to the importance of ice deformation variability in the total sea ice mass balance, and provide crucial information regarding the tail of the ice thickness distribution across the western Arctic. Future research priorities associated with this new data set are presented and discussed, especially in relation to calculations of atmospheric form drag.


2003 ◽  
Vol 22 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Wieslaw Maslowski ◽  
William H. Lipscomb

Author(s):  
Qi Liu 1 ◽  
Yawen Zhang 1

During summer, melt ponds have a significant influence on Arctic sea-ice albedo. The melt pond fraction (MPF) also has the ability to forecast the Arctic sea-ice in a certain period. It is important to retrieve accurate melt pond fraction (MPF) from satellite data for Arctic research. This paper proposes a satellite MPF retrieval model based on the multi-layer neural network, named MPF-NN. Our model uses multi-spectral satellite data as model input and MPF information from multi-site and multi-period visible imagery as prior knowledge for modeling. It can effectively model melt ponds evolution of different regions and periods over the Arctic. Evaluation results show that the MPF retrieved from MODIS data using the proposed model has an RMSE of 3.91% and a correlation coefficient of 0.73. The seasonal distribution of MPF is also consistent with previous results.


Eos ◽  
2011 ◽  
Vol 92 (7) ◽  
pp. 53-54 ◽  
Author(s):  
Ronald Kwok ◽  
Norbert Untersteiner

2019 ◽  
Author(s):  
Ellen Buckley ◽  
Sinéad Farrell ◽  
Kyle Duncan ◽  
Laurence Connor ◽  
John Kuhn ◽  
...  

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