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Sea ice roughness can serve as a proxy for other sea ice characteristics such as ice thickness and ice age. Arctic-wide maps that represent spatial patterns of sea ice roughness can be used to better characterize spatial patterns of ice convergence and divergence processes. Sea ice surface roughness can also control and quantify turbulent exchange between sea ice surface and atmosphere and therefore influence surface energy balance at the basin scale. We have developed a data processing system that produces georeferenced sea ice roughness rasters that can be mosaicked to produce Arctic-wide maps of sea ice roughness. This approach starts with Top-of-Atmosphere radiance data from the Multi-angle Imaging SpectroRadiometer (MISR). We used red-band angular data from three MISR cameras (Ca, Cf, An). We created a training data set in which MISR pixels were matched with co-located and concurrent lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). We used a K-nearest neighbor algorithm with the training data to calibrate the multi-angle data to values of surface roughness and then applied the algorithm to Arctic-wide MISR data for two 16-day periods in April (spring) and July (summer). After georeferencing the roughness rasters, we then mosaicked each 16-day roughness dataset to produce Arctic-wide maps of sea ice roughness for spring and summer. Assessment of the results shows good agreement with independent ATM roughness data, not used in model development. A preliminary exploration of spatial and seasonal changes in sea ice roughness for two locations shows the ability to characterize the roughness of different ice types and the results align with previous studies. This processing system and its data products can help the sea ice research community to gain insights into the seasonal and interannual changes in sea ice roughness over the Arctic. [ABSTRACT FROM AUTHOR]

Spatial and temporal distributions of snow grain size, mass fraction of snow impurity, and snow surface temperature around the northern polar region were retrieved together with snow and sea ice cover extents from radiance data observed by the Global Imager (GLI), an optical sensor onboard Japan''s Advanced Earth Observing Satellite-II (ADEOS-II). The sensor acquired global radiance data from April 2 to October 24 in 2003. Two grain sizes, one for the shallow (0–20 cm) layer and the other for the top surface (0–2 cm) layer, were retrieved using channels with different light penetration depths. Snow and sea ice related products derived from the Advanced Microwave Scanning Radiometer (AMSR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) were used for comparison. Retrieved seasonal variations of snow and sea ice cover extents were consistent with the MODIS and AMSR products. GLI derived snow surface temperatures were also correlated with the MODIS surface temperatures and exhibited latitudinal dependence over the northern hemisphere. Retrieved snow impurities were considered to be partly inaccurate over the Arctic sea ice in April and over inland areas of the continents possibly due to thin snow and/or dense vegetation. However, the snow cover around the northern Canadian Arctic tundra and Archipelago regions and the Greenland ice sheet in April to June was found to be very clean with a soot-equivalent impurity fraction of around 0.05 ppmw or less. Thus, these areas can be used as reference sites for monitoring pollution levels of the Arctic snow cover. Retrieved snow grain radii in the shallow layer seemed erroneous over thin snow covers, but indicated drastic spatial and seasonal evolution of the snow cover over the northern hemisphere with radius ranging from 50 μm to over 1000 μm synchronized with variations in surface temperature. The spatial variability of the top surface grain radius was partly different from that of the grain size in the shallow layer suggesting the possibility of detecting vertical inhomogeneity of the top 10 cm of the snow cover. [Copyright &y& Elsevier]