Mosadegh, E.; Nolin, A.W. A New Data Processing System for Generating Sea Ice Surface Roughness Products from the Multi-Angle Imaging SpectroRadiometer (MISR) Imagery. Remote Sens.2022, 14, 4979.
Mosadegh, E.; Nolin, A.W. A New Data Processing System for Generating Sea Ice Surface Roughness Products from the Multi-Angle Imaging SpectroRadiometer (MISR) Imagery. Remote Sens. 2022, 14, 4979.
Mosadegh, E.; Nolin, A.W. A New Data Processing System for Generating Sea Ice Surface Roughness Products from the Multi-Angle Imaging SpectroRadiometer (MISR) Imagery. Remote Sens.2022, 14, 4979.
Mosadegh, E.; Nolin, A.W. A New Data Processing System for Generating Sea Ice Surface Roughness Products from the Multi-Angle Imaging SpectroRadiometer (MISR) Imagery. Remote Sens. 2022, 14, 4979.
Abstract
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.
Keywords
sea ice; surface roughness; remote sensing; MISR
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
