Combined airborne profiling over Fram Strait sea ice: Fractional sea-ice types, albedo, and thickness measurements

• Published in: Cold Regions Science and Technology
• Main Authors: Pedersen, C. A., Hall, R., Gerland, S., Sivertsen, A. H., Svenoe, T., Haas, Christian
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2009
• Subjects: Fram Strait; Sea ice
• Online Access: https://epic.awi.de/id/eprint/18656/; https://epic.awi.de/id/eprint/18656/1/Ped2008a.pdf; https://doi.org/10.1016/j.coldregions.2008.08.004; https://hdl.handle.net/10013/epic.31137; https://hdl.handle.net/10013/epic.31137.d001

This paper presents the data collected during an expedition from the marginal ice zone into the multi year sea ice in the Fram Strait in MayJune 2005 to measure the variance in sea-ice types, albedo and thickness. It also describes the techniques used to analyze the data. The principal information from the methodologies applied derives the sea-ice types from digital photography, the spectral and broadband reflectance from spectrometer measurements and the total sea-ice thickness profile from an electromagnetic-probe. A combination of methods was used to extract more information from each data set compared to what traditionally are obtained. The digital images were standardized, textural features extracted and a trained neural network was used for classification, while the optical measurements were normalized and standardized to minimize effects from the set-up and atmospheric conditions. Measurements from June 3rd (before the onset of summer melt) showed that the fractional sea-ice types had large spatial variability, with average fractions for snow-covered sea ice of 81.0%, thick bare ice 4.0%, thin ice 5.3% and open water 9.6%, hence an average ice concentration of 90.3%. The average broadband reflectance factor was 0.73, while the average total sea-ice thickness (including snow) was 2.1 m. Relative high correlations were found between the measured albedo and sea-ice concentration (0.69). The paper also addresses the lessons learned for future fusion of data from large field campaigns.