Cross-validation of polynya monitoring methods from multi-sensor satellite and airborne data: a case study from the Laptev Sea.

• Published in: Canadian Journal of Remote Sensing
• Main Authors: Willmes, Sascha, Krumpen, Thomas, Adams, Susanne, Rabenstein, L., Haas, Christian, Hendricks, S., Heinemann, Günther, Hoelemann, Jens
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Canadian Aeronautics and Space Institute 2010
• Subjects: Arctic; Laptev Sea; laptev; Sea ice
• Online Access: https://oceanrep.geomar.de/id/eprint/19101/; https://doi.org/10.5589/m10-012

Wind-driven coastal polynyas in the polar oceans are recognized as regions of extensive new ice formation in the cold season. Hence, they may play an increasing role in the uncertain future of the sea-ice budget in the polar oceans. The Laptev Sea polynyas in the Siberian Arctic are well recognized as being significant ice producers and might gain special attention with regards to ice volume changes in the Arctic. Long-term monitoring and characterization of these polynyas require stable methods to detect the area of open water and the growth, thickness, and evolution of thin ice. We examine different parameters and methods to observe polynya area and thin ice thickness during a prominent polynya event in the Laptev Sea in April 2008. These are derived from visible, infrared, and microwave satellite data. Airborne electromagnetic ice thickness measurements with high spatial resolution and aerial photography taken across the polynya are used to assess the feasibility of the methods for long-term and large-scale polynya monitoring within this area. Our results indicate that in the narrow flaw polynyas of the Laptev Sea the coarse resolution of commonly used microwave channel combinations provokes sources of error through mixed signals at the fast- and pack-ice edges. Polynya monitoring results can be significantly improved using enhanced resolution data products. This implies that previously suggested methods for the retrieval of polynya area, thin ice thickness, and ice production are not transferable in space and time. Data as well as method parameterizations have to be chosen carefully to avoid large errors due to regional peculiarities.