EGU21-14968
https://doi.org/10.5194/egusphere-egu21-14968
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Collocated OLCI optical imagery and SAR radar altimetry from Sentinel3 for enhanced sea ice surface classification

Dorsa Nasrollahi Shirazi1, Michel Tsamados1, Isobel Lawrence2, Sanggyun Lee1, Thomas Johnson1, Claude De Rijke-Thomas3, Jack Landy3,4, David Brockley5, and Ryan Nichol1
Dorsa Nasrollahi Shirazi et al.
  • 1University College London, Center for Polar Observation and Modelling, Earth Sciences, London, United Kingdom of Great Britain – England, Scotland, Wales (m.tsamados@ucl.ac.uk)
  • 2University of Leeds, Center for Polar Observation and Modelling, Earth Sciences, Leeds, United Kingdom of Great Britain – England, Scotland, Wales
  • 3University of Bristol, Bristol, United Kingdom of Great Britain – England, Scotland, Wales
  • 4University of Tromso, Tromso, Norway
  • 5University College London, Mullard Space Science Laboratory, United Kingdom of Great Britain – England, Scotland, Wales

The Copernicus operational Sentinel-3A since February 2016 and Sentinel-3B since April 2018 build on the CryoSat-2 legacy in terms of their synthetic aperture radar (SAR) mode altimetry providing high-resolution radar freeboard elevation data over the polar regions up to 81N. This technology combined with the Ocean and Land Colour Instrument (OLCI) imaging spectrometer offers the first space-time collocated optical imagery and radar altimetry dataset. We use these joint datasets for validation of several existing surface classification algorithms based on Sentinel-3 altimeter echo shapes. We also explore the potential for novel AI techniques such as convolutional neural networks (CNN) for winter and summer sea ice surface classification (i.e. melt pond fraction, lead fraction, sea ice roughness). For lead surface classification we analyse the winters of 2018/19 and 2019/20 and for summer sea ice feature classification we focus on the Sentinel-3A &3B tandem phase of the summer 2018. We compare our CNN models with other existing surface classification algorithms.

How to cite: Nasrollahi Shirazi, D., Tsamados, M., Lawrence, I., Lee, S., Johnson, T., De Rijke-Thomas, C., Landy, J., Brockley, D., and Nichol, R.: Collocated OLCI optical imagery and SAR radar altimetry from Sentinel3 for enhanced sea ice surface classification, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14968, https://doi.org/10.5194/egusphere-egu21-14968, 2021.

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