EGU24-9175, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Co-located OLCI optical imagery and SAR altimetry from Sentinel-3 for enhanced surface classification in sea ice

Weibin Chen1, Michel Tsamados1, Rosemary Willatt11, David Brockley5, Marc Deisenroth2, Claude De Rijke-Thomas7, Alistair Francis8, Len Hirata9, Thomas Johnson1, Isobel Lawrence3,10, Jack Landy6, Sanggyun Lee4, Wenxuan Liu12, Dorsa Nasrollahi Shirazi1, Connor Nelson1, Julienne Stroeve1, and So Takao2
Weibin Chen et al.
  • 1Centre for Polar Observation and Modelling, Earth Sciences, University College London, London, UK
  • 2Centre for Artificial Intelligence, University College London, London, UK
  • 3ESRIN, European Space Agency, Italy
  • 4Environmental Research Group, Research Institute of Industrial Science and Technology (RIST), South Korea
  • 5Mullard Space Science Laboratory, University College London, London, UK
  • 6UiT The Arctic University of Norway, Tromsø, Norway
  • 7School of Geographical Sciences, University of Bristol, Bristol, UK
  • 8PhiLab, ESRIN, European Space Agency, Italy
  • 9Kyoto University, Japan
  • 10Centre for Polar Observation and Modelling, University of Leeds, UK
  • 11Department of Geography and Environmental Sciences, Northumbria University
  • 12Wuhan University

In our research, we leverage the capabilities of the Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018, respectively, to deepen our understanding of the polar regions. These satellites offer a unique blend of high-resolution Ku-band radar altimetry data, synthetic aperture radar (SAR) mode altimetry, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer. This combination enables the acquisition of both optical imagery and SAR radar altimetry data, extending up to 81 degrees North. Central to our study is the application of deep learning techniques, specifically the Vision Transformers (ViT), which adapt the Transformer algorithm for surface classification in polar environments. This approach is instrumental in distinguishing between sea ice and leads, demonstrating robust performance across various metrics, including accuracy and model roll-out on comprehensive OLCI image datasets. We produce our first lead classification maps at the original OLCI swath level resolution of 300m and a lead fraction prototype mosaic spring pan-Arctic product at gridded level of 1km, 5km and 10km resolution and on daily, weekly and monthly timescales. The use of binned statistics in conjunction with our deep learning classifications provides valuable insights into the spatial distribution and changes of leads within the polar ice. We compare our prototype product with other existing lead products and with auxiliary datasets on thin ice (roughness, thickness). Our work combining different satellite products at pan-Arctic intermediate resolution enhances our capacity to estimate sea ice thickness and aids in forecasting future changes in the Arctic and Antarctic regions, thereby contributing to the field of polar remote sensing with direct applications to the future polar missions CRISTAL and CMIR.

How to cite: Chen, W., Tsamados, M., Willatt, R., Brockley, D., Deisenroth, M., De Rijke-Thomas, C., Francis, A., Hirata, L., Johnson, T., Lawrence, I., Landy, J., Lee, S., Liu, W., Nasrollahi Shirazi, D., Nelson, C., Stroeve, J., and Takao, S.: Co-located OLCI optical imagery and SAR altimetry from Sentinel-3 for enhanced surface classification in sea ice, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9175,, 2024.