EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Retrieval of snow layer and melt pond properties based on airborne hyperspectral imagery

Sophie Rosenburg1,2, Charlotte Lange1,2, Evelyn Jäkel1, Michael Schäfer1, and Manfred Wendisch1
Sophie Rosenburg et al.
  • 1Leipzig, Institute for Meteorology, Atmospheric Radiation, Germany (
  • 2These authors contributed equally to this work.

The melting snow layer, as a composition of ice, liquid water, and air, supplies meltwater in the runoff phase inducing the melt pond formation. These melting processes of Arctic sea ice alter the surface reflection properties and thereby affect the energy budget. Such sea ice surface reflection properties were surveyed by airborne hyperspectral imagery within the framework of an Arctic field campaign performed in May/June 2017. A retrieval approach based on different absorption indices of pure ice and liquid water in the near infrared spectral range is applied to the campaign data retrieving the spatial distribution of snow layer liquid water fraction and effective radius of snow grains. For the same sceneries the melt pond depth was retrieved based on an existing approach that isolates the dependence of a melt pond reflectance spectrum on the pond depth by eliminating the reflection contribution of the pond ice bottom. The presented retrieval methods show the potential of airborne hyperspectral imagery to map the transition phase of the Arctic sea ice surface examining the snow layer composition and melt pond bathymetry.

How to cite: Rosenburg, S., Lange, C., Jäkel, E., Schäfer, M., and Wendisch, M.: Retrieval of snow layer and melt pond properties based on airborne hyperspectral imagery, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3304,, 2023.

Supplementary materials

Supplementary material file