Articles | Volume 13, issue 4
The Cryosphere, 13, 1283–1296, 2019
https://doi.org/10.5194/tc-13-1283-2019
The Cryosphere, 13, 1283–1296, 2019
https://doi.org/10.5194/tc-13-1283-2019

Research article 18 Apr 2019

Research article | 18 Apr 2019

Estimating the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and ice mass balance buoy data

Lise Kilic et al.

Related authors

Technical note: A sensitivity analysis from 1 to 40 GHz for observing the Arctic Ocean with the Copernicus Imaging Microwave Radiometer
Lise Kilic, Catherine Prigent, Carlos Jimenez, and Craig Donlon
Ocean Sci., 17, 455–461, https://doi.org/10.5194/os-17-455-2021,https://doi.org/10.5194/os-17-455-2021, 2021
Short summary

Related subject area

Discipline: Sea ice | Subject: Remote Sensing
Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data
Stefan Kern, Thomas Lavergne, Leif Toudal Pedersen, Rasmus Tage Tonboe, Louisa Bell, Maybritt Meyer, and Luise Zeigermann
The Cryosphere, 16, 349–378, https://doi.org/10.5194/tc-16-349-2022,https://doi.org/10.5194/tc-16-349-2022, 2022
Short summary
Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity
Wenkai Guo, Polona Itkin, Johannes Lohse, Malin Johansson, and Anthony Paul Doulgeris
The Cryosphere, 16, 237–257, https://doi.org/10.5194/tc-16-237-2022,https://doi.org/10.5194/tc-16-237-2022, 2022
Short summary
Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements
Florent Garnier, Sara Fleury, Gilles Garric, Jérôme Bouffard, Michel Tsamados, Antoine Laforge, Marion Bocquet, Renée Mie Fredensborg Hansen, and Frédérique Remy
The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021,https://doi.org/10.5194/tc-15-5483-2021, 2021
Short summary
Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry
Lanqing Huang, Georg Fischer, and Irena Hajnsek
The Cryosphere, 15, 5323–5344, https://doi.org/10.5194/tc-15-5323-2021,https://doi.org/10.5194/tc-15-5323-2021, 2021
Short summary
Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay early spring sea ice thickness
Isolde A. Glissenaar, Jack C. Landy, Alek A. Petty, Nathan T. Kurtz, and Julienne C. Stroeve
The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021,https://doi.org/10.5194/tc-15-4909-2021, 2021
Short summary

Cited articles

Baordo, F. and Geer, A.: Microwave Surface Emissivity over sea-ice, EUMETSAF NWP SAF, Tech. Rep. NWPSAF_EC_VS_026, 1–30, 2015. a
Comiso, J.: Sea ice effective microwave emissivities from satellite passive microwave and infrared observations, J. Geophys. Res., 88, 7686–7704, 1983. a, b, c
Comiso, J., Cavalieri, D., and Markus, T.: Sea ice concentration, ice temperature, and snow depth using AMSR-E data, IEEE T. Geosci. Remote, 41, 243–252, 2003. a
Draper, N. R. and Smith, H.: Applied regression analysis, John Wiley & Sons, Inc., Hoboken, NJ, USA, 1998. a
Dybkjær, G., Tonboe, R., and Høyer, J. L.: Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data, Ocean Sci., 8, 959–970, https://doi.org/10.5194/os-8-959-2012, 2012. a
Download
Short summary
In this study, we develop and present simple algorithms to derive the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice. This is achieved using satellite observations collocated with buoy measurements. The errors of the retrieved parameters are estimated and compared with independent data. These parameters are useful for sea ice concentration mapping, understanding sea ice properties and variability, and for atmospheric sounding applications.