Articles | Volume 13, issue 9
The Cryosphere, 13, 2421–2438, 2019
https://doi.org/10.5194/tc-13-2421-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Cryosphere, 13, 2421–2438, 2019
https://doi.org/10.5194/tc-13-2421-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
17 Sep 2019
Research article
| 17 Sep 2019
Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network
Anne Braakmann-Folgmann and Craig Donlon
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Cited
13 citations as recorded by crossref.
- Filling gaps of black-sky surface albedo of the Arctic sea ice using gradient boosting and brightness temperature data E. Jääskeläinen et al. 10.1016/j.jag.2022.102701
- Impact of channel selection on SST retrievals from passive microwave observations P. Nielsen-Englyst et al. 10.1016/j.rse.2020.112252
- Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment B. Bhattarai et al. 10.3390/w12092339
- Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements F. Garnier et al. 10.5194/tc-15-5483-2021
- A deep learning approach to retrieve cold-season snow depth over Arctic sea ice from AMSR2 measurements H. Li et al. 10.1016/j.rse.2021.112840
- Snow depth product over Antarctic sea ice from 2002 to 2020 using multisource passive microwave radiometers X. Shen et al. 10.5194/essd-14-619-2022
- Sea Ice Thickness Estimation Based on Regression Neural Networks Using L-Band Microwave Radiometry Data from the FSSCat Mission C. Herbert et al. 10.3390/rs13071366
- A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data Z. Dong et al. 10.3390/rs14041041
- Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval L. Zhou et al. 10.5194/tc-15-345-2021
- Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI L. Li et al. 10.3390/rs13081457
- Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery S. Lee et al. 10.1016/j.rse.2020.111919
- Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements H. Shi et al. 10.5194/tc-14-3761-2020
- Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network J. Liu et al. 10.3390/rs11232864
13 citations as recorded by crossref.
- Filling gaps of black-sky surface albedo of the Arctic sea ice using gradient boosting and brightness temperature data E. Jääskeläinen et al. 10.1016/j.jag.2022.102701
- Impact of channel selection on SST retrievals from passive microwave observations P. Nielsen-Englyst et al. 10.1016/j.rse.2020.112252
- Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment B. Bhattarai et al. 10.3390/w12092339
- Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements F. Garnier et al. 10.5194/tc-15-5483-2021
- A deep learning approach to retrieve cold-season snow depth over Arctic sea ice from AMSR2 measurements H. Li et al. 10.1016/j.rse.2021.112840
- Snow depth product over Antarctic sea ice from 2002 to 2020 using multisource passive microwave radiometers X. Shen et al. 10.5194/essd-14-619-2022
- Sea Ice Thickness Estimation Based on Regression Neural Networks Using L-Band Microwave Radiometry Data from the FSSCat Mission C. Herbert et al. 10.3390/rs13071366
- A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data Z. Dong et al. 10.3390/rs14041041
- Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval L. Zhou et al. 10.5194/tc-15-345-2021
- Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI L. Li et al. 10.3390/rs13081457
- Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery S. Lee et al. 10.1016/j.rse.2020.111919
- Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements H. Shi et al. 10.5194/tc-14-3761-2020
- Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network J. Liu et al. 10.3390/rs11232864
Latest update: 08 Aug 2022
Short summary
Snow on sea ice is a fundamental climate variable. We propose a novel approach to estimate snow depth on sea ice from satellite microwave radiometer measurements at several frequencies using neural networks (NNs). We evaluate our results with airborne snow depth measurements and compare them to three other established snow depth algorithms. We show that our NN results agree better with the airborne data than the other algorithms. This is also advantageous for sea ice thickness calculation.
Snow on sea ice is a fundamental climate variable. We propose a novel approach to estimate snow...
