Articles | Volume 13, issue 9
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.
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
| 17 Sep 2019
Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network
European Space Agency, Keplerlaan 1, 2201AZ Noordwijk, the Netherlands
Craig Donlon
European Space Agency, Keplerlaan 1, 2201AZ Noordwijk, the Netherlands
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- 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
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- Triple Collocation-Based Merging of Winter Snow Depth Retrievals on Arctic Sea Ice Derived From Three Different Algorithms Using AMSR2 L. He et al. 10.1109/TGRS.2023.3290073
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- 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
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- A combined multi-source data and deep learning approach for retrieving snow depth on Antarctic Sea ice during the melting season Z. Yan et al. 10.1080/17538947.2024.2376260
- Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI L. Li et al. 10.3390/rs13081457
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- Sensitivity of Passive Microwave Satellite Observations to Snow Density and Grain Size Over Arctic Sea Ice Y. Kwon et al. 10.1109/TGRS.2023.3322401
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- Retrieval of snow depth on Antarctic sea ice from the FY-3D MWRI data Z. Yan et al. 10.1007/s13131-023-2179-5
34 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
- An AI Framework to Obtain High-Accurate and Fine-Resolution LST From Passive Microwave Remote Sensing X. Deng et al. 10.1109/TGRS.2024.3435873
- 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
- Improved snow depth retrieval over Arctic sea ice from FY-3 satellites using machine learning and its future scenario projection C. Ke et al. 10.1016/j.jhydrol.2024.132105
- Mission to Mars: effective tools for searching and diagnosing water resources C. Varotsos et al. 10.1080/2150704X.2022.2033346
- Snowfall events in the Cantabrian Mountains of northwestern Spain: WRF multiphysics ensemble assessment based on ground and multi-satellite observations A. Melón-Nava et al. 10.1016/j.atmosres.2023.106719
- Inter-comparisons of Arctic snow depth products M. Li et al. 10.1080/17538947.2024.2376286
- 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
- Recent warming trends of the Greenland ice sheet documented by historical firn and ice temperature observations and machine learning B. Vandecrux et al. 10.5194/tc-18-609-2024
- A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation Y. Zhou et al. 10.5194/tc-17-1053-2023
- 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
- Winter arctic sea ice volume decline: uncertainties reduced using passive microwave-based sea ice thickness C. Soriot et al. 10.1038/s41598-024-70136-9
- Development of ANN-Based Algorithm to Estimate Wintertime Sea Ice Temperature Profile Over the Arctic Ocean S. Baek et al. 10.1109/TGRS.2023.3293137
- Improving snow depth simulations on Arctic Sea ice by assimilating a passive microwave-derived record H. Li et al. 10.1016/j.coldregions.2023.103929
- Triple Collocation-Based Merging of Winter Snow Depth Retrievals on Arctic Sea Ice Derived From Three Different Algorithms Using AMSR2 L. He et al. 10.1109/TGRS.2023.3290073
- 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
- On the Synergy of SMAP and AMSR2 for Estimating Snow Depth on Arctic Sea Ice L. He et al. 10.1109/LGRS.2022.3188001
- Deep neural network-based spatial gap-filling of MODIS ice surface temperatures over the Arctic using satellite and reanalysis data S. Sim et al. 10.1080/2150704X.2022.2138620
- Toward Daily Snow Depth Estimation on Arctic Sea Ice During the Whole Winter Season From Passive Microwave Radiometer Data L. He et al. 10.1109/TGRS.2024.3358340
- 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
- A combined multi-source data and deep learning approach for retrieving snow depth on Antarctic Sea ice during the melting season Z. Yan et al. 10.1080/17538947.2024.2376260
- Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI L. Li et al. 10.3390/rs13081457
- Making Waves: Microwaves in Climate Change R. Siegel & P. Siegel 10.1109/JMW.2023.3283395
- On the Importance of Representing Snow Over Sea‐Ice for Simulating the Arctic Boundary Layer G. Arduini et al. 10.1029/2021MS002777
- Arctic Sea Ice Thickness Estimation From Passive Microwave Satellite Observations Between 1.4 and 36 GHz C. Soriot et al. 10.1029/2022EA002542
- Sensitivity of Passive Microwave Satellite Observations to Snow Density and Grain Size Over Arctic Sea Ice Y. Kwon et al. 10.1109/TGRS.2023.3322401
- Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry A. Horton et al. 10.3390/rs14246210
- Retrieval of snow depth on Antarctic sea ice from the FY-3D MWRI data Z. Yan et al. 10.1007/s13131-023-2179-5
Latest update: 14 May 2025
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...
