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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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