26 citations as recorded by crossref.

1. 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
2. Impact of channel selection on SST retrievals from passive microwave observations P. Nielsen-Englyst et al. 10.1016/j.rse.2020.112252
3. Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment B. Bhattarai et al. 10.3390/w12092339
4. 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
5. 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
6. Mission to Mars: effective tools for searching and diagnosing water resources C. Varotsos et al. 10.1080/2150704X.2022.2033346
7. 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
8. 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
9. A sensor-agnostic albedo retrieval method for realistic sea ice surfaces: model and validation Y. Zhou et al. 10.5194/tc-17-1053-2023
10. Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery S. Lee et al. 10.1016/j.rse.2020.111919
11. 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
12. Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network J. Liu et al. 10.3390/rs11232864
13. 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
14. 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
15. 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
16. 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
17. On the Synergy of SMAP and AMSR2 for Estimating Snow Depth on Arctic Sea Ice L. He et al. 10.1109/LGRS.2022.3188001
18. 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
19. 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
20. 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
21. A New Structure for the Sea Ice Essential Climate Variables of the Global Climate Observing System T. Lavergne et al. 10.1175/BAMS-D-21-0227.1
22. Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI L. Li et al. 10.3390/rs13081457
23. Making Waves: Microwaves in Climate Change R. Siegel & P. Siegel 10.1109/JMW.2023.3283395
24. On the Importance of Representing Snow Over Sea‐Ice for Simulating the Arctic Boundary Layer G. Arduini et al. 10.1029/2021MS002777
25. Arctic Sea Ice Thickness Estimation From Passive Microwave Satellite Observations Between 1.4 and 36 GHz C. Soriot et al. 10.1029/2022EA002542
26. Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry A. Horton et al. 10.3390/rs14246210