35 citations as recorded by crossref.

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2. Deep Learning for Seasonal Navigability Prediction Along the Northern Sea Route: When Does It Add Value? S. Lee et al. https://doi.org/10.3390/su18104873
3. Impact of intermediate ice concentration training data on sea ice concentration estimates from a convolutional neural network Y. Xu & K. Scott https://doi.org/10.1080/01431161.2019.1582113
4. The AutoICE Challenge A. Stokholm et al. https://doi.org/10.5194/tc-18-3471-2024
5. Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data With a U-Net and Curriculum Learning K. Radhakrishnan et al. https://doi.org/10.1109/JSTARS.2021.3076109
6. Baltic Sea Ice Concentration Estimation Using SENTINEL-1 SAR and AMSR2 Microwave Radiometer Data J. Karvonen https://doi.org/10.1109/TGRS.2017.2655567
7. Simulating transient ice-ocean Ekman transport in the Regional Arctic System Model and Community Earth System Model A. Roberts et al. https://doi.org/10.3189/2015AoG69A760
8. Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle J. Lohse et al. https://doi.org/10.1017/aog.2020.45
9. Skill metrics for evaluation and comparison of sea ice models D. Dukhovskoy et al. https://doi.org/10.1002/2015JC010989
10. Atmospheric controls on the Terra Nova Bay polynya occurrence in Antarctica R. Fonseca et al. https://doi.org/10.1007/s00382-023-06845-0
11. On Suitability of ALOS-2/PALSAR-2 Dual-Polarized SAR Data for Arctic Sea Ice Parameter Estimation J. Karvonen et al. https://doi.org/10.1109/TGRS.2020.2985696
12. Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification R. Ressel & S. Singha https://doi.org/10.3390/rs8030198
13. SAR data applications in earth observation: An overview A. Tsokas et al. https://doi.org/10.1016/j.eswa.2022.117342
14. SAR and Passive Microwave Fusion Scheme: A Test Case on Sentinel‐1/AMSR‐2 for Sea Ice Classification E. Khachatrian et al. https://doi.org/10.1029/2022GL102083
15. MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data M. Mäkynen & J. Karvonen https://doi.org/10.3390/rs9121324
16. Baltic Sea Ice Concentration Estimation From C-Band Dual-Polarized SAR Imagery by Image Segmentation and Convolutional Neural Networks J. Karvonen https://doi.org/10.1109/TGRS.2021.3097885
17. Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges W. Huang et al. https://doi.org/10.3390/rs16050842
18. A Conditional Denoising Diffusion Probabilistic Model for Sea Ice Concentration Estimation Y. Chen et al. https://doi.org/10.1109/LGRS.2025.3606975
19. A practical algorithm for the retrieval of floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar imagery B. Hwang et al. https://doi.org/10.1525/elementa.154
20. Review of Methods to Retrieve Sea-Ice Parameters from Satellite Microwave Radiometer Data E. Zabolotskikh https://doi.org/10.1134/S0001433818060166
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22. The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset V. Ludwig et al. https://doi.org/10.5194/tc-13-2051-2019
23. Multi-scale observations of the co-evolution of sea ice thermophysical properties and microwave brightness temperatures during the summer melt period in Hudson Bay M. Harasyn et al. https://doi.org/10.1525/elementa.412
24. Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks H. Boulze et al. https://doi.org/10.3390/rs12132165
25. A Meta-Analysis of Sea Ice Monitoring Using Spaceborne Polarimetric SAR: Advances in the Last Decade H. Lyu et al. https://doi.org/10.1109/JSTARS.2022.3194324
26. Sea and Freshwater Ice Concentration from VIIRS on Suomi NPP and the Future JPSS Satellites Y. Liu et al. https://doi.org/10.3390/rs8060523
27. Classification of sea ice types in Sentinel-1 synthetic aperture radar images J. Park et al. https://doi.org/10.5194/tc-14-2629-2020
28. Estimating Sea Ice Concentration From SAR: Training Convolutional Neural Networks With Passive Microwave Data C. Cooke & K. Scott https://doi.org/10.1109/TGRS.2019.2892723
29. Satellite SAR Data-based Sea Ice Classification: An Overview N. Zakhvatkina et al. https://doi.org/10.3390/geosciences9040152
30. Evaluation of a Neural Network With Uncertainty for Detection of Ice and Water in SAR Imagery N. Asadi et al. https://doi.org/10.1109/TGRS.2020.2992454
31. Recognizing the Shape and Size of Tundra Lakes in Synthetic Aperture Radar (SAR) Images Using Deep Learning Segmentation D. Demchev et al. https://doi.org/10.3390/rs15051298
32. Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data S. Kern et al. https://doi.org/10.5194/tc-16-349-2022
33. Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification J. Lohse et al. https://doi.org/10.3390/rs13040552
34. Prediction of Categorized Sea Ice Concentration From Sentinel-1 SAR Images Based on a Fully Convolutional Network I. de Gelis et al. https://doi.org/10.1109/JSTARS.2021.3074068
35. A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic J. Liu et al. https://doi.org/10.3390/rs14051130