15 citations as recorded by crossref.

1. Enhancing Arctic Ice Extent Predictions: Leveraging Time Series Analysis and Deep Learning Architectures B. Ahanda et al. https://doi.org/10.3390/glacies2040012
2. Prediction of monthly Arctic sea ice concentration using physics-constrained U2-Net M. Liu et al. https://doi.org/10.1016/j.engappai.2026.115334
3. Advancing cryospheric studies: a historical perspective on radio-echo soundgram analysis techniques A. Awati et al. https://doi.org/10.1007/s12145-025-01996-6
4. Artificial intelligence for ocean phenomena forecasting X. Li et al. https://doi.org/10.1016/j.scib.2026.04.002
5. Explainable spatiotemporal deep learning for subseasonal super-resolution forecasting of Arctic sea ice concentration during the melting season J. He et al. https://doi.org/10.1016/j.isprsjprs.2025.11.027
6. ASICast: a novel model for daily antarctic sea ice concentration prediction C. Hu et al. https://doi.org/10.1080/17538947.2026.2696680
7. Research Progress of Deep Learning in Sea Ice Prediction J. Ran et al. https://doi.org/10.3390/rs18030419
8. 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
9. TPFMNet: A Probabilistic U-Net With Triple-Path Fusion Module for Spatially Adaptive Uncertainty in Year-Round Arctic Sea Ice Forecasting M. Wang et al. https://doi.org/10.1109/TGRS.2026.3708289
10. INTEGRATING ARTIFICIAL INTELLIGENCE INTO THE ARCTIC ECONOMY: PROSPECTS FOR RUSSIAN-CHINESE COOPERATION I. Skvortsova et al. https://doi.org/10.37614/2220-802X.4.2025.90.010
11. Geographically-weighted weakly supervised Bayesian High-Resolution Transformer for 200 m resolution pan-Arctic sea ice concentration mapping and uncertainty estimation using Sentinel-1, RCM, and AMSR2 data M. Heffring & L. Xu https://doi.org/10.1016/j.isprsjprs.2026.05.032
12. Enhancing Seasonal Arctic Sea Ice Prediction for Late Summer by Transfer Learning of PIOMAS and CryoSat-2-Observed Sea Ice Thickness Data Y. Ren et al. https://doi.org/10.1109/LGRS.2026.3680499
13. Frequency-Enhanced Hilbert Scanning Mamba for Short-Term Arctic Sea Ice Concentration Prediction F. Gao et al. https://doi.org/10.1109/TGRS.2026.3665141
14. Correcting errors in seasonal Arctic sea ice prediction of Earth system models with machine learning Z. He et al. https://doi.org/10.5194/tc-19-3279-2025
15. Exploring the limitations of transformer models for metocean forecasting J. Borisova et al. https://doi.org/10.1016/j.jocs.2026.102917