9 citations as recorded by crossref.

1. Automating glacier facies classification: Benchmark dataset and deep learning baseline from pan-European sample K. Maslov et al. https://doi.org/10.1016/j.srs.2026.100506
2. 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
3. Temperature-driven surface velocity of the Bara Shigri glacier: a proof-of-concept integration of SAR observations and ANN modelling . Vansheika et al. https://doi.org/10.1007/s12518-025-00666-y
4. Machine learning improves seasonal mass balance prediction for unmonitored glaciers K. Sjursen et al. https://doi.org/10.5194/tc-19-5801-2025
5. Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature N. Bochow et al. https://doi.org/10.5194/tc-20-1841-2026
6. Evaluating the Performance of Multiple Machine Learning and Deep Learning Models on Glacier Mass Balance Estimation Y. Liao et al. https://doi.org/10.3390/sym18050873
7. TROPIPOLAR – GLASCLIM project: advancing scientific collaboration on glacier mass balance modelling across the Andes and the Antarctic Peninsula through machine learning C. Torres et al. https://doi.org/10.1080/2154896X.2026.2696754
8. Reconstruction of glacier mass balance in a data-scarce Himalayan environment using reanalysis climate data and machine learning S. Sambyal et al. https://doi.org/10.1016/j.pgeola.2026.101224
9. Seasonal mass balance drivers for Swiss glaciers over 2010–2024 inferred from remote-sensing observations and modelling A. Cremona et al. https://doi.org/10.5194/tc-20-3111-2026