38 citations as recorded by crossref.

1. More than a century of direct glacier mass-balance observations on Claridenfirn, Switzerland M. Huss et al. 10.1017/jog.2021.22
2. A two-fold deep-learning strategy to correct and downscale winds over mountains L. Le Toumelin et al. 10.5194/npg-31-75-2024
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4. Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al. 10.1017/jog.2021.120
5. EisNet: Extracting Bedrock and Internal Layers From Radiostratigraphy of Ice Sheets With Machine Learning S. Dong et al. 10.1109/TGRS.2021.3136648
6. Overall negative trends for snow cover extent and duration in global mountain regions over 1982–2020 C. Notarnicola 10.1038/s41598-022-16743-w
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8. Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques R. Anilkumar et al. 10.5194/tc-17-2811-2023
9. Results from the Ice Thickness Models Intercomparison eXperiment Phase 2 (ITMIX2) D. Farinotti et al. 10.3389/feart.2020.571923
10. Universal differential equations for glacier ice flow modelling J. Bolibar et al. 10.5194/gmd-16-6671-2023
11. Climatic and Morphometric Explanatory Variables of Glacier Changes in the Andes (8–55°S): New Insights From Machine Learning Approaches A. Caro et al. 10.3389/feart.2021.713011
12. A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015 J. Bolibar et al. 10.5194/essd-12-1973-2020
13. Snow and Ice Animation Methods in Computer Graphics P. Goswami 10.1111/cgf.15059
14. A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning S. de Roda Husman et al. 10.1016/j.rse.2023.113950
15. Climate Variability and Glacier Evolution at Selected Sites Across the World: Past Trends and Future Projections A. Al‐Yaari et al. 10.1029/2023EF003618
16. Glacier retreat in Himachal from 1994 to 2021 using deep learning S. Rajat et al. 10.1016/j.rsase.2022.100870
17. Empirical glacier mass-balance models for South America S. Mutz & J. Aschauer 10.1017/jog.2022.6
18. Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić 10.3389/frwa.2022.934709
19. The West Kunlun Glacier Anomaly and Its Response to Climate Forcing during 2002–2020 J. Luo et al. 10.3390/rs14143465
20. Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India M. Haq et al. 10.1017/jog.2021.19
21. Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning J. Bolibar et al. 10.1038/s41467-022-28033-0
22. The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021) M. Vernay et al. 10.5194/essd-14-1707-2022
23. Deep learning-based framework for monitoring of debris-covered glacier from remotely sensed images A. Khan et al. 10.1016/j.asr.2022.05.060
24. Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia W. Ren et al. 10.3390/rs16060956
25. Remote sensing of the mountain cryosphere: Current capabilities and future opportunities for research L. Taylor et al. 10.1177/03091333211023690
26. Estimation of area and volume change in the glaciers of the Columbia Icefield, Canada using machine learning algorithms and Landsat images S. Ambinakudige & A. Intsiful 10.1016/j.rsase.2022.100732
27. Glacier Boundary Mapping Using Deep Learning Classification over Bara Shigri Glacier in Western Himalayas V. Sood et al. 10.3390/su142013485
28. LamaH-Ice: LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason & B. Nijssen 10.5194/essd-16-2741-2024
29. Rapid prediction of lab-grown tissue properties using deep learning A. Andrews et al. 10.1088/1478-3975/ad0019
30. Glacial retreat delineation using machine and deep learning: A case of a lower Himalayan region S. Vemuri et al. 10.1007/s12040-024-02285-4
31. Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches J. Che et al. 10.3390/rs14225775
32. Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks M. van der Meer et al. 10.1029/2022MS003593
33. Accelerating Subglacial Hydrology for Ice Sheet Models With Deep Learning Methods V. Verjans & A. Robel 10.1029/2023GL105281
34. Insight into glacio-hydrologicalprocesses using explainable machine-learning (XAI) models H. Hao et al. 10.1016/j.jhydrol.2024.131047
35. Uncertainty-Aware Learning With Label Noise for Glacier Mass Balance Modeling C. Diaconu & N. Gottschling 10.1109/LGRS.2024.3356160
36. Ice‐Dynamical Glacier Evolution Modeling—A Review H. Zekollari et al. 10.1029/2021RG000754
37. Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current approaches, open challenges, and future opportunities C. Persello et al. 10.1109/MGRS.2021.3136100
38. Brief communication: Non-linear sensitivity of glacier mass balance to climate attested by temperature-index models C. Vincent & E. Thibert 10.5194/tc-17-1989-2023