16 citations as recorded by crossref.

1. Uncertainty-Aware Learning With Label Noise for Glacier Mass Balance Modeling C. Diaconu & N. Gottschling https://doi.org/10.1109/LGRS.2024.3356160
2. Mass loss of Bayi Glacier in the Heihe River Basin revealed by ground-penetration radar measurements from 2006 to 2023 X. Pang et al. https://doi.org/10.1016/j.ejrh.2025.102255
3. Machine learning for mapping glacier surface facies in Svalbard S. Wankhede et al. https://doi.org/10.1016/j.rsase.2025.101753
4. Machine learning of Antarctic firn density by combining radiometer and scatterometer remote-sensing data W. Li et al. https://doi.org/10.5194/tc-19-37-2025
5. Evaluating the affecting factors of glacier mass balance in Tanggula Mountains using explainable machine learning and the open global glacier model Q. Xu et al. https://doi.org/10.1007/s11629-024-9047-4
6. Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard V. Steidl et al. https://doi.org/10.5194/tc-19-645-2025
7. Emulating the expansion of Antarctic perennial firn aquifers in the 21st century S. Veldhuijsen et al. https://doi.org/10.5194/tc-19-5157-2025
8. 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
9. Assessing glacial lake outburst flood risk in the Eastern Himalayas: a Bayesian neural network framework A. Vashistha et al. https://doi.org/10.1007/s11069-025-07668-4
10. Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia W. Ren et al. https://doi.org/10.3390/rs16060956
11. Snapshot and time-dependent inversions of basal sliding using automatic generation of adjoint code on graphics processing units I. Utkin et al. https://doi.org/10.1017/jog.2025.40
12. A minimal machine-learning glacier mass balance model M. van der Meer et al. https://doi.org/10.5194/tc-19-805-2025
13. Reconstructing ice phenology of a lake with complex surface cover: a case study of Lake Ulansu during 1941–2023 P. Huo et al. https://doi.org/10.5194/tc-19-849-2025
14. Machine learning improves seasonal mass balance prediction for unmonitored glaciers K. Sjursen et al. https://doi.org/10.5194/tc-19-5801-2025
15. Universal differential equations for glacier ice flow modelling J. Bolibar et al. https://doi.org/10.5194/gmd-16-6671-2023
16. Machine Learning-Based probabilistic prediction of glacial lake formation using erosional and topographic features A. Vashistha et al. https://doi.org/10.1038/s41598-025-17401-7