10 citations as recorded by crossref.

1. 2024 ESA-ECMWF workshop report: current status, progress and opportunities in machine learning for Earth system observation and prediction P. Ebel et al. https://doi.org/10.1038/s41612-024-00757-4
2. Multifractal analysis of the perimeters of glaciers in the Svalbard Archipelago M. Carpineti et al. https://doi.org/10.1088/2632-072X/ad5263
3. Distinct characteristics and mechanisms of two tidewater glaciers exhibiting velocity anomalies in western Svalbard W. Su et al. https://doi.org/10.1080/15481603.2025.2596939
4. Pervasive glacier retreats across Svalbard from 1985 to 2023 T. Li et al. https://doi.org/10.1038/s41467-025-55948-1
5. Billefjorden’s benthic biodiversity: the impact of glacier retreat on faunal communities in a high Arctic fjord A. van der Kamp et al. https://doi.org/10.1007/s00300-025-03390-w
6. Acoustic activity indicates submarine melt at tidewater glaciers H. Vishnu et al. https://doi.org/10.1017/jog.2025.10061
7. A Scoping Review of Automated Calving Front Detection in Satellite Images and Calving Front Position Datasets W. Milczarek et al. https://doi.org/10.3390/rs18070969
8. Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imagery E. Loebel et al. https://doi.org/10.5194/essd-17-65-2025
9. CISNet: Change information guided semantic segmentation network for automatic extraction of glacier calving fronts J. Zhao et al. https://doi.org/10.1016/j.isprsjprs.2025.08.001
10. Surging glaciers in Svalbard: Observing their distribution, characteristics and evolution W. Harcourt et al. https://doi.org/10.1016/j.earscirev.2026.105410