9 citations as recorded by crossref.

1. Improving AMSR2-derived Snow Depth Spatial Resolution Through Synergistic Combination of Machine Learning and Random Sets Over the European Alps A. Ghofrani Esfahani et al. https://doi.org/10.1007/s41064-025-00338-x
2. C-Band Radar-Based Improved Snow Depth Estimation (C-RISE) in the Indian Western Himalayas and Colorado Rocky Mountains R. Chandra Prabha et al. https://doi.org/10.1109/JSTARS.2025.3563462
3. Universal Snow Avalanche Modeling Index Based on SAFI–Flow-R Approach in Poorly-Gauged Regions U. Durlević et al. https://doi.org/10.3390/ijgi13090315
4. KAN- and CNN-Driven Snow Stratigraphy Retrieval Across Antarctic and Subarctic Environments Z. Chen et al. https://doi.org/10.1109/JSTARS.2025.3631822
5. Arctic Snow Density Retrieval from AMSR-2 Passive Microwave Brightness Temperatures: A Comparative Evaluation of Machine-Learning and Deep-Learning Models J. Zhang et al. https://doi.org/10.3390/jmse14161461
6. Estimation of Moderate-Resolution Snow Depth in Xinjiang With Enhanced-Resolution Passive Microwave and Reanalysis Data by Machine Learning Methods Y. Yan et al. https://doi.org/10.1109/JSTARS.2025.3562216
7. Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable machine learning and SHAP analysis M. Boueshagh et al. https://doi.org/10.3389/frsen.2025.1554084
8. A New Operational Northern Hemisphere Snow Water Equivalent Retrieval Algorithm for FY-3F/MWRI-II Based on Pixel-Based Regression Coefficients J. Yang et al. https://doi.org/10.1109/TGRS.2024.3479452
9. Snow avalanche susceptibility, hazard, and exposure assessment in the Western Himalaya using machine learning and numerical modelling A. Abhinav & A. Sattar https://doi.org/10.1038/s41598-025-22051-w