76 citations as recorded by crossref.

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4. 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. 10.1109/TGRS.2024.3479452
5. Identifying climate change impacts on surface water supply in the southern Central Valley, California Z. Liu et al. 10.1016/j.scitotenv.2020.143429
6. Binary and Fractional MODIS Snow Cover Mapping Boosted by Machine Learning and Big Landsat Data W. Luan et al. 10.1109/TGRS.2022.3198508
7. High-Resolution Inversion Method for the Snow Water Equivalent Based on the GF-3 Satellite and Optimized EQeau Model Y. Yang et al. 10.3390/rs14194931
8. Snow avalanche susceptibility mapping from tree-based machine learning approaches in ungauged or poorly-gauged regions Y. Liu et al. 10.1016/j.catena.2023.106997
9. Spatial Estimation of Snow Water Equivalent for Glaciers and Seasonal Snow in Iceland Using Remote Sensing Snow Cover and Albedo A. Gunnarsson & S. Gardarsson 10.3390/hydrology11010003
10. Development of a Snow Depth Estimation Algorithm over China for the FY-3D/MWRI J. Yang et al. 10.3390/rs11080977
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12. 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
13. Estimating high-resolution snow depth over the North Hemisphere mountains utilizing active microwave backscatter and machine learning Z. Ni et al. 10.1016/j.jhydrol.2024.132203
14. Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia J. Ahmad et al. 10.3389/feart.2019.00212
15. Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model A. Teweldebrhan et al. 10.5194/hess-24-4641-2020
16. Validation of remotely sensed estimates of snow water equivalent using multiple reference datasets from the middle and high latitudes of China J. Yang et al. 10.1016/j.jhydrol.2020.125499
17. A New Method to Simulate the Microwave Effective Snow Grain Size in the Northern Hemisphere Without Using Snow Depth Priors J. Yang et al. 10.1109/JSTARS.2024.3441817
18. Midwinter Dry Spells Amplify Post‐Fire Snowpack Decline B. Hatchett et al. 10.1029/2022GL101235
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20. Machine-Learning-Based Ensemble Prediction of the Snow Water Equivalent in the Upper Yalong River Basin J. Zhang et al. 10.3390/su17093779
21. Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. 10.1016/j.jhydrol.2022.128835
22. Mountain Snow Depth Retrieval From Optical and Passive Microwave Remote Sensing Using Machine Learning C. Xiong et al. 10.1109/LGRS.2022.3226204
23. Statistical emulation of hyper-resolution mechanistic snow modeling assesses forest management and the importance of tree arrangement J. Biederman et al. 10.1016/j.jhydrol.2025.133886
24. An Approach to Improve the Spatial Resolution and Accuracy of AMSR2 Passive Microwave Snow Depth Product Using Machine Learning in Northeast China Y. Wei et al. 10.3390/rs14061480
25. Improving snow depth estimation by coupling HUT-optimized effective snow grain size parameters with the random forest approach J. Yang et al. 10.1016/j.rse.2021.112630
26. Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data I. Alabi et al. 10.3389/frsen.2024.1481848
27. Canopy Adjustment and Improved Cloud Detection for Remotely Sensed Snow Cover Mapping K. Rittger et al. 10.1029/2019WR024914
28. Comparison of Machine Learning-Based Snow Depth Estimates and Development of a New Operational Retrieval Algorithm over China J. Yang et al. 10.3390/rs14122800
29. Retrieving snow water equivalent from GRACE/GRACE-FO terrestrial water storage anomalies using modified spectral combination theory F. Fatolazadeh et al. 10.1016/j.jhydrol.2025.133754
30. The Influence of Cloudiness on Hydrologic Fluctuations in the Mountains of the Western United States E. Sumargo & D. Cayan 10.1029/2018WR022687
31. A past and present perspective on the European summer vapor pressure deficit V. Nagavciuc et al. 10.5194/cp-20-573-2024
32. Estimating snow water equivalent using unmanned aerial vehicles for determining snow-melt runoff T. Niedzielski et al. 10.1016/j.jhydrol.2019.124046
33. Fine-Resolution Satellite Remote Sensing Improves Spatially Distributed Snow Modeling to Near Real Time G. Sexstone et al. 10.3390/rs17101704
34. Estimating Snow-Related Daily Change Events in the Canadian Winter Season: A Deep Learning-Based Approach K. Malik et al. 10.3390/jimaging11070239
35. Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach J. Yang et al. 10.5194/tc-14-1763-2020
36. Artificial intelligence in the water domain: Opportunities for responsible use N. Doorn 10.1016/j.scitotenv.2020.142561
37. Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset Y. Liu et al. 10.5194/tc-15-5261-2021
38. Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges J. Du et al. 10.3390/rs11161952
39. High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area M. Liu et al. 10.3390/rs12030460
40. Impact of climate change on spatiotemporal patterns of snow hydrology: Conceptual frameworks, machine learning versus nested model M. Besharatifar & M. Nasseri 10.1016/j.pce.2024.103691
41. An Overview of Snow Water Equivalent: Methods, Challenges, and Future Outlook M. Taheri & A. Mohammadian 10.3390/su141811395
42. Deep learning in environmental remote sensing: Achievements and challenges Q. Yuan et al. 10.1016/j.rse.2020.111716
43. Bias correction for near-real-time estimation of snow water equivalent using machine learning algorithms: A case study in the Tuolumne River basin, California K. Yang et al. 10.1016/j.rse.2025.114693
44. High‐resolution snow depth prediction using Random Forest algorithm with topographic parameters: A case study in the Greiner watershed, Nunavut J. Meloche et al. 10.1002/hyp.14546
45. Quantifying regional variability of machine-learning-based snow water equivalent estimates across the Western United States D. Liljestrand et al. 10.1016/j.envsoft.2024.106053
46. Improving the snowpack monitoring in the mountainous areas of Sweden from space: a machine learning approach J. Zhang et al. 10.1088/1748-9326/abfe8d
47. Evaluating different machine learning algorithms for snow water equivalent prediction M. Vafakhah et al. 10.1007/s12145-022-00846-z
48. Global snow drought hot spots and characteristics L. Huning & A. AghaKouchak 10.1073/pnas.1915921117
49. Achieving Breakthroughs in Global Hydrologic Science by Unlocking the Power of Multisensor, Multidisciplinary Earth Observations M. Durand et al. 10.1029/2021AV000455
50. How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction? E. Bair et al. 10.5194/tc-17-2629-2023
51. An Examination of Snow Albedo Estimates From MODIS and Their Impact on Snow Water Equivalent Reconstruction E. Bair et al. 10.1029/2019WR024810
52. Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps U. Mital et al. 10.1175/AIES-D-22-0010.1
53. Spatial patterns of snow distribution in the sub-Arctic K. Bennett et al. 10.5194/tc-16-3269-2022
54. A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
55. Comparison of modeled snow properties in Afghanistan, Pakistan, and Tajikistan E. Bair et al. 10.5194/tc-14-331-2020
56. Combining ground-based and remotely sensed snow data in a linear regression model for real-time estimation of snow water equivalent K. Yang et al. 10.1016/j.advwatres.2021.104075
57. Mapping snow cover frequency at 30 m for studying seasonal variations and topographic controls on the Tibetan Plateau G. Wang et al. 10.1016/j.jhydrol.2025.133303
58. Passive Microwave Brightness Temperature Assimilation to Improve Snow Mass Estimation Across Complex Terrain in Pakistan, Afghanistan, and Tajikistan J. Ahmad et al. 10.1109/JSTARS.2021.3102965
59. Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach D. Shao et al. 10.5194/essd-14-795-2022
60. Snow water equivalent prediction in a mountainous area using hybrid bagging machine learning approaches K. Khosravi et al. 10.1007/s11600-022-00934-0
61. Benchmarking large-scale evapotranspiration estimates: A perspective from a calibration-free complementary relationship approach and FLUXCOM N. Ma et al. 10.1016/j.jhydrol.2020.125221
62. Identifying and mapping the spatial distribution of regions prone to snowmelt flood hazards in the arid region of Central Asia: A case study in Xinjiang, China Y. Liu et al. 10.1111/jfr3.12947
63. Intercomparison of snow water equivalent products in the Sierra Nevada California using airborne snow observatory data and ground observations K. Yang et al. 10.3389/feart.2023.1106621
64. Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach Y. Liu et al. 10.1080/17538947.2023.2299208
65. Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications S. Schilling et al. 10.3390/rs16061085
66. Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction A. Meyal et al. 10.3389/frwa.2020.574917
67. Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska T. Douglas & C. Zhang 10.1088/1748-9326/ac04d8
68. Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran H. Ghanjkhanlo et al. 10.1007/s11629-018-4875-8
69. Distributed modelling of snow and ice melt in the Naltar Catchment, Upper Indus basin M. Usman Liaqat & R. Ranzi 10.1016/j.jhydrol.2024.131935
70. Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data H. Elyoussfi et al. 10.1016/j.ejrh.2024.102085
71. Evaluation of Machine Learning Techniques for Inflow Prediction in Lake Como, Italy M. Pini et al. 10.1016/j.procs.2020.09.087
72. Trends in Snow Cover Duration Across River Basins in High Mountain Asia From Daily Gap-Filled MODIS Fractional Snow Covered Area C. Ackroyd et al. 10.3389/feart.2021.713145
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74. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al. 10.3390/w16131904
75. Improving Snow Water Equivalent Maps With Machine Learning of Snow Survey and Lidar Measurements P. Broxton et al. 10.1029/2018WR024146
76. Estimation of Moderate-Resolution Snow Depth in Xinjiang With Enhanced-Resolution Passive Microwave and Reanalysis Data by Machine Learning Methods Y. Yan et al. 10.1109/JSTARS.2025.3562216