36 citations as recorded by crossref.

1. Adaptability analysis of snow in the Zhangjiakou competition zone of the Beijing Olympic Winter Games for the next 30 years D. Shao et al. 10.1016/j.ejrh.2023.101358
2. Fusing daily snow water equivalent from 1980 to 2020 in China using a spatiotemporal XGBoost model L. Sun et al. 10.1016/j.jhydrol.2024.130876
3. Developments and Future Strategies of Earth Science from Space in China J. SHI et al. 10.11728/cjss2021.01.095
4. An improvement of snow/cloud discrimination from machine learning using geostationary satellite data D. Jin et al. 10.1080/17538947.2022.2152886
5. Significant decreasing trends in snow cover and duration in Northeast China during the past 40 years from 1980 to 2020 Y. Wei et al. 10.1016/j.jhydrol.2023.130318
6. A comparison of machine learning methods for estimation of snow density using satellite images M. Goodarzi et al. 10.1111/wej.12939
7. 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
8. 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
9. Mountain Snow Depth Retrieval From Optical and Passive Microwave Remote Sensing Using Machine Learning C. Xiong et al. 10.1109/LGRS.2022.3226204
10. Snow depth variability across the Qinghai Plateau and its influencing factors during 1980–2018 H. Ma et al. 10.1002/joc.7883
11. Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar V. Dharmadasa et al. 10.5194/tc-17-1225-2023
12. Variation of Snow Mass in a Regional Climate Model Downscaling Simulation Covering the Tianshan Mountains, Central Asia T. Yang et al. 10.1029/2020JD034183
13. Snow depth retrieval from microwave remote sensing by combining wavelet transform and machine learning models in Northern Xinjiang, China H. Hou et al. 10.1117/1.JRS.18.024517
14. 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
15. A novel fine-resolution snow depth retrieval model to reveal detailed spatiotemporal patterns of snow cover in Northeast China Y. Wei et al. 10.1080/17538947.2023.2196446
16. 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
17. Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications S. Schilling et al. 10.3390/rs16061085
18. 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
19. Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere Y. Hu et al. 10.3390/rs13071250
20. A Fine-Resolution Snow Depth Retrieval Algorithm From Enhanced-Resolution Passive Microwave Brightness Temperature Using Machine Learning in Northeast China Y. Wei et al. 10.1109/LGRS.2022.3196135
21. Triple Collocation-Based Merging of Winter Snow Depth Retrievals on Arctic Sea Ice Derived From Three Different Algorithms Using AMSR2 L. He et al. 10.1109/TGRS.2023.3290073
22. Spatiotemporal Changes of Snow Depth in Western Jilin, China from 1987 to 2018 Y. Wei et al. 10.1007/s11769-023-1400-y
23. Performances of three representative snow depth products originated from passive microwave sensors over the Mongolian Plateau S. Chang et al. 10.1080/17538947.2024.2395871
24. Development of a Pixel-Wise Forest Transmissivity Model at Frequencies of 19 GHz and 37 GHz for Snow Depth Inversion in Northeast China G. Wang et al. 10.3390/rs14215483
25. A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning Y. Hu et al. 10.1080/20964471.2023.2177435
26. 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
27. Passive Microwave Remote Sensing of Snow Depth: Techniques, Challenges and Future Directions S. Tanniru & R. Ramsankaran 10.3390/rs15041052
28. A past and present perspective on the European summer vapor pressure deficit V. Nagavciuc et al. 10.5194/cp-20-573-2024
29. A 0.01° daily improved snow depth dataset for the Tibetan Plateau D. Yan & Y. Zhang 10.1016/j.jhydrol.2024.130706
30. Passive microwave remote-sensing-based high-resolution snow depth mapping for Western Himalayan zones using multifactor modeling approach D. Singh et al. 10.5194/tc-18-451-2024
31. Daily station-level records of air temperature, snow depth, and ground temperature in the Northern Hemisphere V. Tran et al. 10.1038/s41597-024-03483-x
32. 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
33. Reconstructing MODIS normalized difference snow index product on Greenland ice sheet using spatiotemporal extreme gradient boosting model F. Ye et al. 10.1016/j.jhydrol.2024.132277
34. 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
35. Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions E. Enderlin et al. 10.1016/j.rse.2022.113307
36. 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