70 citations as recorded by crossref.

1. High-altitude meteorology of Indian Himalayan Region: complexities, effects, and resolutions J. Yadav et al. https://doi.org/10.1007/s10661-021-09418-y
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3. Machine Learning-Based Estimation of High-Resolution Snow Depth in Alaska Using Passive Microwave Remote Sensing Data S. Tanniru & R. Ramsankaran https://doi.org/10.1109/JSTARS.2023.3287410
4. A New Geostationary Satellite-Based Snow Cover Recognition Method for FY-4A AGRI H. Qiao et al. https://doi.org/10.1109/JSTARS.2021.3125015
5. Cloud gap filling for continuous normalized difference snow index snow cover using the dynamic seasonally recurrent snow depletion pattern over a mountainous watershed C. Woodruff et al. https://doi.org/10.1016/j.rsase.2026.102062
6. A Two-Stage Fusion Framework to Generate a Spatio–Temporally Continuous MODIS NDSI Product over the Tibetan Plateau Y. Jing et al. https://doi.org/10.3390/rs11192261
7. Estimating Regional Snow Line Elevation Using Public Webcam Images C. Portenier et al. https://doi.org/10.3390/rs14194730
8. Spatiotemporal Reconstruction of MODIS Normalized Difference Snow Index Products Using U-Net with Partial Convolutions D. Xing et al. https://doi.org/10.3390/rs14081795
9. Investigating spatial-temporal trend of snow cover over the three provinces of Northeast China based on a cloud-free MODIS snow cover product Z. Gao et al. https://doi.org/10.1016/j.jhydrol.2024.132044
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11. Comparison of Three Different Random Forest Approaches to Retrieve Daily High-Resolution Snow Cover Maps from MODIS and Sentinel-2 in a Mountain Area, Gran Paradiso National Park (NW Alps) C. Richiardi et al. https://doi.org/10.3390/rs15020343
12. Snow Cover Variability and Trends over Karakoram, Western Himalaya and Kunlun Mountains During the MODIS Era (2001–2024) C. Almagioni et al. https://doi.org/10.3390/rs17050914
13. Lake water and glacier mass gains in the northwestern Tibetan Plateau observed from multi-sensor remote sensing data: Implication of an enhanced hydrological cycle G. Zhang et al. https://doi.org/10.1016/j.rse.2019.111554
14. A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling S. Chen et al. https://doi.org/10.3390/rs12213577
15. Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning L. Zhu et al. https://doi.org/10.3390/w15213726
16. Detection of Wet Snow by Weakly Supervised Deep Learning Change Detection Algorithm with Sentinel-1 Data H. Gong et al. https://doi.org/10.3390/rs16193575
17. Improved cloudy-sky snow albedo estimates using passive microwave and VIIRS data A. Jia et al. https://doi.org/10.1016/j.isprsjprs.2023.01.004
18. Development of a cloud-free MODIS NDSI dataset (2001–2020) over Northeast China H. Guo et al. https://doi.org/10.1080/17538947.2024.2398062
19. Investigating snow cover duration changes based on a cloud-free snow cover product developed using a spatiotemporal cloud removal method for Northeast China D. Yan & Y. Zhang https://doi.org/10.1080/17538947.2025.2497520
20. Multilevel Mapping From Remote Sensing Images: A Case Study of Urban Buildings Y. Shen et al. https://doi.org/10.1109/TGRS.2021.3062751
21. MAT-MS: A mask-aware transformer for constructing gap-free MODIS normalized difference snow index products J. Xu et al. https://doi.org/10.1016/j.isprsjprs.2025.07.004
22. A Spatial–Frequency Combined Transformer for Cloud Removal of Optical Remote Sensing Images F. Zhao et al. https://doi.org/10.3390/rs17091499
23. Spatial and Temporal Adaptive Gap-Filling Method Producing Daily Cloud-Free NDSI Time Series S. Chen et al. https://doi.org/10.1109/JSTARS.2020.2993037
24. Cloud Removal Algorithm by Combining MODIS and VIIRS Snow Products X. Wang et al. https://doi.org/10.1109/JSTARS.2025.3590441
25. Evaluating MODIS cloud-free snow cover datasets using massive spatial benchmark data in the Tibetan Plateau Y. Gao et al. https://doi.org/10.1016/j.scitotenv.2024.175245
26. Generative deep learning models for cloud removal in satellite imagery: A comparative review of GANs and diffusion methods S. Edirisinghe et al. https://doi.org/10.1016/j.ophoto.2025.100110
27. STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) for China Y. Jing et al. https://doi.org/10.5194/essd-14-3137-2022
28. Improving Fractional Snow Cover Retrieval From Passive Microwave Data Using a Radiative Transfer Model and Machine Learning Method X. Xiao et al. https://doi.org/10.1109/TGRS.2021.3128524
29. Monitoring the Spatiotemporal Dynamics of Arctic Winter Snow/Ice with Moonlight Remote Sensing: Systematic Evaluation in Svalbard D. Liu et al. https://doi.org/10.3390/rs15051255
30. Evaluation of Earth System Models outputs over the continental Portuguese coast: A historical comparison between CMIP5 and CMIP6 H. Pereira et al. https://doi.org/10.1016/j.ocemod.2023.102207
31. Tracking spatio-temporal dynamics of harmful algal blooms using long-term MODIS observations of Chaohu Lake in China from 2000 to 2021 T. Zhou et al. https://doi.org/10.1016/j.ecolind.2022.109842
32. The Application of SWAT Model and Remotely Sensed Products to Characterize the Dynamic of Streamflow and Snow in a Mountainous Watershed in the High Atlas S. Taia et al. https://doi.org/10.3390/s23031246
33. Developing a New Parameterization Scheme of Temperature Lapse Rate for the Hydrological Simulation in a Glacierized Basin Based on Remote Sensing W. Song et al. https://doi.org/10.3390/rs14194973
34. Satellite observed spatiotemporal variability of snow cover and snow phenology over high mountain Asia from 2002 to 2021 Z. Tang et al. https://doi.org/10.1016/j.jhydrol.2022.128438
35. Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland A. Caparó Bellido & B. Rundquist https://doi.org/10.3390/rs13112045
36. Snow Cover Variability in the Greater Alpine Region in the MODIS Era (2000–2019) D. Fugazza et al. https://doi.org/10.3390/rs13152945
37. Accuracy assessment of cloud removal methods for Moderate-resolution Imaging Spectroradiometer (MODIS) snow data in the Tianshan Mountains, China Q. Wang et al. https://doi.org/10.1007/s40333-025-0098-3
38. Gauging the effects of the COVID-19 pandemic lockdowns on atmospheric pollution content in select countries J. Mandal & P. Patel https://doi.org/10.1016/j.rsase.2021.100551
39. Estimating Early Summer Snow Depth on Sea Ice Using a Radiative Transfer Model and Optical Satellite Data M. Wang & N. Oppelt https://doi.org/10.3390/rs15205016
40. Snow cover estimation from MODIS and Sentinel-1 SAR data using machine learning algorithms in the western part of the Tianshan Mountains Y. Liu et al. https://doi.org/10.1007/s11629-019-5723-1
41. Identifying areas of archaeological potential in the Swiss Alps using satellite-derived time-series of snow cover estimates C. Cornut et al. https://doi.org/10.1016/j.rsase.2022.100838
42. Daily Terra–Aqua MODIS cloud-free snow and Randolph Glacier Inventory 6.0 combined product (M*D10A1GL06) for high-mountain Asia between 2002 and 2019 S. Muhammad & A. Thapa https://doi.org/10.5194/essd-13-767-2021
43. Reservoir Ice Conditions from Multi-Sensor Remote Sensing and ERA5-Land: The Manicouagan Hydroelectric Reservoir Case Study G. Siles & R. Leconte https://doi.org/10.3390/hydrology10050108
44. A Novel Approach Based on a Hierarchical Multiresolution Analysis of Optical Time Series to Reconstruct the Daily High-Resolution Snow Cover Area V. Premier et al. https://doi.org/10.1109/JSTARS.2021.3103585
45. HMRFS–TP: long-term daily gap-free snow cover products over the Tibetan Plateau from 2002 to 2021 based on hidden Markov random field model Y. Huang et al. https://doi.org/10.5194/essd-14-4445-2022
46. A dataset of daily cloud-free remote sensing indices for the cryosphere in High Mountain Asia (2000–2023) X. SUN et al. https://doi.org/10.11922/11-6035.ncdc.2025.0030.zh
47. An Integrated Shadow-Adjusted Snow-Aging Index for Alpine Regions H. Li et al. https://doi.org/10.3390/rs12081249
48. Estimation and validation of standalone SCATSAT-1 derived snow cover area using different MODIS products S. Singh et al. https://doi.org/10.1080/10106049.2022.2142962
49. Development of Global Snow Cover—Trends from 23 Years of Global SnowPack S. Roessler & A. Dietz https://doi.org/10.3390/earth4010001
50. Reconstructing a Gap-Free MODIS Normalized Difference Snow Index Product Using a Long Short-Term Memory Network J. Hou et al. https://doi.org/10.1109/TGRS.2022.3178421
51. Simulation of Winter Wheat Gross Primary Productivity Incorporating Solar-Induced Chlorophyll Fluorescence X. Zhang et al. https://doi.org/10.3390/agronomy15092187
52. Evaluating the performance of random forest for large-scale flood discharge simulation L. Schoppa et al. https://doi.org/10.1016/j.jhydrol.2020.125531
53. Multi-source method for analysing ice cover phenology of high-altitude (High Tatra Mts.) lakes K. Hrivnáková et al. https://doi.org/10.5817/CPR2024-2-13
54. A Compilation of Snow Cover Datasets for Svalbard: A Multi-Sensor, Multi-Model Study H. Vickers et al. https://doi.org/10.3390/rs13102002
55. An Automatic Method to Detect Lake Ice Phenology Using MODIS Daily Temperature Imagery X. Zhang et al. https://doi.org/10.3390/rs13142711
56. Reconstructing MODIS normalized difference snow index product on Greenland ice sheet using spatiotemporal extreme gradient boosting model F. Ye et al. https://doi.org/10.1016/j.jhydrol.2024.132277
57. Advances and prospects in reconstruction approaches for snow cover mapping using polar-orbiting satellites J. Zhang et al. https://doi.org/10.3389/feart.2025.1649808
58. Gap-Filling of a MODIS Normalized Difference Snow Index Product Based on the Similar Pixel Selecting Algorithm: A Case Study on the Qinghai–Tibetan Plateau M. Li et al. https://doi.org/10.3390/rs12071077
59. A snow cover mapping algorithm based on a multitemporal dataset for GK-2A imagery S. Lee & J. Choi https://doi.org/10.1080/15481603.2022.2097395
60. Impacts of aspect on snow characteristics using remote sensing from 2000 to 2020 in Ajichai-Iran M. Goodarzi et al. https://doi.org/10.1016/j.coldregions.2022.103682
61. Contemporary Snow Changes in the Karakoram Region Attributed to Improved MODIS Data between 2003 and 2018 A. Thapa & S. Muhammad https://doi.org/10.3390/w12102681
62. A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection X. Wu et al. https://doi.org/10.1016/j.isprsjprs.2021.01.023
63. RETRACTED ARTICLE: Spatiotemporal Geospatial Modeling for 3D City Based on Edge Computing Using Statistical Framework Linear Injection Model Based on Multi-data Source Fusion X. Wang et al. https://doi.org/10.1007/s13369-021-05603-w
64. Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020 C. Pu et al. https://doi.org/10.3390/w16101364
65. A dataset of MODIS gap-filled fractional snow cover in the source area of Yellow River during 2000–2021 Y. YANG et al. https://doi.org/10.11922/11-6035.ncdc.2022.0005.zh
66. Assessment of snow cover variability and its sensitivity to hydrometeorological factors in the Karakoram and Himalayan region J. Dharpure et al. https://doi.org/10.1080/02626667.2021.1985125
67. Multi-Stage Frequency Attention Network for Progressive Optical Remote Sensing Cloud Removal C. Wu et al. https://doi.org/10.3390/rs16152867
68. Spatiotemporal snow cover characterization and its linkage with climate change over the Chenab river basin, western Himalayas J. Dharpure et al. https://doi.org/10.1080/15481603.2020.1821150
69. A harmonized 2000–2024 dataset of daily river ice concentration and annual phenology for major Arctic rivers J. Qiu et al. https://doi.org/10.5194/essd-18-2703-2026
70. Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia G. Deng et al. https://doi.org/10.3390/rs16010192