Articles | Volume 14, issue 6
https://doi.org/10.5194/tc-14-1763-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-14-1763-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jun 2020
Research article |
| 03 Jun 2020
| 03 Jun 2020
Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach
Jianwei Yang
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of
Chinese Academy of Sciences, Beijing Engineering Research Center for Global
Land Remote Sensing Products, Faculty of Geographical Science, Beijing
Normal University, Beijing 100875, China
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of
Chinese Academy of Sciences, Beijing Engineering Research Center for Global
Land Remote Sensing Products, Faculty of Geographical Science, Beijing
Normal University, Beijing 100875, China
Kari Luojus
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki,
Finland
Jinmei Pan
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Juha Lemmetyinen
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki,
Finland
Matias Takala
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki,
Finland
Shengli Wu
National Satellite Meteorological Center, China Meteorological
Administration, Beijing 100081, China
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29 citations as recorded by crossref.
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- 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
- Developments and Future Strategies of Earth Science from Space in China J. SHI et al. 10.11728/cjss2021.01.095
- An improvement of snow/cloud discrimination from machine learning using geostationary satellite data D. Jin et al. 10.1080/17538947.2022.2152886
- 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
- 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
- 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
- Mountain Snow Depth Retrieval From Optical and Passive Microwave Remote Sensing Using Machine Learning C. Xiong et al. 10.1109/LGRS.2022.3226204
- Snow depth variability across the Qinghai Plateau and its influencing factors during 1980–2018 H. Ma et al. 10.1002/joc.7883
- 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
- Variation of Snow Mass in a Regional Climate Model Downscaling Simulation Covering the Tianshan Mountains, Central Asia T. Yang et al. 10.1029/2020JD034183
- 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
- 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
- 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
- Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications S. Schilling et al. 10.3390/rs16061085
- 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
- Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere Y. Hu et al. 10.3390/rs13071250
- 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
- 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
- Spatiotemporal Changes of Snow Depth in Western Jilin, China from 1987 to 2018 Y. Wei et al. 10.1007/s11769-023-1400-y
- 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
- 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
- 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
- Passive Microwave Remote Sensing of Snow Depth: Techniques, Challenges and Future Directions S. Tanniru & R. Ramsankaran 10.3390/rs15041052
- A past and present perspective on the European summer vapor pressure deficit V. Nagavciuc et al. 10.5194/cp-20-573-2024
- A 0.01° daily improved snow depth dataset for the Tibetan Plateau D. Yan & Y. Zhang 10.1016/j.jhydrol.2024.130706
- 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
- 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
- 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
29 citations as recorded by crossref.
- 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
- 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
- Developments and Future Strategies of Earth Science from Space in China J. SHI et al. 10.11728/cjss2021.01.095
- An improvement of snow/cloud discrimination from machine learning using geostationary satellite data D. Jin et al. 10.1080/17538947.2022.2152886
- 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
- 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
- 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
- Mountain Snow Depth Retrieval From Optical and Passive Microwave Remote Sensing Using Machine Learning C. Xiong et al. 10.1109/LGRS.2022.3226204
- Snow depth variability across the Qinghai Plateau and its influencing factors during 1980–2018 H. Ma et al. 10.1002/joc.7883
- 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
- Variation of Snow Mass in a Regional Climate Model Downscaling Simulation Covering the Tianshan Mountains, Central Asia T. Yang et al. 10.1029/2020JD034183
- 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
- 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
- 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
- Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications S. Schilling et al. 10.3390/rs16061085
- 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
- Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere Y. Hu et al. 10.3390/rs13071250
- 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
- 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
- Spatiotemporal Changes of Snow Depth in Western Jilin, China from 1987 to 2018 Y. Wei et al. 10.1007/s11769-023-1400-y
- 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
- 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
- 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
- Passive Microwave Remote Sensing of Snow Depth: Techniques, Challenges and Future Directions S. Tanniru & R. Ramsankaran 10.3390/rs15041052
- A past and present perspective on the European summer vapor pressure deficit V. Nagavciuc et al. 10.5194/cp-20-573-2024
- A 0.01° daily improved snow depth dataset for the Tibetan Plateau D. Yan & Y. Zhang 10.1016/j.jhydrol.2024.130706
- 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
- 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
- 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
Latest update: 18 Apr 2024
Short summary
There are many challenges for accurate snow depth estimation using passive microwave data. Machine learning (ML) techniques are deemed to be powerful tools for establishing nonlinear relations between independent variables and a given target variable. In this study, we investigate the potential capability of the random forest (RF) model on snow depth estimation at temporal and spatial scales. The result indicates that the fitted RF algorithms perform better on temporal than spatial scales.
There are many challenges for accurate snow depth estimation using passive microwave data....
