Articles | Volume 14, issue 8
https://doi.org/10.5194/essd-14-3549-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-14-3549-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A new snow depth data set over northern China derived using GNSS interferometric reflectometry from a continuously operating network (GSnow-CHINA v1.0, 2013–2022)
Wei Wan
CORRESPONDING AUTHOR
Institute of Remote Sensing and GIS, School of Earth and Space
Sciences, Peking University, Beijing 100871, China
Jie Zhang
College of Oceanography and Space Informatics, China University of
Petroleum (East China), Qingdao 266580, China
Liyun Dai
Key Laboratory of Remote Sensing of Gansu Province, Northwest
Institute of Eco-Environment and Resources, Chinese Academy of Sciences,
Lanzhou 730000, China
Meteorological Observation Center, China Meteorological
Administration, Beijing 100081, China
Ting Yang
Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing, China
Baojian Liu
Institute of Remote Sensing and GIS, School of Earth and Space
Sciences, Peking University, Beijing 100871, China
Zhizhou Guo
Institute of Remote Sensing and GIS, School of Earth and Space
Sciences, Peking University, Beijing 100871, China
Heng Hu
Meteorological Observation Center, China Meteorological
Administration, Beijing 100081, China
Limin Zhao
Aerospace Information Research Institute, Chinese Academy of
Sciences, Beijing 100101, China
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Cited
13 citations as recorded by crossref.
- A 0.01° daily improved snow depth dataset for the Tibetan Plateau D. Yan & Y. Zhang https://doi.org/10.1016/j.jhydrol.2024.130706
- Toward Terrain Effects on GNSS Interferometric Reflectometry Snow Depth Retrievals: Geometries, Modeling, and Applications W. Wan et al. https://doi.org/10.1109/TGRS.2022.3215817
- Using GNSS-IR Snow Depth Estimation to Monitor the 2022 Early February Snowstorm over Southern China J. Zhang et al. https://doi.org/10.3390/rs14184530
- Predicting significant wave height in the South China Sea using the SAC-ConvLSTM model B. Hou et al. https://doi.org/10.3389/fmars.2024.1424714
- 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
- Improving the accuracy of soil organic matter mapping in typical Planosol areas based on prior knowledge and probability hybrid model D. Zang et al. https://doi.org/10.1016/j.still.2024.106358
- Satellite-Based Assessment of Snow Dynamics and Climatic Drivers in the Changbai Mountain Region (2001–2022) X. Hua et al. https://doi.org/10.3390/rs17030442
- Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach Y. Liu et al. https://doi.org/10.1080/17538947.2023.2299208
- Scale patterns of the Sentinel-1 SAR-based snow depth product compared with station measurements and airborne LiDAR observations J. Ying et al. https://doi.org/10.5194/tc-20-227-2026
- Estimating snow depth based on dual polarimetric radar index from Sentinel-1 GRD data: A case study in the Scandinavian Mountains T. Feng et al. https://doi.org/10.1016/j.jag.2024.103873
- Evaluating the performance of snow depth reanalysis products in the arid region of Central Asia L. Zhang et al. https://doi.org/10.1080/17538947.2024.2447368
- The applications of GNSS-IR (Global Navigation Satellite System Interferometric Reflectometry): a comprehensive review M. Abdelhamid & K. Maciuk https://doi.org/10.1016/j.asr.2025.05.080
- Snowpack absence drives seasonally divergent plant and microbial carbon pathways in peatland soils Y. Cai et al. https://doi.org/10.1016/j.catena.2026.110070
13 citations as recorded by crossref.
- A 0.01° daily improved snow depth dataset for the Tibetan Plateau D. Yan & Y. Zhang https://doi.org/10.1016/j.jhydrol.2024.130706
- Toward Terrain Effects on GNSS Interferometric Reflectometry Snow Depth Retrievals: Geometries, Modeling, and Applications W. Wan et al. https://doi.org/10.1109/TGRS.2022.3215817
- Using GNSS-IR Snow Depth Estimation to Monitor the 2022 Early February Snowstorm over Southern China J. Zhang et al. https://doi.org/10.3390/rs14184530
- Predicting significant wave height in the South China Sea using the SAC-ConvLSTM model B. Hou et al. https://doi.org/10.3389/fmars.2024.1424714
- 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
- Improving the accuracy of soil organic matter mapping in typical Planosol areas based on prior knowledge and probability hybrid model D. Zang et al. https://doi.org/10.1016/j.still.2024.106358
- Satellite-Based Assessment of Snow Dynamics and Climatic Drivers in the Changbai Mountain Region (2001–2022) X. Hua et al. https://doi.org/10.3390/rs17030442
- Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach Y. Liu et al. https://doi.org/10.1080/17538947.2023.2299208
- Scale patterns of the Sentinel-1 SAR-based snow depth product compared with station measurements and airborne LiDAR observations J. Ying et al. https://doi.org/10.5194/tc-20-227-2026
- Estimating snow depth based on dual polarimetric radar index from Sentinel-1 GRD data: A case study in the Scandinavian Mountains T. Feng et al. https://doi.org/10.1016/j.jag.2024.103873
- Evaluating the performance of snow depth reanalysis products in the arid region of Central Asia L. Zhang et al. https://doi.org/10.1080/17538947.2024.2447368
- The applications of GNSS-IR (Global Navigation Satellite System Interferometric Reflectometry): a comprehensive review M. Abdelhamid & K. Maciuk https://doi.org/10.1016/j.asr.2025.05.080
- Snowpack absence drives seasonally divergent plant and microbial carbon pathways in peatland soils Y. Cai et al. https://doi.org/10.1016/j.catena.2026.110070
Saved (final revised paper)
Latest update: 30 May 2026
Short summary
The GSnow-CHINA data set is a snow depth data set developed using the two Global Navigation Satellite System station networks in China. It includes snow depth of 24, 12, and 2/3/6 h records, if possible, for 80 sites from 2013–2022 over northern China (25–55° N, 70–140° E). The footprint of the data set is ~ 1000 m2, and it can be used as an independent data source for validation purposes. It is also useful for regional climate research and other meteorological and hydrological applications.
The GSnow-CHINA data set is a snow depth data set developed using the two Global Navigation...
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