Articles | Volume 14, issue 9
https://doi.org/10.5194/essd-14-4445-2022
© 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-14-4445-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
29 Sep 2022
Data description paper |
| 29 Sep 2022
| 29 Sep 2022
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
Yan Huang
CORRESPONDING AUTHOR
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Jiahui Xu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Jingyi Xu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Yelei Zhao
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Bailang Yu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Hongxing Liu
Department of Geography, the University of Alabama, Tuscaloosa, AL
35487, USA
Shujie Wang
Department of Geography, Earth and Environmental Systems Institute,
Pennsylvania State University, University Park, PA 16802, USA
Wanjia Xu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Jianping Wu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Zhaojun Zheng
National Satellite Meteorological Center, Beijing 100081, China
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Cited
12 citations as recorded by crossref.
- Sensitivities of hydrological processes under climate warming and Landuse/landcover change in the Lhasa basin, Tibetan plateau K. Guédé et al. 10.1016/j.ejrh.2024.101731
- Improved snow depth estimation on the Tibetan Plateau using AMSR2 and ensemble learning models Q. Gu et al. 10.1016/j.jag.2024.104102
- Using music and music‐based analysis to model and to classify terrain in geomorphology S. Lin et al. 10.1002/esp.5785
- Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020 C. Pu et al. 10.3390/w16101364
- Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia G. Deng et al. 10.3390/rs16010192
- A new snow cover mapping algorithm for Chinese geostationary meteorological satellite FY-4A AGRI data L. He et al. 10.1080/17538947.2024.2367086
- Imbalance in lake variability but not embodying driving factors on the Qinghai-Tibetan Plateau calls on heterogeneous lake management X. Leng et al. 10.1016/j.jenvman.2023.119887
- Development of a Daily Cloud-Free Snow-Cover Dataset Using MODIS-Based Snow-Cover Probability for High Mountain Asia during 2000–2020 D. Yan et al. 10.3390/rs16162956
- Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022 J. Xu et al. 10.5194/tc-18-1817-2024
- MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022) F. Pan et al. 10.5194/essd-16-2501-2024
- Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022 J. Xu et al. 10.5194/tc-18-1817-2024
- Evaluation of Vegetation Indexes and Green-Up Date Extraction Methods on the Tibetan Plateau J. Xu et al. 10.3390/rs14133160
10 citations as recorded by crossref.
- Sensitivities of hydrological processes under climate warming and Landuse/landcover change in the Lhasa basin, Tibetan plateau K. Guédé et al. 10.1016/j.ejrh.2024.101731
- Improved snow depth estimation on the Tibetan Plateau using AMSR2 and ensemble learning models Q. Gu et al. 10.1016/j.jag.2024.104102
- Using music and music‐based analysis to model and to classify terrain in geomorphology S. Lin et al. 10.1002/esp.5785
- Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020 C. Pu et al. 10.3390/w16101364
- Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia G. Deng et al. 10.3390/rs16010192
- A new snow cover mapping algorithm for Chinese geostationary meteorological satellite FY-4A AGRI data L. He et al. 10.1080/17538947.2024.2367086
- Imbalance in lake variability but not embodying driving factors on the Qinghai-Tibetan Plateau calls on heterogeneous lake management X. Leng et al. 10.1016/j.jenvman.2023.119887
- Development of a Daily Cloud-Free Snow-Cover Dataset Using MODIS-Based Snow-Cover Probability for High Mountain Asia during 2000–2020 D. Yan et al. 10.3390/rs16162956
- Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022 J. Xu et al. 10.5194/tc-18-1817-2024
- MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022) F. Pan et al. 10.5194/essd-16-2501-2024
2 citations as recorded by crossref.
Latest update: 20 Nov 2024
Short summary
Reliable snow cover information is important for understating climate change and hydrological cycling. We generate long-term daily gap-free snow products over the Tibetan Plateau (TP) at 500 m resolution from 2002 to 2021 based on the hidden Markov random field model. The accuracy is 91.36 %, and is especially improved during snow transitional period and over complex terrains. This dataset has great potential to study climate change and to facilitate water resource management in the TP.
Reliable snow cover information is important for understating climate change and hydrological...
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