Articles | Volume 18, issue 4
https://doi.org/10.5194/tc-18-1817-2024
© Author(s) 2024. 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-18-1817-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022
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
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai, 200241, China
Yao Tang
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
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai, 200241, China
Linxin Dong
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
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai, 200241, China
Shujie Wang
Department of Geography, Earth and Environmental Systems Institute, Pennsylvania State University, University Park, PA, 16802, USA
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
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, 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
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai, 200241, China
Zhaojun Zheng
National Satellite Meteorological Center, Beijing, 100081, China
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
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai, 200241, China
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Cited
8 citations as recorded by crossref.
- A Novel Approach for Cloud-Free MODIS NDSI Reconstruction on the Tibetan Plateau Combining Spatiotemporal Cube and Environmental Features L. Dong et al. 10.1109/TGRS.2025.3542095
- The Impact of Autumn Snowfall on Vegetation Indices and Autumn Phenology Estimation Y. Tang et al. 10.3390/rs16244783
- Assessing the Response of the Net Primary Productivity to Snow Phenology Changes in the Tibetan Plateau: Trends and Environmental Drivers J. Liu et al. 10.3390/rs16193566
- Dynamic monitoring of eco-environmental quality in the Greater Mekong Subregion: Evolutionary characteristics and country differences C. Liu et al. 10.1016/j.eiar.2024.107700
- Snow Cover Variability and Trends over Karakoram, Western Himalaya and Kunlun Mountains During the MODIS Era (2001–2024) C. Almagioni et al. 10.3390/rs17050914
- Urban green spaces enhanced human thermal comfort through dual pathways of cooling and humidifying X. Yu et al. 10.1016/j.scs.2024.106032
- Spatiotemporal evolution of vegetation phenology and its response to environmental factors in the upper and middle reaches of the Yellow River Basin X. Li et al. 10.1016/j.jenvman.2025.124970
- A novel perspective on near-surface soil freeze states: Discontinuity of the freeze process X. Wang et al. 10.1016/j.geoderma.2025.117258
8 citations as recorded by crossref.
- A Novel Approach for Cloud-Free MODIS NDSI Reconstruction on the Tibetan Plateau Combining Spatiotemporal Cube and Environmental Features L. Dong et al. 10.1109/TGRS.2025.3542095
- The Impact of Autumn Snowfall on Vegetation Indices and Autumn Phenology Estimation Y. Tang et al. 10.3390/rs16244783
- Assessing the Response of the Net Primary Productivity to Snow Phenology Changes in the Tibetan Plateau: Trends and Environmental Drivers J. Liu et al. 10.3390/rs16193566
- Dynamic monitoring of eco-environmental quality in the Greater Mekong Subregion: Evolutionary characteristics and country differences C. Liu et al. 10.1016/j.eiar.2024.107700
- Snow Cover Variability and Trends over Karakoram, Western Himalaya and Kunlun Mountains During the MODIS Era (2001–2024) C. Almagioni et al. 10.3390/rs17050914
- Urban green spaces enhanced human thermal comfort through dual pathways of cooling and humidifying X. Yu et al. 10.1016/j.scs.2024.106032
- Spatiotemporal evolution of vegetation phenology and its response to environmental factors in the upper and middle reaches of the Yellow River Basin X. Li et al. 10.1016/j.jenvman.2025.124970
- A novel perspective on near-surface soil freeze states: Discontinuity of the freeze process X. Wang et al. 10.1016/j.geoderma.2025.117258
Latest update: 28 Mar 2025
Short summary
Understanding snow phenology (SP) and its possible feedback are important. We reveal spatiotemporal heterogeneous SP on the Tibetan Plateau (TP) and the mediating effects from meteorological, topographic, and environmental factors on it. The direct effects of meteorology on SP are much greater than the indirect effects. Topography indirectly effects SP, while vegetation directly effects SP. This study contributes to understanding past global warming and predicting future trends on the TP.
Understanding snow phenology (SP) and its possible feedback are important. We reveal...