Articles | Volume 16, issue 1
https://doi.org/10.5194/essd-16-277-2024
© 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/essd-16-277-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Mapping 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020
Mengyao Zhu
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 101408, China
China-Pakistan Joint Research Center on Earth Sciences, CAS-HEC, Islamabad, 45320, Pakistan
Huanjiong Wang
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Juha M. Alatalo
Environmental Science Centre, Qatar University, Doha, 2713, Qatar
Wei Liu
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 101408, China
Yulong Hao
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 101408, China
Quansheng Ge
CORRESPONDING AUTHOR
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 101408, China
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Cited
11 citations as recorded by crossref.
- Selecting of global phenological field observations for validating coarse AVHRR-derived forest phenology products based on spatial heterogeneity and temporal consistency Q. Shao et al. https://doi.org/10.1016/j.ecoinf.2025.103216
- Effects of Climatic Fluctuations on the First Flowering Date and Its Thermal Requirements for 28 Ornamental Plants in Xi’an, China W. Huang et al. https://doi.org/10.3390/horticulturae11070772
- A comprehensive evaluation framework for climate effect on plant viewing activities X. Gao et al. https://doi.org/10.1007/s00484-025-03029-9
- Enhancing broad-scale prediction of flowering onset by incorporating spatial heterogeneity in heat accumulation threshold J. Jin et al. https://doi.org/10.1007/s11676-026-02004-3
- Emerging evidence for delaying effect of winter warming on green-up onset in alpine grasslands on the Tibetan Plateau S. Wu et al. https://doi.org/10.1016/j.agrformet.2025.110586
- Mapping temperate groundwater-dependent terrestrial ecosystems in Denmark D. Christiansen et al. https://doi.org/10.1016/j.jhydrol.2026.136067
- Drought legacies delay spring green-up in northern ecosystems Y. Liu et al. https://doi.org/10.1038/s41558-025-02273-6
- Spatiotemporal Variations in Vegetation Phenology in the Qinling Mountains and Their Responses to Climate Variability H. Li et al. https://doi.org/10.3390/rs17244051
- Tree growth response and adaptation to climate change and climate extremes: From canopy to stem F. Yang et al. https://doi.org/10.1111/jipb.70145
- AI-Powered Plant Science: Transforming Forestry Monitoring, Disease Prediction, and Climate Adaptation Z. Xu & D. Jiang https://doi.org/10.3390/plants14111626
- Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review M. Debbagh et al. https://doi.org/10.1016/j.plaphe.2025.100089
11 citations as recorded by crossref.
- Selecting of global phenological field observations for validating coarse AVHRR-derived forest phenology products based on spatial heterogeneity and temporal consistency Q. Shao et al. https://doi.org/10.1016/j.ecoinf.2025.103216
- Effects of Climatic Fluctuations on the First Flowering Date and Its Thermal Requirements for 28 Ornamental Plants in Xi’an, China W. Huang et al. https://doi.org/10.3390/horticulturae11070772
- A comprehensive evaluation framework for climate effect on plant viewing activities X. Gao et al. https://doi.org/10.1007/s00484-025-03029-9
- Enhancing broad-scale prediction of flowering onset by incorporating spatial heterogeneity in heat accumulation threshold J. Jin et al. https://doi.org/10.1007/s11676-026-02004-3
- Emerging evidence for delaying effect of winter warming on green-up onset in alpine grasslands on the Tibetan Plateau S. Wu et al. https://doi.org/10.1016/j.agrformet.2025.110586
- Mapping temperate groundwater-dependent terrestrial ecosystems in Denmark D. Christiansen et al. https://doi.org/10.1016/j.jhydrol.2026.136067
- Drought legacies delay spring green-up in northern ecosystems Y. Liu et al. https://doi.org/10.1038/s41558-025-02273-6
- Spatiotemporal Variations in Vegetation Phenology in the Qinling Mountains and Their Responses to Climate Variability H. Li et al. https://doi.org/10.3390/rs17244051
- Tree growth response and adaptation to climate change and climate extremes: From canopy to stem F. Yang et al. https://doi.org/10.1111/jipb.70145
- AI-Powered Plant Science: Transforming Forestry Monitoring, Disease Prediction, and Climate Adaptation Z. Xu & D. Jiang https://doi.org/10.3390/plants14111626
- Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review M. Debbagh et al. https://doi.org/10.1016/j.plaphe.2025.100089
Saved (final revised paper)
Latest update: 05 Aug 2026
Short summary
This study utilized 24,552 in situ phenology observation records from the Chinese Phenology Observation Network to model and map 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020. These phenology maps are the first gridded, independent and reliable phenology data sources for China, offering a high spatial resolution of 0.1° and an average deviation of about 10 days. It contributes to more comprehensive research on plant phenology and climate change.
This study utilized 24,552 in situ phenology observation records from the Chinese Phenology...
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