Articles | Volume 16, issue 1
https://doi.org/10.5194/essd-16-277-2024
https://doi.org/10.5194/essd-16-277-2024
Data description paper
 | 
11 Jan 2024
Data description paper |  | 11 Jan 2024

Mapping 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020

Mengyao Zhu, Junhu Dai, Huanjiong Wang, Juha M. Alatalo, Wei Liu, Yulong Hao, and Quansheng Ge

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Cited articles

An, S., Chen, X., Zhang, X., Lang, W., Ren, S., and Xu, L.: Precipitation and minimum temperature are primary climatic controls of alpine grassland autumn phenology on the Qinghai-Tibet Plateau, Remote Sens., 12, 431, https://doi.org/10.3390/rs12030431, 2020. 
Aono, Y. and Kazui, K.: Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th century, Int. J. Climatol., 28, 905–914, https://doi.org/10.1002/joc.1594, 2008. 
Ault, T. R., Schwartz, M. D., Zurita-Milla, R., Weltzin, J. F., and Betancourt, J. L.: Trends and natural variability of spring onset in the coterminous United States as evaluated by a new gridded dataset of spring indices, J. Climate, 28, 8363–8378, https://doi.org/10.1175/jcli-d-14-00736.1, 2015. 
Basler, D.: Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe, Agric. For. Meteorol., 217, 10–21, https://doi.org/10.1016/j.agrformet.2015.11.007, 2016. 
Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., and Friedl, M. A.: Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery, Remote Sens. Environ., 240, 111685, https://doi.org/10.1016/j.rse.2020.111685, 2020. 
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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.
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