Articles | Volume 16, issue 4
https://doi.org/10.5194/essd-16-1771-2024
https://doi.org/10.5194/essd-16-1771-2024
Data description paper
 | 
11 Apr 2024
Data description paper |  | 11 Apr 2024

Spatial mapping of key plant functional traits in terrestrial ecosystems across China

Nannan An, Nan Lu, Weiliang Chen, Yongzhe Chen, Hao Shi, Fuzhong Wu, and Bojie Fu

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Cited articles

Ali, A. M., Darvishzadeh, R., Skidmore, A. K., van Duren, I., Heiden, U., and Heurich, M.: Estimating leaf functional traits by inversion of PROSPECT: Assessing leaf dry matter content and specific leaf area in mixed mountainous forest, Int. J. Appl. Earth Obs. Geoinf., 45, 66–76, https://doi.org/10.1016/j.jag.2015.11.004, 2016. 
An, N. N., Lu, N., Fu, B. J., Wang, M. Y., and He, N. P.: Distinct responses of leaf traits to environment and phylogeny between herbaceous and woody angiosperm species in China, Front. Plant Sci., 12, 799401, https://doi.org/10.3389/fpls.2021.799401, 2021. 
An, N. N., Lu, N., Chen, W. L., Chen, Y. Z., Shi, H., Wu, F. Z., and Fu, B. J.: Maps of plant functional traits with 1-km spatial resolution in terrestrial ecosystems across China, figshare [data set], https://doi.org/10.6084/m9.figshare.22351498, 2023. 
Bakker, M. A., Carreño-Rocabado, G., and Poorter, L.: Leaf economics traits predict litter decomposition of tropical plants and differ among land use types, Funct. Ecol., 25, 473–483, https://doi.org/10.1111/j.1365-2435.2010.01802.x, 2011. 
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This study generated a spatially continuous plant functional trait dataset (~1 km) in China in combination with field observations, environmental variables and vegetation indices using machine learning methods. Results showed that wood density, leaf P concentration and specific leaf area showed good accuracy with an average R2 of higher than 0.45. This dataset could provide data support for development of Earth system models to predict vegetation distribution and ecosystem functions.
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