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Global Distribution of C4 vegetation

Using a photosynthetic optimality theory, in combination with observations from the TRY database and the global nutrient network, and remote sensing, we produced a new observation-constrained estmiate of global C4 distribution (including C4 natural grasses and C4 crops) from 2001 to 2019. We found globally C4 accounted for ~17% of the vegetated surface area and ~19.5% of global photosynthetic carbon assimilation. The global C4 distribution decreased from 17.7% of the global land surface to 17.1% during the study period, as a result of a decrease in C4 natural grasses (due to elevated CO2) and an increase in C4 crops (due to maize expansion). 

[Link to data]


Luo, X., Zhou, H., Satriawan, T.W., Tian, J., Zhao, R., Keenan, T.F., Griffith, D. M., Sitch, S. Smith, N.G. & Still, C.J. (2024). Mapping the global distribution of C4 vegetation using observations and optimality theory. Nature Communications. DOI: 10.1038/s41467-024-45606-3.


Global distribution of leaf photosynthetic capacity

Using leaf trait databases, remote sensing leaf chlorophyll content, gridded climate and soil data, in combination with a Random Forest approach, we derived one of the first data-driven maps of the maximum leaf carboxylate rate (Vcmax25) and the fraction of leaf nitrogen allocated to Rubisco (fLNR) - both are critical to the estimation of plant photosynthesis. This dataset can be used to constrain global photosynthesis and the carbon cycle estimates from Earth system models.

[Link to data]


Luo, X., Keenan, T.F., Chen, J.M., Croft, H., Prentice, I.C., Smith, N.G., Walker, A.P., Wang, H., Wang, R., Xu, C. & Zhang, Y. (2021) Global variation in the fraction of leaf nitrogen allocated to photosynthesis. Nature Communications. DOI: 10.1038/s41467-021-25163-9.


Biosphere-atmosphere Exchange Process Simulator​ (BEPS)

BEPS is a two-leaf enzyme kinetic Terrestrial Biosphere Model that is used to simulate hourly and daily gross primary productivity, evapotranspiration and net primary productivity. It has been intensively validated on a wide range of biomes and participated in North American Carbon Program. The global estimates of GPP and ET by BEPS are available from 2001 to 2020 [Link to data].

BEPS version 4.11 [Link to code].


Chen, J. M., Liu, J., Cihlar, J., & Goulden, M .(1999). Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecological Modelling. 124(2–3), 99–119. 

Luo, X., Chen, J.M., Liu, J., Black, T.A., Croft, H., Staebler, R., He, L., Arain, M.A., Chen, B., Mo, G., Gonsamo, A. & McCaughey, H. (2018). Comparison of big-leaf, two-big-leaf and two-leaf upscaling schemes for evapotranspiration estimation using coupled carbon-water modelling. Journal of Geophysical Research - Biogeoscience. DOI: 10.1002/2017JG003978.

Leng, J., Chen, J.M., Li, W., Luo, X., Xu, M., Liu, J., Wang, R., Rogers, C., Li, B. & Yan Y. (2024). Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations. Earth System Science Data. DOI: 10.5194/essd-16-1283-2024.


Satellite-derived Leaf Chlorophyll Content

Global leaf chlorophyll content (Chl) were derived from MERIS surface reflectance, using a two-step process-based algorithm. The first step was to retrieve leaf reflectance spectra from satellite-derived canopy reflectance spectra through the inversion of canopy radiative transfer models (4-Scale and SAIL). The second step was to use the retrieved leaf reflectance spectra from step 1 to estimate Chl by inverting a leaf radiative transfer model (PROSPECT). 

Satellite-derived Chl v1.0 for 124 eddy covariance sites [Link to data].


Croft, H., Chen, J.M., Mo, G., Luo, S., Luo, X., Arabian, J., Zhang, Y., Simic, A., Noland, T.L., He, Y., Homolová, L., Malenovský, Z., Yi, Q., Beringer, J., Amiri, R., Hutley, L., Arellano, P., Stahl, C. & Bonal, D. (2020). Global distribution of leaf chlorophyll content. Remote Sensing of Environment. DOI: 10.1016/j.rse.2019.111479.

Luo, X., Croft, H., Chen, J.M., He, L. & Keenan, T.F. (2019) Improved estimates of global photosynthesis using information on leaf chlorophyll content. Global Change Biology. DOI: 10.1111/gcb.14624.

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