Preprints
https://doi.org/10.5194/essd-2022-136
https://doi.org/10.5194/essd-2022-136
 
29 Apr 2022
29 Apr 2022
Status: this preprint is currently under review for the journal ESSD.

Global Datasets of Leaf Photosynthetic Capacity for Ecological and Earth System Research

Jing M. Chen1,2, Rong Wang2, Yihong Liu1, Liming He3, Holly Croft4, Xiangzhong Luo5, Han Wang6, Nicholas G. Smith7, Trevor F. Keenan8,9, I. Colin Prentice6,10,11, Yongguang Zhang12, Weimin Ju12, and Ning Dong10,11 Jing M. Chen et al.
  • 1Department of Geography and Planning, University of Toronto
  • 2School of Geography, Fujian Normal University
  • 3Canada Centre for Remote Sensing, Natural Resources Canada
  • 4School of Biosciences, University of Sheffield, Sheffield, UK
  • 5Department of Geography, National University of Singapore
  • 6Department of Earth System Science, Tsinghua University, Beijing 100084, China
  • 7Department of Biological Sciences, Texas Tech University, Lubbock, TX, USA
  • 8Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
  • 9Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, USA
  • 10Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot SL5 7PY, UK
  • 11Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
  • 12International Institute of Earth System Science, Nanjing University, China

Abstract. The maximum rate of Rubisco carboxylation (Vcmax) determines leaf photosynthetic capacity and is a key parameter for estimating the terrestrial carbon cycle, but its spatial information is lacking, hindering global ecological research. Here, we convert leaf chlorophyll content (LCC) retrieved from satellite data to Vcmax, based on plants’ optimal distribution of nitrogen between light harvesting and carboxylation pathways. We also derive Vcmax from satellite observations of sun-induced chlorophyll fluorescence (SIF) as a proxy of leaf photosynthesis. These two independent global Vcmax products agree well (r2 = 0.79, RMSE = 15.46 μmol m-2 s-1, P < 0.001) and compare well with 3672 ground-based measurements (r2 = 0.68, RMSE = 13.55 μmol m-2 s-1 and P < 0.001 for SIF; r2 = 0.55, RMSE = 17.55 μmol m-2 s-1 and P < 0.001 for LCC). Through a data assimilation technique, these two types of Vcmax products from remote sensing are combined to provide an optimized Vcmax product. The global distributions of these products are compatible with Vcmax computed from an ecological optimality theory using meteorological variables, but importantly reveal additional information on the influence of land cover, irrigation, soil pH and leaf nitrogen on leaf photosynthetic capacity. These satellite-based approaches and spatial Vcmax products are primed to play a major role in global ecosystem research. The three remote sensing Vcmax products are available at https://doi.org/10.5281/zenodo.6466968 (Chen et al., 2020) and the code for implementing the ecological optimality theory is available at https://github.com/SmithEcophysLab/optimal_vcmax_R (Smith, 2020).

Jing M. Chen et al.

Status: open (until 24 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Jing M. Chen et al.

Data sets

Vcmax from global leaf chlorophyll content map (Croft et al., 2020, RSE): LCC_Vcmax_Tg_mean.mat Jing Chen, Rong Wang, and co-authors https://doi.org/10.5281/zenodo.6466967

Vcmax from TROPOMI SIF: TROPOMI_Vmax_Tg_mean.mat Jing Chen, Rong Wang, and coauthors https://doi.org/10.5281/zenodo.6466967

Vcmax from GOME-2 SIF: GOME2_Vcmax_Tg_05deg.tif Jing M. Chen, Rong Wang, and co-authors https://doi.org/10.5281/zenodo.6466967

Model code and software

Ecocology optimality model Nick Smith https://github.com/SmithEcophysLab/optimal_vcmax_R

Jing M. Chen et al.

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Short summary
Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit chlorophyll fluorescence as a byproduct. Both chlorophyll pigments and fluorescence can be measured by Earth-orbiting satellite sensors. Here we demonstrate that leaf photosynthetic capacity can be reliably derived globally using these measurements. This new satellite-based information overcomes a bottleneck in global ecological research where such spatially explicit information is currently lacking.