Global datasets of leaf photosynthetic capacity for ecological and earth system research
<p>The maximum rate of Rubisco carboxylation (<span class="inline-formula"><i>V</i><sub>cmax</sub></span>) determines leaf photosynthetic capacity and is a key parameter for estimating the terrestrial carbon cycle, but its spatial information is la...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-09-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/14/4077/2022/essd-14-4077-2022.pdf |
| Summary: | <p>The maximum rate of Rubisco carboxylation (<span class="inline-formula"><i>V</i><sub>cmax</sub></span>)
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 <span class="inline-formula"><i>V</i><sub>cmax</sub></span>, based
on plants' optimal distribution of nitrogen between light harvesting and
carboxylation pathways. We also derive <span class="inline-formula"><i>V</i><sub>cmax</sub></span> from satellite (GOME-2)
observations of sun-induced chlorophyll fluorescence (SIF) as a proxy of
leaf photosynthesis using a data assimilation technique. These two
independent global <span class="inline-formula"><i>V</i><sub>cmax</sub></span> products agree well (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M5" display="inline" overflow="scroll" dspmath="mathml"><mrow><msup><mi>r</mi><mn mathvariant="normal">2</mn></msup><mo>=</mo><mn mathvariant="normal">0.79</mn><mo>,</mo><mi mathvariant="normal">RMSE</mi><mo>=</mo><mn mathvariant="normal">15.46</mn><mspace width="0.125em" linebreak="nobreak"/><mrow class="unit"><mi mathvariant="normal">µ</mi></mrow></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="129pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="1cb08b9869c7d3facff26f397ef438e1"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-14-4077-2022-ie00001.svg" width="129pt" height="15pt" src="essd-14-4077-2022-ie00001.png"/></svg:svg></span></span>mol m<span class="inline-formula"><sup>−2</sup></span> s<span class="inline-formula"><sup>−1</sup></span>, <span class="inline-formula"><i>P</i><i><</i>0.001</span>) and compare well
with 3672 ground-based measurements (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M9" display="inline" overflow="scroll" dspmath="mathml"><mrow><msup><mi>r</mi><mn mathvariant="normal">2</mn></msup><mo>=</mo><mn mathvariant="normal">0.69</mn><mo>,</mo><mi mathvariant="normal">RMSE</mi><mo>=</mo><mn mathvariant="normal">13.8</mn><mspace linebreak="nobreak" width="0.125em"/><mrow class="unit"><mi mathvariant="normal">µ</mi></mrow></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="123pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="e4b1cd6460782f3283fc52e81199344e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-14-4077-2022-ie00002.svg" width="123pt" height="15pt" src="essd-14-4077-2022-ie00002.png"/></svg:svg></span></span>mol m<span class="inline-formula"><sup>−2</sup></span> s<span class="inline-formula"><sup>−1</sup></span> and <span class="inline-formula"><i>P</i><i><</i>0.001</span> for SIF; <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M13" display="inline" overflow="scroll" dspmath="mathml"><mrow><msup><mi>r</mi><mn mathvariant="normal">2</mn></msup><mo>=</mo><mn mathvariant="normal">0.55</mn><mo>,</mo><mi mathvariant="normal">RMSE</mi><mo>=</mo><mn mathvariant="normal">18.28</mn><mrow class="unit"><mi mathvariant="normal">µ</mi></mrow></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="128pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="1e7590bac7c2176af37541ae765f8518"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-14-4077-2022-ie00003.svg" width="128pt" height="15pt" src="essd-14-4077-2022-ie00003.png"/></svg:svg></span></span>mol m<span class="inline-formula"><sup>−2</sup></span> s<span class="inline-formula"><sup>−1</sup></span> and <span class="inline-formula"><i>P</i><i><</i>0.001</span> for LCC). The LCC-derived
<span class="inline-formula"><i>V</i><sub>cmax</sub></span> product is also used to constrain the retrieval of <span class="inline-formula"><i>V</i><sub>cmax</sub></span> from TROPical Ozone Mission (TROPOMI) SIF data to produce an optimized <span class="inline-formula"><i>V</i><sub>cmax</sub></span> product using
both SIF and LCC information. The global distributions of these products are
compatible with <span class="inline-formula"><i>V</i><sub>cmax</sub></span> 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 <span class="inline-formula"><i>V</i><sub>cmax</sub></span> products are primed to play a major role in global
ecosystem research. The three remote sensing <span class="inline-formula"><i>V</i><sub>cmax</sub></span> products based on
SIF, LCC, and SIF<span class="inline-formula">+</span>LCC are available at <a href="https://doi.org/10.5281/zenodo.6466968">https://doi.org/10.5281/zenodo.6466968</a> (Chen et al., 2022), and the code
for implementing the ecological optimality theory is available at <span class="uri">https://github.com/SmithEcophysLab/optimal_vcmax_R</span> and <a href="https://doi.org/10.5281/zenodo.5899564">https://doi.org/10.5281/zenodo.5899564</a> (last access: 31 August 2022) (Smith et al., 2022).</p> |
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| ISSN: | 1866-3508 1866-3516 |