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Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests
Author(s) -
Wu Jin,
Rogers Alistair,
Albert Loren P.,
Ely Kim,
Prohaska Neill,
Wolfe Brett T.,
Oliveira Raimundo Cosme,
Saleska Scott R.,
Serbin Shawn P.
Publication year - 2019
Publication title -
new phytologist
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.742
H-Index - 244
eISSN - 1469-8137
pISSN - 0028-646X
DOI - 10.1111/nph.16029
Subject(s) - canopy , biosphere , atmospheric sciences , tropical forest , tropics , panama , environmental science , botany , biology , ecology , geology
Summary Understanding the pronounced seasonal and spatial variation in leaf carboxylation capacity ( V c,max ) is critical for determining terrestrial carbon cycling in tropical forests. However, an efficient and scalable approach for predicting V c,max is still lacking. Here the ability of leaf spectroscopy for rapid estimation of V c,max was tested. V c,max was estimated using traditional gas exchange methods, and measured reflectance spectra and leaf age in leaves sampled from tropical forests in Panama and Brazil. These data were used to build a model to predict V c,max from leaf spectra. The results demonstrated that leaf spectroscopy accurately predicts V c,max of mature leaves in Panamanian tropical forests ( R 2  = 0.90). However, this single‐age model required recalibration when applied to broader leaf demographic classes (i.e. immature leaves). Combined use of spectroscopy models for V c,max and leaf age enabled construction of the V c,max –age relationship solely from leaf spectra, which agreed with field observations. This suggests that the spectroscopy technique can capture the seasonal variability in V c,max , assuming sufficient sampling across diverse species, leaf ages and canopy environments. This finding will aid development of remote sensing approaches that can be used to characterize V c,max in moist tropical forests and enable an efficient means to parameterize and evaluate terrestrial biosphere models.

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