Dynamic leaf energy balance: deriving stomatal conductance from thermal imaging in a dynamic environment

Abstract

In spite of the significant progress made in recent years, the use of thermography to derive biologically relevant traits remains a challenge under fluctuating conditions. The aim of this study was to rethink the current method to process thermograms and derive temporal responses of stomatal conductance (gsw) using dynamic energy balance equations. Time-series thermograms provided the basis for a spatial and temporal characterization of gsw responses in wheat (Triticum aestivum). A leaf replica with a known conductance was used to validate the approach and to test the ability of our model to be used with any material and under any environmental conditions. The results highlighted the importance of the co-ordinated stomatal responses that run parallel to the leaf blade despite their patchy distribution. The diversity and asymmetry of the temporal response of gsw observed after a step increase and step decrease in light intensity can be interpreted as a strategy to maximize photosynthesis per unit of water loss and avoid heat stress in response to light flecks in a natural environment. This study removes a major bottleneck for plant phenotyping platforms and will pave the way to further developments in our understanding of stomatal behaviour.

Plant phenotyping relevance

熱画像と動的エネルギー収支方程式から気孔コンダクタンスを推定する手法を開発し、既知コンダクタンスの葉レプリカで検証している。植物表現型取得法が中心である。

Code and data availability

The supplied blocks describe a dynamic leaf energy balance model with Bayesian inference in Stan, but contain no authors' public code repository, no deposited thermal image or phenotype dataset, and no supplement URL. The only URLs present (SUNDIALS ODE solver, Stan platform, StanCon ODE notebook) are generic third- or

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