DynG: a dynamic scaling factor for thermographic stomatal conductance estimation under changing environmental conditions.

Abstract

Summary Thermal imaging is a key plant phenotyping and monitoring technique but faces major bottlenecks in accurately and efficiently inferring stomatal conductance (gsw) from leaf temperature. The conductance index (Ig) was previously proposed to estimate gsw from thermography by linking temperature differences between real and artificial leaves (ALs) based on the leaf energy balance. However, Ig is highly sensitive to environmental fluctuations, hampering interpretation and reducing reproducibility. We developed a simple and novel correction factor (named DynG) for Ig that accounts for environmental fluctuations when scaling Ig to gsw. This was achieved by capturing temperature variations in a set of ALs with a range of known constant pore conductances. This approach provided the Ig–conductance relationship, using ALs as a reference, to infer gsw of real leaves from their measured Ig. In fluctuating environments, gsw estimated using DynG showed greater accuracy and stability than gsw calculated from Ig alone, and was in good agreement with gsw determined using lysimetric and gas exchange methods. DynG's power was further showcased in distinguishing gsw of Arabidopsis genotypes differing in stomatal traits (Col‐0, epf1epf2, and EPF2OE). We conclude that Ig corrected with DynG can reliably estimate gsw in fluctuating environments without complex modeling, opening new avenues for gsw phenotyping and monitoring.

Plant phenotyping relevance

熱画像から気孔コンダクタンスを推定する補正係数を開発し、変動環境下で既存法と比較検証した、植物フェノタイピング手法の中心的研究である。

Code and data availability

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