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

## TL;DR

Researchers developed a new correction factor called DynG to improve the accuracy of estimating plant stomatal conductance using thermal imaging in changing environmental conditions.

## Contribution

The novel DynG correction factor enhances the reliability of stomatal conductance estimation from thermal imaging under fluctuating environments.

## Key findings

- DynG improved the accuracy and stability of stomatal conductance estimates compared to the original method.
- DynG estimates matched well with those from lysimetric and gas exchange methods.
- DynG successfully distinguished stomatal conductance in different Arabidopsis genotypes.

## Abstract

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

Thermal imaging is a key plant phenotyping and monitoring technique but faces major bottlenecks in accurately and efficiently inferring stomatal conductance (g
sw) from leaf temperature. The conductance index (I
g) was previously proposed to estimate g
sw from thermography by linking temperature differences between real and artificial leaves (ALs) based on the leaf energy balance. However, I
g is highly sensitive to environmental fluctuations, hampering interpretation and reducing reproducibility.

We developed a simple and novel correction factor (named DynG) for I
g that accounts for environmental fluctuations when scaling I
g to g
sw. This was achieved by capturing temperature variations in a set of ALs with a range of known constant pore conductances. This approach provided the I
g–conductance relationship, using ALs as a reference, to infer g
sw of real leaves from their measured I
g.

In fluctuating environments, g
sw estimated using DynG showed greater accuracy and stability than g
sw calculated from I
g alone, and was in good agreement with g
sw determined using lysimetric and gas exchange methods. DynG's power was further showcased in distinguishing g
sw of Arabidopsis genotypes differing in stomatal traits (Col‐0, epf1epf2, and EPF2OE).

We conclude that I
g corrected with DynG can reliably estimate g
sw in fluctuating environments without complex modeling, opening new avenues for g
sw phenotyping and monitoring.