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

Summary 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.