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

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.