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Updated: Jun 28, 2025

Relating Stomatal Conductance to Leaf Functional Traits
Published on: October 12, 2015
Practical Considerations and Limitations of Using Leaf and Canopy Temperature Measurements as a Stomatal Conductance
Ismael K Mayanja1, Christine H Diepenbrock2, Vincent Vadez3
1Department of Biological Systems Engineering, University of California, Davis, Davis, CA, USA.
Abstract:
Stomatal conductance (gs) is a crucial component of plant physiology, as it links plant productivity and water loss through transpiration. Estimating gs indirectly through leaf temperature (Tl) measurement is common for reducing the high labor cost associated with direct gs measurement. However, the relationship between observed Tl and gs can be notably affected by local environmental conditions, canopy structure, measurement scale, sample size, and gs itself. To better understand and quantify the variation in the relationship between Tl measurements to gs, this study analyzed the sensitivity of Tl to gs using a high-resolution three-dimensional model that resolves interactions between microclimate and canopy structure. The model was used to simulate the sensitivity of Tl to gs across different environmental conditions, aggregation scales (point measurement, infrared thermometer, and thermographic image), and sample sizes. Results showed that leaf-level sensitivity of Tl to gs was highest under conditions of high net radiation flux, high vapor pressure deficit, and low boundary layer conductance. The study findings also highlighted the trade-off between measurement scale and sample size to maximize sensitivity. Smaller scale measurements (e.g., thermocouple) provided maximal sensitivity because they allow for exclusion of shaded leaves and the ground, which have low sensitivity. However, large sample sizes (up to 50 to 75) may be needed to differentiate genotypes. Larger-scale measurements (e.g., thermal camera) reduced sample size requirements but include low-sensitivity elements in the measurement. This work provides a means of estimating leaf-level sensitivity and offers quantitative guidance for balancing scale and sample size issues.
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