A new wet reference target method for continuous infrared thermography of vegetations
- Publication Type:
- Journal Article
- Agricultural and Forest Meteorology, 2016, 226-227 pp. 119 - 131
- Issue Date:
© 2016 Elsevier B.V.. Although infrared thermography for stress detection in plants is popular in scientific research, it is rarely used in continuous and automated applications. One of the main reasons for this is that the most precise method for generating wet reference targets, used for normalizing the leaf or canopy surface temperature for microclimatic conditions, requires manual wetting before each image capture. In this article, we present and evaluate a new type of wet reference target that remains wet while having an energy balance as similar as possible to that of the canopy. This reference target consists of a cloth knitted around a solid frame whose shape and dimensions mimic those of the leaves. The cloth remains wet by constantly absorbing water from a reservoir. The new reference target was evaluated on grapevine and kiwifruit plants in greenhouse and orchard conditions. In greenhouse conditions, measured stomatal conductance was consistently more highly correlated with the stomatal conductance index Igwhen Igwas calculated with the new wet reference rather than the manually wetted reference target. Furthermore, the temperature difference between leaves and the new reference target remained stable for as long as measured, in contrast with the manually wetted leaves. Igobtained with the new reference target method was also highly correlated with stomatal conductance (gs) of both crops in orchard conditions. A new empirical regression model to estimate gsfrom Igin greenhouse conditions was introduced and evaluated. This regression model incorporates the background temperature, a parameter that needs to be included in thermographic measurements for obtaining correct surface temperatures, thus avoiding the need for any additional measurements. The same regression model can be applied on different days with differing conditions. The model performed better than other tested empirical models and provided unbiased estimates of gson days with different conditions, resulting in a root mean square error of 22-25% of gs. Thus, it provides a promising method for continuous remote sensing of stomatal conductance or drought stress detection of plants and vegetations.
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