Use of thermal imaging and the photochemical reflectance index (PRI) to detect wheat response to elevated CO2 and drought

Abstract

The spectral-based photochemical reflectance index (PRI) and leaf surface temperature (T_leaf ) derived from thermal imaging are two indicative metrics of plant functioning. The relationship of PRI with radiation-use efficiency (RUE) and T_leaf with leaf transpiration could be leveraged to monitor crop photosynthesis and water use from space. Yet, it is unclear how such relationships will change under future high carbon dioxide concentrations ([CO₂ ]) and drought. Here we established an [CO₂ ] enrichment experiment in which three wheat genotypes were grown at ambient (400 ppm) and elevated (550 ppm) [CO₂ ] and exposed to well-watered and drought conditions in two glasshouse rooms in two replicates. Leaf transpiration (T_r ) and latent heat flux (LE) were derived to assess evaporative cooling, and RUE was calculated from assimilation and radiation measurements on several dates along the season. Simultaneous hyperspectral and thermal images were taken at $~$ 1.5 m from the plants to derive PRI and the temperature difference between the leaf and its surrounding air ( $∆$ T_leaf-air ). We found significant PRI and RUE and $∆$ T_leaf-air and T_r correlations, with no significant differences among the genotypes. A PRI-RUE decoupling was observed under drought at ambient [CO₂ ] but not at elevated [CO₂ ], likely due to changes in photorespiration. For a LE range of 350 W m^-2 , the ΔT_leaf-air range was $~$ 10°C at ambient [CO₂ ] and only $~$ 4°C at elevated [CO₂ ]. Thicker leaves in plants grown at elevated [CO₂ ] suggest higher leaf water content and consequently more efficient thermoregulation at high [CO₂ ] conditions. In general, T_leaf was maintained closer to the ambient temperature at elevated [CO₂ ], even under drought. PRI, RUE, ΔT_{leaf -air} , and T_r decreased linearly with canopy depth, displaying a single PRI-RUE and ΔT_{leaf -air} T_r model through the canopy layers. Our study shows the utility of these sensing metrics in detecting wheat responses to future environmental changes.

Keywords: climate impact; leaf temperature; radiation-use efficiency (RUE); remote sensing.