Temperature/emissivity separation using hyperspectral thermal infrared imagery and its potential for detecting the water content of plants

Thermal infrared (TIR) remote sensing is among the most effective tools for retrieving land surface temperatures (LSTs) at different scales using remotely sensed data with different spatial resolutions. Significant advancements have recently been made in TIR sensor technology, and hyperspectral TIR images now provide an opportunity to separate temperatures and emissivities with high accuracy. In this study, an experiment is performed to retrieve temperatures and emissivities based on a hyperspectral TIR spectrometer, the HyperCam-LW (Long Wave), and show the potential of this instrument in detecting the water content variations, water deficiencies and health of plants. In this study, a temperature emissivity separation (TES) method based on spectral smoothness is used to retrieve the temperature and emissivity of wheat plants from hyperspectral TIR data. Based on the retrieved temperatures and emissivities, the variations in the emissivity from different wheat plants are observed, and the relationship between the emissivity dynamics and water content is analysed. A comparison of the temperature of different plants was performed, and the results clearly showed the canopy structure of the plants. Subsequently, the health of the wheat was analysed, and the results showed that for water-deficit plants, the emissivity increased in a consistent manner over all spectral bands while the spectral shape remained almost unchanged because the spectral emissivity is sensitive to water content variations in plants. In this paper, the results also demonstrated that it is possible and perhaps reasonable to attribute the overall increase in the emissivity of plants with water deficits to cavity effects resulting from biochemical and structural changes in the leaves caused by water deficits.