CROPS WATER STATUS QUANTIFICATION USING THERMAL AND MULTISPECTRAL SENSING TECHNOLOGIES
Thermal and multispectral imagery can provide users with insights into the water stress status and evapotranspiration demand of crops. However, traditional platforms, such as satellites, for these thermal and multispectral sensors are limited in their usefulness due to low spatial and temporal resolution. Small unmanned aircraft system (UAS) have the potential to have similar sensors installed and provide canopy temperature and reflectance information at spatial and temporal resolutions more useful for crop management; however, most of the existing research on the calibration or the estimation of water status were established based on the satellite platforms either for the sensors calibration or water status quantification. There is, therefore, a need to develop methods specifically for UAS-mounted sensors. In this research, a pixel-based calibration and an atmospheric correction method based on in-field approximate blackbody sources were developed for an uncooled thermal camera, and the higher accurate vegetative temperature acquired after calibration was used as inputs to an algorithm developed for high-resolution thermal imagery for calculating crop latent heat flux. At last, a thermal index based on the Bowen ratio is proposed to quantify the water deficit stress in a crop field, along with this, a method for plot-level analysis of various vegetation and thermal indices have been demonstrated to illustrate its broad application to genetic selection. The objective was to develop a workflow to use high-resolution thermal and multispectral imagery to derive indices that can quantify crops water status on a plot level which will facilitate the research related to breeding selection.
The camera calibration method can effectively reduce the root mean square error (RMSE) and variability of measurements. The pixel-based thermal calibration method presented here was able to reduce the measurement uncertainty across all the pixels in the images, thus improving the accuracy and reducing the between-pixel variability of the measurements. During field calibration, the RMSE values relative to ground reference targets for two flights in 2017 were reduced from 6.36°C to 1.24°C and from 4.56°C to 1.32°C, respectively. The latent heat flux estimation algorithm yields an RMSE of 65.23 W/m2 compared with the ground reference data acquired from porometer. The Bowen ratio has a high correlation with drought conditions quantified using the soil moisture index, stomatal conductance, and crop water stress index (CWSI), which indicates the potential of this index to be used as a water deficit stress indicator. The thermal and multispectral indices on a plot level displayed will facilitate the breeding selection.