Estimating plant water stress and evapotranspiration using very-high-resolution (VHR) UAV imagery

© 2018 Dr Suyoung ParkAdequate and timely irrigation of crops is critical to maximise productivity and water use efficiency. Thus, having a capability of monitoring crop water status based on real-time monitoring can promise precise irrigation scheduling in a decision-making strategy. Unmanned aerial vehicle (UAV) remote sensing becomes a readily usable tool for agricultural water management with high temporal and spatial resolutions. This research estimates crop water stress and evapotranspiration (ET) by integrating very-high-resolution UAV imagery into crop water stress model and surface energy balance model in the aspects of crop water status and water use. Thermal infrared (TIR) and multispectral (MS) imagery from UAV systems are used as inputs of surface temperature and vegetation canopy structure with the aim to employ the least ground-based measurements. In the first part of the research, an adaptive estimation method of crop water stress index (CWSI) is proposed to determine automatic and crop-property-specific thresholds of reference boundary limits (Twet and Tdry). In the second part of the research, diurnal changes of plant water stress are explored by a series of UAV sensing at different times of the day. In the last part of the research, tree-by-tree instantaneous ET rate is estimated based on remote sensing energy balance (RSEB) model, presenting water losses of individual plant and assessing the intra-field variability of ET. This research contributes towards the improvement of the precise and rapid estimation of the plant water stress level by a quantitative index: crop water stress index (CWSI) using TIR-based UAV sensing in order to assess the spatial variability of water stress over heterogeneous agricultural fields. In addition, this research contributes to the analysis of the tree-by-tree ET which quantifies the plant water use as well as the assessment of the intra-field variability of ET. The results of the research demonstrate that: 1) the adaptive CWSI method showed strong relationships with stem water potential (ψstem) and stomatal conductance (gs) as plant physiological measurements. A higher correlation to ψstem and gs was obtained than the single reference CWSI; 2) Diurnal CWSIs showed significant correlation to gs according to the irrigation levels at three different times of the day (from morning to afternoon); and 3) the estimated ET was obtained with a strong linear relationship with leaf transpiration (Tr)