Diurnal and seasonal variation in ET at canopy scales using a novel UAV-based approach

Unmanned aerial vehicles (UAVs) constitute a new frontier in remote sensing of ET that bridges the gap between in situmeasurements and remotely sensed observations of plant water use. While a single satellite pixel often comprises a mixture of plant types and bare soil, UAV imagery can resolve fine- (m- to cm-) scale differences in surface temperature without thermal unmixing. Furthermore, they can be used to observe diurnal patterns of plant water use and photosynthesis, providing critical insights into the timing and severity of plant water stress. We highlight a novel approach for estimating ET at leaf- to canopy-scales using thermal infrared (TIR) imagery, structural data, and a suite of environmental sensors mounted on a UAV platform. ET is calculated solely from these UAV-acquired data using a combined atmospheric profile and surface energy balance algorithm. Centimeter-scale leaf position and orientation information derived from Structure-from-Motion (SfM) are integrated with the functional data to constrain available energy, allowing for multi-scale estimation of plant water use within and across canopies.

We present UAV-derived ET across diurnal and seasonal time scales for two landscapes, a native California grassland and a riparian oak woodland. Grassland flights were conducted at 90-minute intervals spanning early morning to late afternoon during the 2021 and 2022 growing seasons. Results show good agreement (<20%) with measured ET fluxes from a collocated eddy covariance tower throughout the growing season. Riparian oak canopies were observed monthly and diurnally over the summer of 2021. Ground measurements of surface temperature, stomatal conductance, and soil moisture were collected during each flight. Water-stressed tress at the driest site showed peak ET at midday, decreasing into afternoon, reflecting down-regulation of photosynthesis to preserve hydraulic function. Relative canopy water use and stress across a range of tree sizes will also be discussed using measurements of stem and canopy area and ET for individual tree crowns extracted from the UAV imagery. By collecting comprehensive meteorological data from sensors on the UAV itself, our approach eliminates the need for extensive field data collection and enables characterization of highly spatially and temporally resolved fluxes within and across complex landscapes. This work opens up new avenues to investigate how ecologically important species—and even individual trees—respond to drought and the impacts of these responses on water use, water stress, and the ecological health of critical habitats like riparian forests.