## 4 Discussion  
UAVs are increasingly being used to map natural and agricultural vegetation [43] due to their precision and flexibility [56]: UAVs allow the use of a range of cameras, control over ground sample distance, and the ability to precisely control the timing of image acquisition. Because of the ability to acquire data throughout the day, imagery from UAVs can offer very precise and new insights into the dynamics of land surface behavior at detailed spatial scales. Scientific interests such as phenology, changes in transpiration, plant water status, and carbon fluxes can be studied at unprecedented temporal resolutions (e.g., hourly or daily), potentially creating hypertemporal vegetation products for input into ecosystem models and new insights into ecosystem behavior. For example, [PERSON] et al. [57] explored diurnal temperature dynamics of grass and maize canopies in Saudi Arabia with varying water status levels at ultra-high spatial resolution (0.02 m). The use of UAVs allowed for fine temporal characterization of plant-water relations, as leaf skin temperature is strongly coupled with stomatal conductance and related to transpiration rates [58]. Several papers focusing on orchards in Spain showcase the ability of UAV-based hyperspectral imagery (and the PRI index in particular) to relate diurnal changes in plant water content to measures in the field at the time of each image acquisition [59; 60]. Despite increasing spatiotemporal resolutions to understand variations in plant water relations, capturing accurate and meaningful data from UAVs and their associated sensors can be difficult. Key error sources associated with UAV data collection and processing, such as solar angle, weather conditions, geolocation, and radiometric calibration, can all influence the accuracy of the calculated vegetation indices [61]. In our study we found variations in calculated indices of NDVI and NDRE over the course of a day and varying solar angles. We found that NDVI was best able to capture differences across our treatments between 10:00 and 12:00, while the best differentiation in NDRE slightly earlier in the day (9:00). Studies have reported minimum values for vegetation indices at solar noon in areas with LAI (leaf area index) of 0.5-2, and data collected during the traditionally used  
Figure 5: Scaling results comparing UAV data and PlanetScope data: (a) boxplot of NDVI for PlanetScope and UAV resampled to 3 m; (b) correlation between UAV NDVI and PlanetScope NDVI; and (c) % change from control in NDVI value for UAS raw value and PlanetScope. \"high sun\" condition with lower predictive capability than at oblique sun angles (solar zenith angle (SZA) \(>\) 40\({}^{\circ}\)) [62]. Our study didn't observe the lowest vegetation index values at peak sun conditions; however, differences across treatments were more accurately captured at larger solar zenith angles earlier in the day. Additionally, the spatial resolution influences the detectability of changes in water condition as well as differences between treatments, therefore, determining the appropriate resolution imagery in which to disentangle these differences is important. When focusing on plant water relations, commercial grade multispectral cameras are very useful, particularly when they have both red edge and NIR bands. We found complementarity between and nonredundant information content provided by both NDVI and NDRE. Both measures were able to identify moisture deficit in plants and distinguish them from control and watered plants; however, NDVI was better able to distinguish between treatments than NDRE and was more positively correlated with field measurements of leaf water content than NDRE. These results highlight the consistent utility of NDVI in water deficit and plant health studies. Despite these mixed results, it is likely that applications for and evaluation of red edge sensors will grow.