## 5 Conclusions  
We conducted a water exclusion experiment over one week to plots of _Baccharis pilularis_ (coyote brush) at the Blue Oak Ranch Reserve (BORR) near San Jose, California. We monitored the experiment at several altitudes with nearly hourly data collections over two days to determine: (1) spatial and temporal thresholds at which water limitation in plants could be detected via common spectral indices (NDVI and NDAE); (2) relationships between spectral and physiological responses to plant water deficit; and (3) how coarser resolution imagery provided by PlanetScope data compared with UAV-collected imagery. We found the best separation between treatments at higher resolution (lower flying height), and the best separation between treatments in the morning (NDVI) and early morning (NDRE). Overall, most treatments could be differentiated spectrally from each other and from the control, with some exceptions. For example, it was difficult to separate the driest plots (Tx1 and Tx2) using NDVI. These results suggest that while there are important differences between NDVI and NDAE across treatments, there also exists some complementarity between NDVI and NDAE indices that needs further evaluation. We found strong relationships between NDVI and leaf water content (highest r = 0.94). When comparing PlanetScope to UAV imagery collected at the same time, we found that PlanetScope data was able to capture the overall trend in treatments (i.e., able to detect healthy green vegetation from dead dry vegetation), even when treatments were represented by single pixels. However, PlanetScope data was not able to capture subtle changes in water content. Although there was a positive correlation between NDVI derived from the UAV and PlanetScope, the UAV imagery captured a larger spectral range, suggesting a greater spectral sensitivity to plant responses to water deficit with the UAV camera than with the PlanetScope imagery. In this case a narrow-band sensor such as the RedEdge provided more detailed information by capturing a more precise measurement of specific wavelengths than did the PlanetScope sensor. What differentiates data collected from UAVs from data collected by high-resolution satellite sensors is their ability to collect data on demand, at high temporal resolution, and with multiple sensor payloads. Because of this and their increasing popularity, more experiments that evaluate the relationship between direct field measurements and camera sensitivity are needed. Scaling simple vegetation indices from UAVs to CubeSats will increase our ability to translate field-based physiology measurements to landscape or regional scales. The following are available online at [[http://www.mdpi.com/2072-4292/11/16/1853/s1](http://www.mdpi.com/2072-4292/11/16/1853/s1)]([http://www.mdpi.com/2072-4292/11/16/1853/s1](http://www.mdpi.com/2072-4292/11/16/1853/s1)), Figure S1: A simple linear regression was calculated to predict mid-day water potential on the basis of water content. Data gathered for Tx4 from Day 2 (outlier) was removed to further assess fit. A significant regression equation was found with an R\({}^{2}\) of 0.921. Leaf water potential decreased 0.0585 (MPa) for each percent water content. Figure S2: Spectral index value for all pixels (Day 1 and Day 2) by treatment: NDRE values. Difference between pairs of treatments determined from Wilcox test are indicated at the top of each pair: ns = not significant at 0.05 significance level (\(p>0.05\)), *: \(p\leq 0.05\), **: \(p\leq 0.01\), ***: \(p\leq 0.001\); Figure S3: Density plot of distribution of NDVI vs. NDRE values for a) all pixels within all n = 12 UAV flights, b) all pixels for all flights for each treatment.