## 4 Discussion
As recently as 2014, red-edge cameras were not routinely used for vegetation mapping [63], however, now there are several satellite-based sensors that include one or more red-edge bands: the Sentinel-2 satellite (3 bands between 700 and 790 nm, 20 m GSD), RapidEye (690-730 nm, 6.5 m GSD), and WorldView-3 (but not WorldView-4) (705-745 nm, 1.4 m GSD), and UAV mounted commercial off-the-shelf multispectral cameras with red edge sensitivity are increasingly popular [47]. Our results suggest a more thorough evaluation of the application and underlying physiological responses to changes in the red-edge spectra is needed. However, assessing the applicability of vegetation indices to represent physiological difference in plants must be continually calibrated. [PERSON] et al. [64] articulated the difficulty in using measures of greenness including NDVI as a proxy for productivity. They concluded that because greenness changes more slowly than plant physiological function, relationships between remotely sensed vegetation indices of drought-tolerant species and gross primary productivity break down on short timescales and decouple during periods of water deficit [64; 65]. Consequently, there remains a critical need to calibrate vegetation indices across ecosystems and across time to enable robust remote sensing of plant physiology. Our experiment showed the potential for linking field-based water content and water potential measurements to NDVI and NDRE, with particularly strong correlations between water content and NDVI. Several studies linking plant water content to spectral indices focus on reflected radiation in the 800 nm-2500 nm range from custom sensors or hyperspectral sensors [66; 67]. However, the use of such systems is limited due to their high cost and fewer platforms that carry them. With the increased use of off-the-shelf multispectral sensors in environmental and agricultural research, there is a need for continued evaluation of vegetation indices that can be derived from narrow-band multispectral sensors such as MicaSense RedEdge and Sequoia. For good overviews of vegetation indices derived from UAV data see [1; 68]. Connecting relative water content, which is an indirect measurement of a change in water status, to physiological determinants of water status such as water potential is still needed. Here we showed that water potential can be derived from estimates of water content with an R\({}^{2}\) of 54% (92% with outliers removed), however this is a small sample size, and although the relationship appears encouraging, we do not have enough information to conclude that water potential can reliability be inferred from common vegetation indices such as NDVI or NDRE. NDVI does detect fine scale variations in water deficit, as shown in Figure 4, apart from treatments that were extremely dry (e.g., Tx1 and Tx2) and treatments with fine scale variation (control vs. water). Despite these limitations, UAVs appear to be a useful tool for detecting small-scale changes in water status (content and water potential). The retrieval of spectral signatures and their association with underlying physiological processes remains a research challenge but also an opportunity, especially in scaling research. Calibration experiments such as the one reported on here are critical to develop robust scaling relationships. In this paper, we show that the derived NDVI values from UAVs and PlanetScope CubeSat imagery across treatments are largely consistent with important differences, particularly in their sensitivity to subtle changes to water content (control and water). Studies have shown that vegetation indices, such as NDVI, can be significantly affected by differences in spectral bandwidth [61; 69]. The spectral range of the red band has been found to be of importance to NDVI. Therefore, investment in increased spectral resolution rather than increased spatial resolution in the development of CubeSats may be more important for understanding patterns in vegetation health and water content.