## 1 Introduction
### Remote Sensing Context  
Since the launch of the first Landsat satellite in the 1970s, a significant body of research has shown the utility of satellite imagery in quantifying aspects of vegetation productivity, health, and change. Using broadband multispectral remote sensing, it is common to utilize the visible portion of the electromagnetic spectrum to assess leaf chlorophyll and pigment content, the near-infrared (NIR) for cell properties, and the shortwave infrared for water content [20]. Most often this is done through the development of simple band ratio indices such as the normalized difference vegetation index, NDVI (normalized ratio of the red and NIR bands). NDVI is an indicator of photosynthetic capacity of plants [21], and it correlates with leaf chlorophyll, green color, and plant vigor [22] and is often used as a proxy for vegetation abundance, health, and growth [23; 24; 25], and to measure land cover dynamics and evapotranspiration [26]. Newer satellite sensors (e.g., WorldView-3, Sentinel and Planet RapidEye) include narrowband red-edge band or bands (in the region between 680 nm to 730 nm). This area of the spectrum is particularly suitable for detecting differences in chlorophyll content and leaf structure. The red-edge band has been used to measure plant productivity [27], leaf chlorophyll [28; 29; 30], and to estimate biomass at varying canopy covers [1; 21; 31; 32; 33]. In particular, this band has been useful for studying how experimental water deficits (such as those induced by girdling trees) change characteristics of plant physiology (e.g., chlorophyll a/b ratio); as plants experiencing a water deficit change their foliar chlorophyll composition, resulting in a shift of red-edge reflectance towards shorter wavelengths [1; 34]. Substituting the red band with a red-edge band in a vegetation index (i.e., NDRE, or normalized difference red edge index) can show plant pigment changes [35] and has been correlated with drought-induced variation in leaf photosynthetic rates [36]. Remote sensing has been used to map the spatial extent of water limitation and drought impacts on vegetation, and many prominent examples come from agricultural experiments in greenhouses [37]. For example, [PERSON] et al. [38] compared the ability of hyperspectral indices and NDVI (from imagery covering 430 nm and 890 nm) to detect what they called \"water stress\" in barley experiments using greenhouse potted plants under well-watered and drought conditions. Plant response to water limitation was detected up to ten days earlier using a combined model of several hyperspectral indices. In agricultural field applications, [PERSON] et al. [39] found success using NDVI to predict water limitation in a corn crop using a time series of NDVI from multiple years (2000-2010) of Landsat 5 TM imagery. The red-edge band and moderate spatial resolution (20 m ground sampling distance (GSD)) of the Sentinel-2 and Sentinel-3 satellites have also shown great promise for agricultural work. [PERSON] and [PERSON] (2018) showcase the potential of several red-edge indices to estimate canopy chlorophyll and nitrogen content in regional agricultural applications. In addition to agricultural applications of remotely sensed plant properties, recent studies in natural environments have also used multispectral data for monitoring leaf and canopy moisture status, including narrow bands in the shortwave infrared (SWIR) region as well as the red edge. For example, [PERSON] et al. (2012) had success evaluating changes in canopy water content in the California Sierra Nevada forests with narrow spectral features centered at 980 nm and 1160 nm in combination with LiDAR scans. [PERSON] et al. (2018) evaluated a time series of RapidEye scenes covering a pifnon-juniper woodland in central New Mexico acquired before and after water deficit was induced by girdling. They found that the NDAE (normalized difference red edge index) detected changes in plant stress, as indicated by shifts in chlorophyll a/b ratio, much earlier than NDVI and GNDVI.