Plant productivity shapes how species compete and respond to stressors (Mahaut et al., 2020) and consequently how species are distributed in space, thereby shaping the assembly of plant communities. Productivity is traditionally estimated directly by measuring aboveground biomass and cover, but remote sensing is emerging as a less time-and resource-intensive tool (Heinsch et al., 2006; Yu et al., 2018). Estimating vegetation cover requires relatively little time and resources but considers growth in only two dimensions, ignoring vegetation height and density. Biomass estimates more directly measure total plant growth; however, collecting, drying, and weighing biomass are both resource intensive and destructive, limiting the practicality of biomass studies at large spatial and temporal scales. Recent developments in remote multispectral imagery and vegetation structure mapping have improved our ability to estimate plant productivity (Cerasoli et al., 2018; Fischer et al., 2019). Multispectral vegetation indices (VIs) are a collection of ratios and transformations of light reflectance intensities detected in certain spectral bands. They are based on the light that is reflected by vegetation, which is influenced by leaf health, density, and photosynthetic activity (Xue and Su, 2017); thus, VIs have emerged as an important tool for estimating plant community productivity (Wang et al., 2010; Cavender-Bares et al., 2017). Many VIs use the ratio of red to near-infrared reflectance because of the correlation between the contrast in the absorption of these bands and leaf health (Myneni et al., 1995). The normalized difference vegetation index (NDVI; Tucker, 1979) is the most commonly used VI. NDVI is based on the ratio of red and near infrared reflectance and is often applied in ecological studies (Pettorelli et al., 2005), where it has