## 1 Introduction  
Agricultural remote sensing is undergoing a fundamental transformation. On the one hand, imaging spectroscopy (hyperspectral) missions such as SBG, EMIT, EnMAP, DESIS, PRISMA, and HISUI [1; 2; 3; 4; 5] promise more accurate plant phenotyping, including measures of important concerns such as water stress and nutrient deficiency [6] by improving spectral fidelity. Simultaneously, CubeSat constellations such as Planet enable order-of-magnitude improvements in spatial and temporal resolution of multispectral satellite imagery [7], allowing for improved intra-field monitoring -- if radiometric inconsistencies can be overcome [8]. As the volume and variety of optical imagery continue to accelerate, applications will demand models capable of the synergistic use of datasets with widely varying spatial, spectral, and temporal resolutions and noise characteristics. Developing such models will thus require a quantitative understanding of the relationships between metrics derived from various sensors, in order to best leverage the strengths and mitigate the limitations of each. Agricultural applications have been a primary focus of optical remote sensing since the inception of the field, and spectral vegetation indices (VIs) have for decades been one of the most commonly used tools for this purpose [9; 10]. Over the decades, computational simplicity and ease of use have resulted in a proliferation of VI options (e.g., [11; 12; 13; 14; 15]). The growing availability and diminishing cost of decameter [16; 17] and sub-decameter [18] multispectral satellite imagery is making VIs increasingly popular with a broader science and applications community. Data from Planet's Dove and SuperDove sensors are of particular interest given their (nominal) daily global coverage and 3-4.2 m spatial resolution [7], with constantly improving utility for agricultural land management [19]. Such applications span a broad range, including high resolution soil moisture mapping [20], leaf area index [21], pest impacts [22], crop maturation [23], and more. As recently reviewed by [24; 25; 26], popular multispectral VIs vary widely in underlying theoretical basis and conceptual interrelationships. Yet despite this complexity, independent evaluations of multispectral VIs against other approaches for vegetation abundance estimation, particularly using hyperspectral data, are relatively rare. These comparisons will be critical given the proliferation of spaceborne hyperspectral data that is planned for the coming decade. Here, we leveraged existing, state-of-the-art airborne hyperspectral imagery to conduct such a comparative analysis for six popular vegetation indices over a diverse, globally significant agricultural landscape. Global compilations of multispectral imagery from spectrally diverse landscapes have compared and contrasted interrelationships among vegetation indices through the use of bivariate distributions between pairs of indices (e.g., [27]). Results of these intercomparisons have shown a wide range of interrelationships among vegetation indices often assumed to be interchangeable metrics for the same physical quantity--green vegetation cover at pixel scale. However, these studies have generally relied on decameter (or hectometer) resolution multispectral imagery, which is known to alias spectral signatures and introduce varying degrees of linear and nonlinear spectral mixing within sensor ground-projected instantaneous fields of view (IFOVs) at these scales. In addition, global compilations of top-of-atmosphere reflectance spectra generally incorporate a variety of atmospheric and bidirectional reflectance distribution function (BRDF) effects.