## 1 Introduction  
Planting cover crops is recognized as an important conservation practice to promote agricultural sustainability via the provisioning of agro-ecosystem services [1]. Some of these well-documented agro-ecosystem services include the protection of soil from erosion,increased nutrient retention, reduction in nutrient leaching losses, reduction in disease and pest infestations, increased weed suppression, increased infiltration, reduced wetland water flow, soil health improvements via carbon sequestration, and developing resilient cropping systems that better adapt and mitigate climate change [2; 3; 4; 5; 6]. Cover crop performance estimations, such as percent ground cover and biomass accumulation, are directly related to the magnitude of agro-ecosystem services that cover crops can provide [4; 7; 8]. Despite the well-recognized relationship between cover crop performance and the associated benefits to cropping systems, few studies to date have assessed the ability of remote sensing to estimate cover crop performance, especially at the landscape scale. Remote sensing is a powerful tool capable of producing landscape-level assessments of cover crop presence and absence [9; 10], emergence and termination [11; 12], and performance [13; 14; 15]. To date, remote sensing in cover crop research has primarily employed optical sensing approaches using proximal (hand-held) sensors [14], unoccupied aerial vehicles (UAVs) [16; 17], high-resolution aerial images [18], and Earth-observing satellites [8; 10; 13; 19]. Optical vegetation indices (VIs) used to estimate plant biomass usually exploit the relationship between visible light with low reflectance (linked to plant pigment concentration) and high reflectance observed in near-infrared (NIR) light (influenced by internal leaf structure) [20]. This visible-NIR reflectance difference is enhanced during plant growth and manifests as increased fractional cover, which is linked to plant biomass. Both the triangular vegetation index (TVI) [21] and the normalized difference vegetation index (NDVI) [22] exploit the visible-NIR difference and have been used to successfully estimate biomass in cereal grass cover crop species [13; 14]. VIs derived from optical imagery, particularly NDVI, tend to saturate at a leaf area index (LAI) greater than three in numerous plant species [23; 24]. Similarly, previous work observed NDVI saturation at approximately 1500 kg ha\({}^{-1}\) biomass in cereal grass species [14]. This substantially limits our ability to estimate biomass values at the higher ranges (i.e., >1500 kg ha\({}^{-1}\)) that are frequently observed in the springtime, particularly in late-terminated cover crop fields under warmer growing conditions. Although red-edge bands were shown to be more resistant to saturation [17; 24; 25; 26] they have only recently been incorporated into earth-observing platforms such as the European Space Agency's (ESA) Sentinel-2 satellites (launched in June 2015 and March 2017). Sentinel-2 satellites contain three red-edge bands and have a 20-m spatial resolution and a five-day revisit time, resulting in a greater spatial and temporal resolution than Landsat satellites. Red-edge indices such as the normalized difference red-edge index (NDVI_RE) and the simple ratio red-edge (SR_RE) have shown strong relationships with chlorophyll A [27], which, in turn, is closely linked with crop biophysical characteristics such as LAI and fractional ground cover [28; 29]. Additionally, previous work demonstrated that the visibly atmospherically resistant index using a red-edge band (VARI_RE) was better able to predict the LAI of corn (_Zea mays_ L.) past the point of saturation (i.e., up to LAI values of six) [30]. In contrast, other studies found that red-edge-based indices did not improve biomass estimation in corn and soybeans (_Glycine max_ L.) when compared to traditional VIs such as NDVI [31].