## 1 Introduction
In addition to red-edge based indices, new VIs, such as the NIR reflectance of vegetation index (NIRv) [32] and kernel NDVI (kNDVI) [33], have also been developed to address saturation issues with optical remote sensing. NIRv has shown good agreement with gross primary production (GPP) in corn and soybeans [32]. Finally, kNDVI is a nonlinear generalization of NDVI that is more resistant to saturation [33] and, therefore, may also be useful in estimating higher cover crop biomass levels beyond the point of saturation. Active remote sensing approaches such as synthetic aperture radar (SAR) also have emerged as a viable technique for monitoring and estimating crop biophysical characteristics [34; 35; 36], although most studies focus on the use of SAR data in crop classification. SARs operate at microwave wavelengths (0.18-30 cm), emit coherent signals, and measure the backscattered intensity (backscatter) and phase of the return signals. The intensity and phase of the return signal at a given frequency and polarization can be used to elucidate the 'polarimetric signatures' of landscape features based on the type of scattering that is present [37]. Agricultural studies have most commonly utilized SAR imagery in either the L-band (15-30 cm) or C-band (3.75-7.5 cm) wavelength ranges, often with two linear polarizations (dual-pol) or four linear polarizations (quad-pol). The unique information contained in SAR polarimetric channels, such as a change from transmitted horizontal (H) to return vertical (V) backscatter or from V to H (i.e., cross-polarization), provides information on the presence of volume scattering targets. Vegetation exhibits strong volume scattering; thus, cross-polarization backscatter has been shown to be well correlated with total biomass in wheat (_Triticum aestivum_ L.) and soybeans [38; 39]. Similarly, the radar vegetation index (RVI), which is produced using co- and cross-polarization backscatter, has been shown to be sensitive to vegetation water content and LAI [40]. In addition to the backscatter intensity, SAR signals also contain phase information. Combining polarimetric backscatter and polarimetric phase information provides additional information, allowing for more refined classifications of ground targets with the use of polarimetric decompositions [36]. For instance, the polarimetric parameters decomposed from SAR data--such as entropy, anisotropy, and alpha angle--can successfully predict crop biophysical characteristics such as biomass and height under certain conditions [41; 42; 43]. Finally, the cross-channel correlation coefficient known as the interferometric SAR (InSAR) coherence compares the phase information from two repeat-pass SAR images. When phase differences are similar for a neighborhood of pixels, InSAR coherence values will be high, and when phase differences are random, InSAR coherence values will be low. Reductions in InSAR coherence are often indicative of changes in target properties between two images, such as changes in soil roughness, soil moisture, vegetation water content, and vegetation structure [44; 45]. However, InSAR coherence may also be useful in biomass estimation, as it quantifies phase change between two images and can, therefore, be used to estimate crop height [46; 47] and growth [48]. Although the use of SAR to estimate crop biophysical characteristics is on the rise, few studies to date have integrated both optical and SAR data [49]. Moreover, to our knowledge, none focus specifically on small-stature winter cover crops that undergo a unique phenological development that includes fall establishment followed by a winter dormancy period and later springtime growth. The ESA's publicly available multispectral (Sentinel-2) and SAR (Sentinel-1; dual polarization, C-band) satellite imagery provides a unique opportunity to assess the efficacy of linking contemporary VIs using red-edge bands and SAR-derived metrics such as backscatter intensities, polarimetric parameters, and InSAR coherence.