## 4 Discussion
### Normalized Difference Red-Edge Index Best Predicts Cover Crop Biomass
Species-level differences in growth pattern among cereal rye, wheat, and triticale were somewhat visually evident in nadir photographs (Figure 3). For instance, triticale appeared to have more ground cover earlier than wheat and cereal rye, due to a more prostrate growth habit, while cereal rye appeared to be taller and possess a more erect leaf angle distribution in the spring. Based on these findings, future research could investigate whether species-level differences in canopy architecture, such as leaf angle distribution, are truly present among cereal grass cover crops and how this may influence the remote sensing of biomass. Such parameters may play an important role in light scattering [81] and will, therefore, influence optical remote sensing observations. Interferometric Synthetic Aperture Radar (InSAR) Coherence Improved Cover Crop Biomass Estimates, but Did Not Improve Issues with Saturation at High Biomass Levels  
We assessed whether the integration of optical and SAR metrics would improve our ability to estimate cover crop biomass, specifically past optical saturation. We consistently observed improved fits and predictive ability in our models when both InSAR coherence and NDVI_RE1 were included as predictors (Tables 4-6), but the magnitude of improvement varied by species (R\({}^{2}\) increase of 4-11%). Despite the improved model performance found with the addition of InSAR coherence, we did not observe improved predictions of cover crop biomass at high biomass levels (>2000 kg ha\({}^{-1}\)). Although InSAR coherence has not previously been used to predict biomass, other studies demonstrated the utility of InSAR coherence for estimating cash crop height [46; 47] and growth [48]. InSAR coherence remains moderate to high (-0.5-1) when crop growth is slow or otherwise limited--such as during the winter dormancy period for cover crops--but will decrease as crop growth continues [82]--such as during the spring growth period when cover crops rapidly accumulate large quantities of biomass (Figure 6). However, InSAR coherence is also sensitive to other changes in scene texture and composition for agricultural regions such as tillage, soil moisture variations, and harvesting, as these events also cause a significant change in return signal phase. The use of InSAR coherence for the biomass estimation of cereal grass cover crops, in combination with the NDVI_RE1 optical index, performed best in this study, provided that one is aware of management activities such as cover crop termination, as such events will also show similarly low coherence values to those of growing vegetation. If management information is not easily obtainable from growers, future work could consider pairing VIs to differentiate termination from a green field, or employing the within-season termination (WIST) algorithm, which has shown a strong ability to estimate cover crop termination dates [11]. In addition to InSAR coherence, we also found species-specific differences between other SAR metrics. Incorporating polarimetric parameters such as entropy and anisotropy, respectively, along with NDVI_RE1 and InSAR coherence, resulted in better wheat and triticale biomass predictive models compared to use of the optical model alone. However, neither entropy nor anisotropy aided in better predictions past the point of saturation and also appeared to saturate at a similar biomass level to NDVI_RE1 (-1900 kg ha\({}^{-1}\)). This may be, in part, because we performed polarimetric decompositions on Sentinel-1 dual-pol imagery with VV and VH channels only, as opposed to quad-pol imagery. Previous work demonstrated that alpha, entropy, and anisotropy estimated winter wheat biomass moderately well (R\({}^{2}\) = 0.51, 0.42, and 0.44, respectively) using RADARSAT-2 quad-pol imagery, which more effectively resolves target-scattering mechanisms when compared to dual-polSAR imagery [83].