## 4 Discussion
### Normalized Difference Red-Edge Index Best Predicts Cover Crop Biomass
Despite the noted model improvements in wheat and triticale biomass predictive models with the inclusion of entropy and anisotropy in this study, we observed no substantial differences when compared to the model with only InSAR coherence and NDVI_RE1 for both wheat (\(\Delta\)R\({}^{2}\) = 0; \(\Delta\)RMSE = +3 kg ha\({}^{-1}\); \(\Delta\)MAE = \(-\)15 kg ha\({}^{-1}\)) and triticale (\(\Delta\)R\({}^{2}\) = 0%; \(\Delta\)RMSE = +2 kg ha\({}^{-1}\); \(\Delta\)MAE = +2 kg ha\({}^{-1}\)) during cross-validation. Future work could investigate the efficacy of cover crop biomass estimations using the quad-pol polarimetric decompositions applied to imagery acquired from satellites such as RADARSAT-2, although the data are not publicly available as with Sentinel-1. Improvements in polarimetric decompositions could also be achieved by using dual-pol imagery with both linear co-polarizations (HH and VV) [83]. The compact polarimetry operating modes of RADARSAT-2 and the RADARSAT Constellation Mission (RCM)--with a circular polarization transmitted signal and V and H linear receivers--would likely lead to substantial improvements in the accuracy of polarimetric decomposition parameters compared to linear dual-pol operating modes. The compact polarimetry mode is similarly effective as a quad-pol for crop classifications [84], indicating potential performance similarities for crop biomass estimation. Optical-SAR models for cereal rye differed from wheat and triticale in that model performance improved when backscatter intensities (VV, VH, and RVI) were included (Tables 3 and 4). Previous work demonstrated a significant relationship between cross-polarization (VH) backscatter and biomass [39], as complex canopies are more likely to volume scatter SAR signals, leading to higher VH backscatter values. However, others found that the VV and HH backscatter were correlated with the biomass of canola (_Brassica napus_) and corn, respectively, which is likely indicative of non-volume (i.e., single bounce, double bounce) scattering interactions with well-defined crop structures (horizontal and broad canola leaves (_Brassica napus_), and vertical corn stalks) [43]. In this study, however, we would expect a greater volume scattering (VH) response for cereal grass canopies with smaller and less geometrically well-defined structures, but with potential for variability between species with slightly different canopy structures. If our supposition regarding species-level canopy geometry differences is accurate, and cereal rye shows a more erect leaf angle distribution, we would expect cereal rye to exhibit a relatively higher VV than VH when compared to the other species, which exhibit more volume scattering in canopies with less well-defined structures. These differences in canopy-scattering mechanisms between species may indeed be present in our results, as indicated by the fact that including RVI (fundamentally a VV and VH comparison metric) only improved the performance of the cereal rye biomass predictive model, which is consistent with previous work on cash crops [85]. The best-performing biomass predictive model for cereal rye included NDVI_RE1, InSAR coherence, VV backscatter, and RVI. This combined model provided the largest increase in its predictive ability and the highest AICw (\(\Delta\)R\({}^{2}\) = +15%; AICw = 0.75). However, as with wheat and triticale, cross-validation indicated that the combined cereal rye model does not outperform the optical model with InSAR coherence alone (Table 6). Although we found species-specific responses to Sentinel-1 C-band SAR backscatter and polarimetric parameters, InSAR coherence consistently improved models for all species both during model evaluation (Table 4) and cross-validation (Table 6). Therefore, for Sentinel-1 C-band SAR operating in dual-pol mode, we conclude InSAR coherence layers were the most effective in improving cover crop biomass predictions when combined with NDVI_RE1.