## 3 Results
### Optical--Synthetic Aperture Radar (SAR) Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Mean R\({}^{2}\)** & **Mean RMSE** & **Mean MAE** \\ \hline \multicolumn{5}{c}{**Wheat**} \\ \hline NDVI\_RE1 & 0.71 & & 1280.97 & 879.3 \\ NDVI\_RE1 + Coh & 0.74 & & 1243.94 & 805.26 \\ NDVI\_RE1 + Coh + \(H\) & 0.74 & & 1246.88 & 789.74 \\ \hline \multicolumn{5}{c}{**Titicale**} \\ \hline NDVI\_RE1 & 0.77 & & 1013.39 & 686.37 \\ NDVI\_RE1 + Coh & 0.82 & & 825.34 & 593.98 \\ NDVI\_RE1 + Coh + \(A\) & 0.81 & & 827.47 & 591.86 \\ \hline \multicolumn{5}{c}{**Cereal Rye**} \\ \hline NDVI\_RE1 & 0.24 & & 826.94 & 662.69 \\ NDVI\_RE1 + Coh & 0.33 & & 804.32 & 625.82 \\ NDVI\_RE1 + VV + & 0.34 & & 905.43 & 693.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of combined optical and synthetic aperture radar (SAR) analysis of variance cross-validation. Models combined the normalized difference red-edge index using band 5 (NDVI_RE1) optical index and the best SAR metrics—InSAR coherence (Coh), entropy (\(H\)), anisotropy (\(A\)), vertical-vertical backscatter (VV), and the radar vegetation index (RVI). We split the data into 70% train and 30% test splits to estimate goodness of fit (adjusted R\({}^{2}\)) and error (RMSE, MAE). We iterated these splits 100 times and reported the mean statistics. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline
**Model** & **VV** & **VHV** & **RVI** & **Alpha** & \(A\) & **H** & **Coh** & **Combined** \\ \hline Wheat & 0.08 & 2.59 & 0.92 & 1.99 & 1.9 & 2.51 & 3.64 * & 26.86 *** & 15.17 *** \\ Triticale & 0.2 & 0.5 & 2.23 & 1.67 & 2.81 & 4.72 * & 3.85 * & 18.64 *** & 9.43 *** \\ Cereal Rye & 6.87 ** & 3.46 & 1.22 & 4.00 * & 0.23 & 0.3 & 0.75 & 15.75 *** & 7.80 *** \\ \hline \multicolumn{5}{c}{* \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\).} \\ \end{tabular}
\end{table} 
Table 5: Results of combined optical and synthetic aperture radar (SAR) species-specific analysis of variance (ANOVA) models. Models combined the normalized difference red-edge index using band 5 (NDVI_RE1) optical index and SAR backscatter indices and polarimetric parameters. All models are compared to the NDVI_RE1 model with no SAR metrics to evaluate statistically significant improvements when SAR metrics are included. Combined models varied by species, and all significant combined models included InSAR coherence (Coh). Wheat also included entropy (\(H\)); tritticale also included anisotropy (\(A\)); and cereal rye included vertical–vertical polarization (VV) and the radar vegetation index (RVI). As the addition of multiple SAR metrics produced statistically improved biomass predictions compared to models with NDVI_RE1 alone (Table 5), we also explored the use of multiple SAR parameters in addition to NDVI_RE1 for each of the three cover crop  
Figure 6: Examples of wheat (**A**), triticale (**B**), and cereal rye (**C**), biomass plotted against InSAR coherence. Lower InSAR coherence values are indicative of general changes in field conditions between two images forming an InSAR pair as well as general shifts from coherent surface scattering to incoherent random scattering as cover crops mature comparing different InSAR pairs. For the biomass sampling dates (included in the upper right of each panel) the following InSAR pairs were matched: (1) 3-14-21 and 3-26-21, (2) 3-26-21 and 4-19-21, and (3) 4-19-21 and 5-1-21. For these example fields, termination occurred on 5-27-21 for cereal rye, 4-29-21 for triticale, and 6-28-21 for wheat. species, which we refer to as \"combined models.\" All combined models were screened for collinearity and no predictor variables correlated above the predefined threshold (r > 0.7). For wheat and trtiticale, the best-performing combined model contained NDVI_RE1, InSAR coherence, and entropy (wheat) or anisotropy (trtiticale) as predictor variables based on a combination of R\({}^{2}\), error measures, AICc and AICw (Tables 3 and 5).