## 3 Results
### Optical Index Results  
Log-linear models best met regression assumptions and performed similarly to polynomial and segmented models. Thus, we only present results from log-linear models in the main text. Among all the considered VIs, NDVI_RE1 proved to be best at predicting cover crop biomass (R2 = 0.61; RMSE = 1019; MAE = 639; AICw = 0.98; Table 3; Figure 5). Although NDRE and NDWI1 had similar coefficients of determination and error values (Table 3), their AICc were \(\geq\) +8 when compared to NDVI_RE1. Our findings were further supported by AICw values, where we found a 98% probability that the model using NDVI_RE1 to predict cover crop biomass was the best when compared to the other VIs included in our evaluation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **Adj R2** & **RMSE** & **MAE** & **AICc** & **AICw** \\ \hline NDVI\_RE1 & 0.61 & 1018.88 & 639.17 & 1337 & 0.98 \\ NDWI1 & 0.61 & 1023.94 & 633.8 & 1345 & 0.02 \\ NDRE & 0.59 & 1057.69 & 663.15 & 1377 & 0 \\ NDWI2 & 0.58 & 2016.30 & 1365.50 & 1389 & 0 \\ NDVI & 0.56 & 1094.22 & 688.71 & 1417 & 0 \\ k NDVI & 0.55 & 1084.84 & 691.23 & 1423 & 0 \\ EVI & 0.55 & 1079.55 & 686.63 & 1430 & 0 \\ VARI\_RE3 & 0.54 & 1092.24 & 695.93 & 1435 & 0 \\ RTVII & 0.53 & 1129.64 & 706.84 & 1444 & 0 \\ SR\_RE1 & 0.53 & 2188.32 & 899.93 & 1452 & 0 \\ RER & 0.52 & 1486.36 & 770.53 & 1456 & 0 \\ TVI & 0.52 & 1127.99 & 718.22 & 1459 & 0 \\ VARI\_RE2 & 0.51 & 1142.31 & 732.25 & 1472 & 0 \\ MCARI2 & 0.51 & 1207.53 & 755.54 & 1473 & 0 \\ MTVI2 & 0.51 & 1207.53 & 755.54 & 1473 & 0 \\ VARI\_G & 0.51 & 1101.29 & 700.12 & 1476 & 0 \\ SAVI & 0.5 & 1181.99 & 760.99 & 1487 & 0 \\ NIRv & 0.47 & 1227.51 & 787.64 & 1517 & 0 \\ RTVI2 & 0.46 & 1357.68 & 751.95 & 1531 & 0 \\ SR\_WI & 0.39 & 1248.93 & 782.68 & 1597 & 0 \\ SR & 0.36 & 1404.49 & 1750.84 & 1625 & 0 \\ SR\_RE2 & 0.34 & 1151.29 & 745.82 & 1640 & 0 \\ NDVI\_RE2 & 0.34 & 1135.56 & 737.49 & 1644 & 0 \\ VARI\_RE1 & 0.23 & 1486.54 & 959.25 & 1729 & 0 \\ RTVI3 & 0.19 & 1535.50 & 993.11 & 1759 & 0 \\ SR\_RE3 & 0.14 & 1560.12 & 997.26 & 1796 & 0 \\ NDVI\_RE3 & 0.14 & 1562.67 & 999.44 & 1797 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of log-linear regression models predicting cover crop aboveground biomass, including the coefficient of determination (adjusted R2), root mean square error (RMSE), and the mean absolute error (MAE), and model fit statistics—Akaike’s information criterion for small sample sizes (AICc) and AIC weights (AICw). Refer to Table 1 for optical index abbreviations. Log-linear models best met parametric assumptions. The red-edge normalized difference vegetation index with band 5 (NDVI_RE1) best estimated winter cover crop biomass. After identifying NDVI_RE1 as most suitable for estimating cover crop biomass, we evaluated whether seasonal- or species-level differences were present using ANOVAs and slope and intercept contrasts. We found that significant effects (i.e., a relationship between NDVI_RE1 vs. biomass) varied between winter (October-February) and spring (March-May) samples (Appendix B; Table A1). Therefore, we opted to move forward with modeling the relationship between cover crop biomass and NDVI_RE1 with only spring data (\(n\) = 332; Figure 5), as there were only two observations in winter that exceeded 1500 kg ha\({}^{-1}\), which is the existing saturation point identified for cereal grass cover crops [14]. Furthermore, we found species-level differences between cereal rye, tritticale, and wheat (Appendix B; Table A2). In our study, cereal rye had lower observed biomass ranges than tritticale and wheat. Therefore, we further examined whether these species-level differences remained when we limited our analysis to similar biomass ranges for these three cereal grass species. Our results suggest that species-level differences were still present within this sub-analysis (results not shown).