## 3 Results  
Cumulative GDD did not vary substantially among study years (Figure 2). Despite similarities in cumulative GDD during the cover crop growth period over the years, cover crop biomass production varied substantially. Destructively sampled cover crop biomass ranged from 141 to 4498 kg ha\({}^{-1}\) in spring 2019, from 3.5 to 8770 kg ha\({}^{-1}\) during 2019-2020, and from 30 to 5675 kg ha\({}^{-1}\) during 2020-2021.  
We did find evidence of species-level differences in fractional vegetative cover at both low, medium, and high biomass levels (Figure 3). However, we did not have sufficient observations to conduct a formal analysis to determine whether these differences were statistically significant. At low biomass levels (~1000 kg ha\({}^{-1}\)), trticlele appears to have a higher fractional vegetative cover than cereal rye and wheat. As no cereal rye biomass levels exceeded 4000 kg ha\({}^{-1}\), we were not able to compare the highest biomass ranges between all three species. However, our in situ photographic data indicated that cereal rye was patchier than wheat and trticlele at all biomass levels, with the caveat that we obtained limited cereal rye photographs and a gap of >7 days from photographs and biomass harvests in 2019-2020.  
Figure 3: Monopole mounted (\(\sim\)3 m), nadir photographs of crop species at various biomass levels. fractional vegetative cover estimates are displayed in the bottom right corner, and values with “*” indicates a difference in \(>\)7 days from when a photograph was collected and when that sample was harvested, dried, and weighed for biomass. Fractional vegetative cover was estimated empirically by applying threshold values for red–green ration and excess green optical vegetation indices.  
Figure 2: Accumulated growing degree days (GDD, including negative values) for study years (2018–2021) with sample dates (points).  
We also examined the spectral response from Sentinel-2 for each species at a low and high biomass (Figure 4). As anticipated, we observed a greater NIR reflectance and decreased red reflectance at high biomass (>3000 kg ha\({}^{-1}\)) compared to low biomass (<1000 kg ha\({}^{-1}\)), which is, in part, related to the exposed soil present at low ground cover. Similarly, the reflectance of the first red-edge band (B5) decreased, while the second and third red-edge bands (B6 and B7) increased reflectance with increasing cover crop biomass levels. Interestingly, in the SWIR portion of the spectrum, reflectance from low biomass exceeded thoseof high biomass for all species, but the magnitude of this difference was smallest in cereal rye.  
Figure 4: Example of spectral reflectance of high (\(\sim\)3500 kg ha\({}^{-1}\)) and low (<1000 kg ha\({}^{-1}\)) biomass for (**A**) wheat, (**B**) triticale, and (**C**) cereal rye. Sentinel-2 bands are labeled in each panel.  
Finally, we examined the relationship between handheld soil moisture collections and SAR metrics. The results showed unexpectedly poor relationships (R2 = 0-0.08) between soil moisture and backscatter intensities, polarimetric parameters, and InSAR coherence (Appendix A). Hence, we did not further employ remote sensing estimates of soil moisture in our analyses due to the high uncertainty in calibration.