## 5 Conclusions  
Estimating cover crop performance is important in quantifying ecosystem services, yet few works to date have evaluated remote sensing approaches to characterize cover crop performance across the landscape. The results from our study demonstrated that estimating cover crop biomass is feasible using VIs alone, as well as by integrating optical and SAR remote sensing, but that season- and species-level effects were also present. We found that the normalized difference red-edge index Sentinel-2 with band five (NDVI_RE1) and Sentinel-1 InSAR coherence provided the best predictions of cereal grass cover crop biomass. These findings may broadly support conservation efforts in the Chesapeake Bay region, which focus on incentivizing cereal grass cover crops and mixes as they decrease nutrient flow into local waterways, which may help to address the 2030 United Nations Sustainable Development Goal to \"ensure availability and sustainable management of water and sanitation for all.\" However, further research is needed to enhance the prediction of higher biomass levels (>1900 kg ha\({}^{-1}\)), which SAR metrics were ineffective at improving based on the predictive capacity of NDVI_RE1 alone. To address this continued concern, future work could consider the combination of weather and climate variables, machine learning algorithms, integration of proximal sensing and satellite observations, as well as the integration of process-based crop-soil simulation models and remote sensing observations.  
Conceptualization, J.S.J., B.T.L., W.D.H., S.B.M., A.T. and R.T.; methodology, J.S.J., B.T.L., W.D.H., S.B.M., A.T. and A.G.; software, J.S.J. and B.T.L.; validation, J.S.J.; formal analysis, J.S.J.; investigation, J.S.J., B.T.L., W.D.H., S.B.M., A.T. and R.T.; resources, S.B.M., W.D.H.; data curation, J.S.J., B.T.L., A.T., A.G.; writing--original draft preparation, J.S.J. and B.T.L.; writing--review and editing, J.S.J., B.T.L., W.D.H., S.B.M., A.T., R.T. and A.G.; visualization J.S.J. and A.G.; supervision, W.D.H. and S.B.M.; project administration, S.B.M. and W.D.H.; funding acquisition, S.B.M. and W.D.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project was supported by the U.S. Geological Survey Land Change Science Program within the Land Resources Mission Area; the U.S. Department of Agriculture--Agricultural Research Service, National Program 211; and the U.S. Department of Agriculture Lower Chesapeake Long Term Agricultural Research (LTAR) program. Additionally, this work is supported by the Agriculture and Food Research Initiative's Sustainable Agricultural Systems Coordinated Agricultural Project [award no. 2019-68012-29818] and Water Coordinated Agricultural Project [award no. 2018-68011-28372] from the United States Department of Agriculture (USDA) National institute of Food and Agriculture.  
**Data Availability Statement:** Data supporting this analysis are published and available for download [55].  
**Acknowledgments:** Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.  
**Conflicts of Interest:** The authors declare no conflict of interest.