## 4 Conclusions  
We compared vegetation phenology (vegetation indices derived from satellite remote sensing) and the dynamics of eddy covariance (EC)-measured CO\({}_{2}\) fluxes and ET during non-growing seasons (November-March) in six co-located native tallgrass prairie pastures. These pastures were managed under different burn intervals and grazing rotations and are situated in different landscape positions. Although relatively large discrepancies in vegetation phenology and eddy fluxes were observed among pastures and years during March (the time of initiation of growth of dominant warm-season C\({}_{4}\) grasses), there were also some noticeable differences in the rates and budgets of eddy fluxes among pastures even during the November-February period of the same year. The MODIS-derived vegetation indices were not fully capable of tracking those differences, most likely due to their inability to capture the presence of some cool-season C\({}_{3}\) species, which are metabolically active at low temperatures during winter, under residues and litters of tallgrass prairie. Thus, documentation of the change in species' composition (e.g., the abundance of C\({}_{3}\) species) during winter (dormant season) is necessary to explain the discrepancies in fluxes and satisfactorily validate satellite remote sensing vegetation indices for tallgrass prairie in winter. The findings of this study provide a better understanding of the dynamics of vegetation phenology and eddy fluxes in the tallgrass prairie during the dormant season. The collection of multiple years of EC data across multiple pastures and investigating the interactive effects among management practices and climatic conditions on eddy fluxes and vegetation phenology will further improve our understanding of prairie ecosystems during the dormant season. Such datasets will also aid in deriving quantitative relationships of CO\({}_{2}\) fluxes and ET with biophysical variables and improve our understanding of regional carbon and hydrologic cycles and their impacts on the mesoscale atmospheric environment.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14112620/s1](https://www.mdpi.com/article/10.3390/rs14112620/s1)]([https://www.mdpi.com/article/10.3390/rs14112620/s1](https://www.mdpi.com/article/10.3390/rs14112620/s1)). Figure S1. The experimental layout of the studied pastures. Blue dots represent the location of eddy covariance towers.  
Formal analysis, P.W.; Funding acquisition, P.W. and S.A.G.; Investigation, P.W.; Methodology, P.W.; Resources, P.W., V.G.K., P.H.G. and X.X.; Writing--original draft, P.W.; Writing--review & editing, V.G.K., P.H.G., X.X., B.K.N., J.P.S.N., P.J.S., J.L.S. and S.A.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the USDA-ARS Office of National Programs (Project number: 3070-21610-003-00D) and USDA-LTAR (Long-Term Agroecosystem Research) Network.  
We greatly acknowledge and appreciate the tremendous support of several USDA-ARS staff members for managing the pastures and assisting in data collection.  
The authors declare no conflict of interest for this study.  
The mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer.