## 5 Conclusions  
Our results demonstrate accurate NDVI-based estimates of biomass production and phenology in a mixed-grass prairie, although seasonal shifts in the NDVI-biomass relationships emerged from temporal patterns of carryover and senescence. These findings indicate that careful consideration of sample timing to consider seasonal effects on the NDVI-biomass relationship is warranted when developing a biomass monitoring program from satellite data. Midsummer peak values avoid the early-season carryover error and the late-season senescence phase, providing the most accurate biomassestimation (\(R^{2}\) = 0.97). Similarly, averaging field data to the township level greatly improved the agreement with satellite data, illustrating the importance of spatial aggregation when comparing data from vastly different scales. This approach demonstrates the value of applying experimental methods to remotely sensed data collected in different ways, and at different times and spatial scales. With these methods, the changes in biomass due to variable precipitation between the two years was clearly identifiable; NDVI illustrated that the wetter 2010 growing season had much greater biomass than the drier 2009 season. Clearly, the inter-annual vegetation and NDVI patterns were affected by the moisture regime available for plant growth during the two growing seasons studied. The MODIS Terra platform provided the best estimate of NDVI of the three satellite platforms studied, followed closely by the MODIS Aqua platform, and SPOT provided the least accurate estimates, in part due to the more limited SPOT coverage. We conclude that satellite NDVI, calibrated against field measurements, is a powerful tool for monitoring yield in a dry mixed-grass prairie ecosystem and can benefit ongoing management efforts. Future studies could focus on combining yield monitoring with other co-benefits, including habitat, biodiversity, and carbon sequestration.  
The following are available online at www.mdpi.com/2072-4292/8/10/872/s1, Figure S1: Effect of spatial aggregation and temporal filtering, Table S1: Mean site coordinates.  
**Acknowledgments:** This project was primarily funded by the Agricultural Financial Services Corporation (AFSC) of Alberta, with additional funding from NSERC, iCORE/AITF, and the Earth Analytics Group to [PERSON]. We thank the staff of AFSC for support and patience over the life of this project. We are also indebted to several hard-working field assistants, including [PERSON], [PERSON], [PERSON], and [PERSON], whose long hours in the field and in the lab enabled successful completion of this project. [PERSON] assisted with analysis of the SPOT data. We would also like to thank [PERSON], [PERSON], and the four anonymous reviewers who provided constructive comments on the manuscript.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] designed the experiment (in coordination with other staff at Agricultural Financial Services Corporation). [PERSON] collected, processed, and analyzed the data. [PERSON] and [PERSON] wrote the paper, and [PERSON] and [PERSON] contributed to revisions.  
**Conflicts of Interest:** The authors declare no conflict of interest.