## 4 Discussion
Focusing on the midsummer peak (July) values, as suggested by [PERSON] and [PERSON] (2009) [45], effectively removed the early-season carryover error and the late-season senescence phase, and resulted in more accurate biomass estimation (\(R^{2}\) = 0.97, Figure 8). Interestingly, despite the seasonal NDVI-green biomass shifts, there were strong correlations between peak-season (July) and end-of-season (August) values for NDVI\({}_{\text{G}}\) (\(R^{2}\) = 0.99, not shown) and log green standing biomass (\(R^{2}\) = 0.99, not shown), suggesting a potential to estimate end-of-season biomass from peak-season measurements or vice versa. Another potential source of error in NDVI measurements can be the background contamination from soil, litter, snow, surface wetness, or low-growing vegetation, which can confound satellite-based assessments of prairie productivity and ground cover [24; 47]. For instance, [PERSON] and [PERSON] (1996) determined that the presence of _Selagintella densa_, a mat-forming member of the fern phylum that is present in dry mixed-grass prairie, can also lead to errors in biomass estimates from optical remote sensing [48]. _Selagintella densa_ mimics sparse grasses in NDVI measurements, yet it does not grow tall enough to be harvested with the methodology used here, so it was not included in our biomass data. Accounting for this effect could reduce scatter in the NDVI-green biomass relationship. By adapting the harvest sampling method to include very low-lying green vegetation in biomass estimates, this \"background\" green vegetation could be explicitly included in the analysis. Error resulting from this phenomenon would vary with rainfall and productivity. For example, drought could decrease the standing component of the vegetation and could allow more reflectance from lower canopy layers. On the other hand, early in the growing season during a drought year there can be more masking of the slowly emerging green biomass by the litter that is present from previous growing seasons (the carryover effect). This would decrease the amount of green vegetation visible to the spectrometer. Both spatial aggregation and season affected the NDVI-biomass relationships (Figure S1), and these findings indicate that careful consideration of sample timing and methods of data aggregation is warranted when developing a biomass monitoring program from satellite data. Selection of peak-season data aggregated at the township scale (approximately 93 km\({}^{2}\)) largely removed these complications, and yielded a very strong agreement between NDVI and standing green biomass (Figure 9), allowing a clear comparison of year-to-year productivity differences (Figure 7). With the exception of SPOT NDVI, interannual changes in NDVI and standing green biomass (yield) were very similar for the different sensors (Figure 7), which indicates that NDVI can be an effective proxy for the standing green biomass in the dry mixed-grass prairie. The different shapes of these seasonal trends for NDVI and standing green biomass can be largely attributable to the non-linear relationship between NDVI and biomass (Figures 6 and 8), which has been reported before [26; 32]. The curvilinear relationship between green biomass and NDVI creates an apparent \"saturation problem,\" where over a certain value NDVI becomes almost invariant to changes in vegetation amount and/or condition [26; 30; 49]. This saturation, a natural result of the shape of the NDVI-biomass relationship [26], can largely be explained by the exponential extinction of radiation in vegetation canopies [50]. This relationship can be readily linearized by taking the log of biomass (Figure 8), revealing the true nature of the relationship between NDVI and biomass.