## 4 Discussion
### Study Assumptions and Potential Limitations  
The method we used for normalization ensures that all data obtained are used. It also corrects for interannual shifts in phenological parameters by placing the event at each location in relation to the expected date of the same exurban event for the same year. This allows for the comparison of the difference in time between an event at each location and the timing of the corresponding expected exurban area's event and not a comparison of the actual dates, which may not be comparable from one year to the next due to changing weather patterns. Thus, after normalization, one can compare the urban and exurban data to show the differences within each separate year and also among all years together. Importantly, the approach accounts for the uneven and inconsistent sample proportions between the urban and exurban environments from year to year. Simply normalizing according to the mean value of all urban and exurban pixels would pull the normalized value in different amounts, based on the proportional size, toward the sample with the larger size and not centering the normalized values at the same value from year to year. For example, if one year had one exurban point for every two urban points and the next year this ratio decreased to 1:4, the normalized values of the second year would be pulled closer to zero for the urban areas and further from zero for the exurban areas, making them incomparable. However, an underlying assumption of this normalization method is that if a shift in dates does occur from one year to the next, the amount of the shift is the same for both the urban and exurban environment. If this assumption is violated, any disproportional interannual shifts would skew the results to either be over or under represented depending on if the urban sample size was large or small for that year. A second underlying assumption of this study is that the expected vegetation type and management practices were similar between like land cover classes for both the urban and rural environments. For example, it was assumed that any influences a homeowner may have on phenology due to landscing practices (i.e., watering the lawn in the spring) and vegetation transplanting (i.e., planting grass for a lawn) were just as likely to occur regardless if the individual lives in a rural and urban settings. This is an important assumption, as it has been observed that phenological traits can differ between vegetation types [51]. However, making comparisons between the urban and rural environments of developed areas with the same land cover classes controlled for this, as opposed to comparing dissimilar land cover classes that may have dissimilar landscape management techniques. The third assumption is that changes in NDVI values accurately represent changes in phenological parameters. The current study used changes in NDVI as a proxy for growing season parameters. However, it is not well understood how well these values, derived from fused imagery, correspond to the actual ground events of SOS, EOS, and LOS. The Collection 5 MODIS NBAR data used in this study were the 8-day overlapping product that is collected over 16 days. Depending on the data availability during the 16-day period especially around the SOS and EOS dates, we could miss or smooth the small changes from the multi-date data products. The fourth limitation is that we did not attempt to resolve any potential errors with the NLCD classifications. Although overall accuracies were 79% and 78% for the previous Level II NLCD versions from 2001 and 2006, respectively, they did have difficulty distinguishing the context of grass. In relation to the land cover classes we looked at, the 2006 NLCD had a producer's accuracy of 42%, 70%, 80%, and 26% for classes 21, 22, 23, and 24, respectively. The user's accuracies for these classes were 52%, 59%, 69%, and 83%, respectively. The 2001 NLCD accuracy was similar (within 5%) to the 2006 accuracies except for the producer's accuracy for class 24, which was 81% [58].