## 5 Conclusions  
This study has uncertainties, but it offers encouragement that STARFM can be sensitive enough to detect the change in phenological parameters of vegetation that we would expect to see because of the UHI effect in urbanized landscapes. Although base pair selection for image fusion did not strongly affect the validity of fused images, it did have a noticeable effect on the phenology results. We suspect that this is in part due to the changing distribution of where no data are available for TIMESAT to fit. As the number of base pairs increases, so will the potential locations of no data from Landsat that is masked out from a prediction. As we saw with disproportional distribution of no data in one instance, these no data points might not be randomly distributed and therefore skew any results. We believe that this could have skewed the results for the 90% base pair selection because we not only added in the potential for an additional 5% unclear pixel coverage, but also due to the software change. STARFM v1.2.1 added in a strict validation that may potentially not allow a prediction to be made in areas where it did before, thus compromising a lesser sample size for an increase in overall prediction quality and not covering up any bias distribution in no data. Our statistical results are mixed, but generally, they support the notion that the UHI leads to an early start of growing season (SOS) and an overall increase in the length of the growing season(LOS). Conversely, the results show that depending on the methods used to select base pairs and the software version used to make the predictions, the effects urban areas have on the timing of the end of the growing season (EOS) can fluctuate. By using the fusion method, we were able to work toward understanding some of the advantages and challenges of modeling the heterogeneous urban landscape while preserving temporal accuracy. Overall, it allowed us to not only see a difference in phenological parameters between urban and exurban areas, but also to see that regardless of fusion method, there is variability among phenological parameters according to the land cover class at 30 m resolution. Depending on the base pair selection and version of STARFM, the differences in mean earlier urban SOS between land cover classes within each selection criteria ranged from as many as 4.5 to as few as 2.5 days, the differences in mean EOS urban delay between land cover classes ranged from 0.67 to 2.3 days, and the differences in mean longer urban LOS ranged from 3.6 to 3.9 days. Without having the temporal and spatial resolution of a fused image, these intercity variations in phenological parameters might not be observable. However, how well the changes detected correlate to actual in situ conditions and the actual timing of phenological events is unknown. Lastly, it has yet to be determined how much information that describes phenology is gained by fusing the data and to what extent errors are introduced into these estimations. The following are available online at www.mdpi.com/2072-4292/9/1/99/s1, Figure S1: Histograms of stacked data for (d1-c10) informal selection of base pairs; (d1-f10) 95% clear threshold for selection of base pairs; and (g1-i10) 90% clear threshold for selection of base pairs. **Acknowledgments:** The work was partially supported by the NASA Science of Terra and Aqua program (NNH13 ZDA001N-TERAQ). [PERSON]'s research was partially supported by the NSF-MRI award #1531511. We would like to thank [PERSON], of the USDA Agricultural Research Services, for helping confirm that our data processing procedures were correct. The USDA is an equal opportunity provider and employer. We would also like to thank [PERSON], SUNY Albany, for his discussions on statistical methods; [PERSON], SUNY Albany, for helping with converting file formats; and [PERSON], SUNY Albany, for his guidance. Last, we would like to thank the editors and the anonymous reviewers for their suggestions.