## 5 Conclusions  
In this paper, we utilized three ground reference data sets for evaluating the performance of five smoothing methods in processing MODIS NDVI time-series. We conclude that the smoothing methods reduced the error between MODIS NDVI and ground-measured NDVI in 89% of the simulations, and that they improved the covariation between MODIS NDVI and ground-measured GPP in 98% of the simulations. It is possible to achieve very good similarity with ground NDVI data with the local filtering methods (Savitzky-Golay and LOESS smoothing) when parameters are finely tuned. However, on average the differences between the methods were small and it is not possible to conclusively recommend a single method in favor of the others. Spline smoothing did not generate better results than any of the other methods, but using cross validation generally improved the spline fits and gave fairly good results. Cross validation was also useful for selecting parameters for Savitzky-Golay and LOESS smoothing; however, in some cases the method failed. The function fitting methods (double logistic and asymmetric Gaussian functions) showed the best performance for estimating average phenology across a topographic gradient. The function fitting methods were also found to generally reduce the risk of achieving very poor results (shown by the smaller range of output results), thus they are safer than the other methods to use when it is not possible to carry out any calibration against ground measurements. **Supplementary Materials:** The following material is available online at www.mdpi.com/2072-4292/9/12/1271/s1. Figure S1: Geographic distributions of ground-based spectral observation sites and Ammar region, Figure S2: Boxplots of RMSEs between ground-based measured NDVI and smoothed satellite NDVI, Figure S3: Plots of smoothed curves of each method at all testing sites based on the settings that provide the minimum RMSE values, Table S1: Additional information of ground-based spectral observation sites and flux tower sites, Table S2: The minimum, median and range of RMSE between smoothed MODIS NDVI from all settings of each smoothing algorithm and ground measured NDVI from all spectral sensor tower sites, Table S3: Maximum, median and range of \(\rho\) between smoothed MODIS NDVI from all settings of each smoothing algorithm and ground measured GPP, Figure S4: Boxplots of [PERSON]'s rank correlation coefficient of tower GPP and smoothed MODIS NDVI for each method, showing the distribution with different settings at test sites, Table S4: The maximum, median and range of \(\rho\) between elevations and land surface phenology parameters extracted by each smoothing algorithm of all settings on MODIS NDVI. **Acknowledgments:** The research was funded by a grant from the Swedish National Space Board to [PERSON] and [PERSON], Dnr. 119/13. We acknowledge access to flux data from ICOS Abisko station, Hyytialala station, Norunda station Sweden, ICOS Finland, and Flavanet. We thank Dr. [PERSON] for providing spectral data for the Sudan sites. The MDO9Q1 data product was retrieved from the online Data Pool, courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, [[https://lpdaac.usgs.gov/data_access/data_pool](https://lpdaac.usgs.gov/data_access/data_pool)]([https://lpdaac.usgs.gov/data_access/data_pool](https://lpdaac.usgs.gov/data_access/data_pool)). **Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed analysis tools and field equipment; [PERSON] and [PERSON] wrote the manuscript draft with further contributions by [PERSON] and [PERSON]. **Conflicts of Interest:** The authors declare no conflict of interest.