## 3 Results
### Validation and Inter-Comparison of MODIS-Based Metrics  
The mean ground-estimated SGS and EGS values estimated from the various techniques were used as validation of the MODIS data, although the ground-estimated EGS dates were considered less reliable due to the high degree of variability in the results. Significant relationships were found between  
Figure 6: The relationships between the four different PRIs and flux tower measured light use efficiency (LUE). The red lines are a simple linear regression lines. Figure 7: The relationship between NDVI\({}_{\text{maiac}}\) and s\({\text{PRI}}_{1}\) and flux tower measured gross primary productivity (GPP). The red lines are simple linear regression lines. all MODIS VI-predicted SGS dates and ground-derived SGS, except for the FFT and Savitzky-Golay smoothed NDVI\({}_{\text{prod}}\) time series (Table 3). The MAIAC-based metrics yielded considerable higher \(R^{2}\) values in comparison with NDVI\({}_{\text{prod}}\). SGS dates estimated using NDVI\({}_{\text{maiac}}\) all strongly correlated with NEE SGS with \(R^{2}\) values between 0.6 and 0.79 (Table 3). SGS estimates based on the sPRI\({}_{1}\) time series smoothed using a double sigmoidal function exhibited the highest \(R^{2}\) value (\(R^{2}\) = 0.8) and had a relatively low RMSE (RMSE = 9.8 days). All except one of the MODIS VIs predicted the SGS earlier in the year than the ground data (the bias values are all negative) (Table 3). Conversely, all MODIS EGS dates were estimated to be later in time than that predicted from the ground-measured NEE. PAR did not correlate significantly with the NEE phenology estimates using any of the four methods. Likewise, no significant correlations were found between any of the satellite and ground-predicted EGS dates (Table 4). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vegetation Index** & **Method** & \(\mathbf{r}\) & \(\mathbf{R^{2}}\) & **RMSE** & **Bias** \\ \hline \multirow{4}{*}{sPRI\({}_{1}\)*} & Gaussian & 0.26 & 0.03 & 17.6 & 14.9 \\  & Double Sigmoidal & 0.25 & 0.03 & 17.6 & 16.5 \\  & FFT & 0.18 & 0.05 & 26.9 & 26.4 \\  & Savitzky–Golay & \(-\)0.26 & 0.06 & 25.1 & 23.9 \\ \hline \multirow{4}{*}{NDVI\({}_{\text{maiac}}\)*} & Gaussian & \(-\)0.27 & 0.02 & 22.5 & 21.3 \\  & Double Sigmoidal & 0.01 & 0.00 & 21.2 & 19.6 \\  & FFT & \(-\)0.07 & 0.00 & 31.3 & 30.7 \\  & Savitzky–Golay & \(-\)0.49 & 0.18 & 25.8 & 24.6 \\ \hline \multirow{4}{*}{NDVI\({}_{\text{prod}}\)**} & Gaussian & 0.33 & 0.05 & 32.8 & 29.0 \\  & Double Sigmoidal & \(-\)0.08 & 0.01 & 29.6 & 32.5 \\  & FFT & \(-\)0.19 & 0.02 & 37.0 & 36.2 \\  & Savitzky–Golay & \(-\)0.42 & 0.25 & 26.3 & 25.5 \\ \hline \multirow{4}{*}{PAR **} & Gaussian & \(-\)0.43 & 0.17 & 29.3 & \(-\)28.5 \\  & Double Sigmoidal & \(-\)0.51 & 0.41 & 57.5 & \(-\)56.5 \\ \cline{1-1}  & FFT & \(-\)0.13 & 0.00 & 25.0 & \(-\)22.3 \\ \cline{1-1}  & Savitzky–Golay & 0.26 & 0.11 & 26.9 & \(-\)26.2 \\ \hline \hline \end{tabular}
* Sample size = 12; ** Sample size = 13.  
\end{table} 
Table 4: Statistics of correlation between the end of the growing season (EGS) derived from ground measured NEE and EGS derived from MODIS VI time series and PAR. \(R^{2}\): Coefficient of determination; \(r\): rho from Spearman Rank’s correlation; RMSE: Root mean squared error. All tested relationships were insignificant (\(p\)-value > 0.05).