## 3 Results
### Ground-Derived Metrics  
SGS and EGS derived from both the raw and smoothed NEE data as well as the mean of the predicted dates are displayed in Figures 3 and 4. SGS was well defined for each year as the values were relatively consistent based on all methods except the Gaussian smoothing method, which produced slightly different results. SGS dates were mostly between Julian dates 70 and 110; however, in 2014, SGS were predicted to occur earlier than all other years, around Day 60-80. NEE-derived SGS tended to follow the timings of air temperatures exceeding a couple of degrees above 0 \({}^{\circ}\)C. An example of this is given in Figure 5, which displays SGS dates predicted from raw NEE values and seasonal variations in air temperatures (5-day moving average) for 2013 and 2014. EGS was less clearly defined from the ground data. The different methods applied produced values that were highly inconsistent relative to the inter-annual variability in the data (Figure 4). No overall long-term trends in the SGS and EGS metrics could be detected in the flux data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**VI** & \multicolumn{2}{c}{**NDVI\({}_{\text{maic}}\)**} & \multicolumn{2}{c}{**sPRI\({}_{\text{1}}\)**} & \multicolumn{2}{c}{**sPRI\({}_{\text{4}}\)**} & \multicolumn{2}{c}{**sPRI\({}_{\text{10}}\)**} & \multicolumn{2}{c}{**sPRI\({}_{\text{12}}\)**} \\ \hline
**Parameter** & **LUE** & **GPP** & **LUE** & **GPP** & **LUE** & **GPP** & **LUE** & **GPP** & **LUE** & **GPP** \\ \hline \(R^{2}\) & 0.16 & **0.47** & 0.13 & **0.63** & 0.01 & 0.14 & 0.06 & 0.02 & 0.00 & 0.11 \\ \(r\) & 0.51 & 0.73 & 0.39 & 0.80 & 0.04 & 0.34 & 0.40 & 0.22 & \(-\)0.07 & 0.25 \\ \(p\)-value & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & 0.24 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & 0.06 & \textless{}0.05 \\ \hline \end{tabular}
\end{table}
Table 2: Statistics of correlation analysis between GPP/LUE and NDVI and four different PRIs. The highest \(R^{2}\) values observed are highlighted in bold and insignificant relationships are marked in grey. \(R^{2}\): Coefficient of determination, \(r\): correlation coefficient (rho from [PERSON]’s test significant at \(p\) < 0.05). Sample size = 766.  
Figure 2: Times series of flux tower measured scaled Net Ecosystem Exchange (NEE) **(a)** and MODIS vegetation indices smoothed according to four different methods: Application of Gaussian functions, double sigmoidal functions, fast Fourier transform (FFT) and a Savitzky–Golay filter. The MODIS time series are: MAIAC-processed scaled PRI (**b**); and NDVI data **(c)**; and MODIS standard NDVI product MOD13Q1 **(d)** and **(e)** scaled PAR.  
Figure 3: Estimated start of growing season (SGS) based on daily Net Ecosystem Productivity (NEE), the three different MODIS vegetation index time series (NDVI\({}_{\text{maiac}}\), sPRI\({}_{1}\) and NDVI\({}_{\text{prod}}\)) and ground-measured photosynthetically active radiation (PAR).  
Figure 4: Estimated end of growing season (EGS) based on raw and smoothed daily Net Ecosystem Productivity (NEE) data, the three different MODIS vegetation index time series (NDVI\({}_{\text{maiac}}\), sPRI\({}_{1}\) and NDVI\({}_{\text{prod}}\)) and ground-measured photosynthetically active radiation (PAR).  
SGS and EGS estimates based on ground-measured PAR varied considerably among the different methods relative to the low inter-annual variability and the dates were generally predicted to occur earlier in the year than those predicted by NEE.