## 5 Results and Discussion
### Ecological Effectiveness
#### 5.2.1 Comparing Daily Values  
Figure 11 exhibits multiple time series at once, over the period September 2000 to August 2013, when the GPP data are available: the little circles represent the 215 average FAPAR values available for that period, estimated as explained above. The four sets of smooth colored dashed curves represent the model fits, and the highly variable grey trace shows the daily values of GPP, derived from the Skukuza flux tower EC measurements. The traces of the four models largely overlap each other when the temporal distribution of the FAPAR data is adequate and when all models exhibit a very similar performance in simulating the phenology (for seasons peaking in early 2002, 2003, 2004, 2006, 2009, and 2013). By contrast, during the seasons peaking in early 2001, 2005, 2007, 2008, 2010, 2011, and 2012, the Parametric Double Sine Function (PDSF) model did not converge to a useful solution, so its trace is absent from the Figure. In these latter cases, it can be seen that the traces of Parametric Double Hyperbolic Tangent Function (PDHyTgF) and Parametric Double Logistic Function (PDLF) coincide, as well as that the Parametric Double Gaussian Function (PDGF) model delivered a different simulation, clearly distinguished from the other two. Figure 10: Number of successfully simulated vegetative seasons. The four left-most panels report the results for the Parametric Double Gaussian Function (PDGF), Parametric Double Hyperbolic Tangent Function (PDHyTgF), Parametric Double Logistic Function (PDLF), and Parametric Double Sine Function (PDSF) models, respectively. The fifth panel shows the success rate when the most successful model is selected for each season at the sample and line locations in the target area. Table 2 quantifies the correlation between daily FAPAR estimates generated by the four models (PDGF, PDHyTgF, PDLF, and PDSF) and the corresponding daily GPP record retrieved from an analysis of the EC flux tower, for each of the 13 vegetative seasons available. The first column reports the dates of the start and end of those seasons and the second refers to the season number. The third and fourth columns indicate the number and the proportion (in%) of days in the corresponding seasons for which GPP measurements were available, respectively. The next four columns report the Pearson correlation coefficient between model-generated daily estimates of FAPAR and the daily GPP values derived from in situ measurements for the same days. The last column shows the same statistics for the combined model, i.e., when the best (smallest \(\chi^{2}\)) model is chosen for each year. Label A in the four model columns flags cases where fewer than half of the daily GPP values were available for the concerned season (as shown in the fourth column, and also in Figure 11). Label B in those columns flags cases where the model values are unavailable, typically because the temporal distribution of the FAPAR data did not permit the model inversion. Vegetative season numbers set in bold face within the second column point to cases where the number of daily GPP was sufficient (more than 50% of the days) and where all four parametric double S-shaped functions could be successfully inverted against the FAPAR data. Correlation coefficients between the daily GPP and daily estimates derived from the PDGF, PDHyTgF, PDLF and combined model approaches are always equal to or larger than 0.69 in seasons 2 to 5 and 9 to 12. This coefficient is much lower in the case of season 1 because (1) a long gap in the remote sensing data, due to persistent cloudiness, hampers the model inversion procedure and (2) the GPP data suggest a noticeable but unexpected double vegetative season during that period, unconfirmed by the remote sensing record. The number of daily GPP measurements available in seasons 6, 7, and 8 was too low to compute that statistic.