## 3 Results
### GPP Models Performance
Only \"Flight\" and \"Daily\" _RRMSE_ of the simplest (MOD1) and the most complex (MOD4) models are shown in the first two rows. Below, _PAR_ (Figure 6g-i), \(T_{\text{air}}\) (Figure 6j-l), _VPD_ (Figure 6m-o), \(\textit{GPP}_{\text{hh}}\) (Figure 6p-r) and footprint-weighted \(\textit{NDVI}_{\text{fp}}\) (Figure 6s-u) are presented. Results show that \"Flight\" MOD1 (Figure 6a-c) and MOD4 (Figure 6d-e) _RRMSE_ increase, compared to \"Daily\" models, in periods when meteorological variables largely differ from those found the same days of the airborne campaigns. The days following the flight campaign in April 2014 are more simi \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Statistics** & **MOD1** & **MOD2** & **MOD3** _PRI_ & **MOD3** _PRI_\({}_{\text{SIS}}\) & **MOD3** _PRI_\({}_{\text{norm}}\) & **MOD3** _CPRI_ & **MOD4** \\ \hline \multicolumn{6}{l}{Models fit with \(S_{\text{fp,avg}}\)} \\ \hline \(R^{2}\) & 0.65 & 0.67 & 0.67 & 0.68 & 0.67 & 0.67 & 0.72 \\ _RRMSE_ & 35.89 & 37.39 & 37.66 & 36.71 & 37.32 & 37.09 & 37.19 \\ _AIC_ & 460.15 & 460.23 & 464.25 & 464.19 & 464.23 & 464.22 & 470.22 \\ \hline \multicolumn{6}{l}{Models fit with \(S_{\text{fp,all}}\)} \\ \hline \(R^{2}\) & 0.64 & 0.66 & 0.66 & 0.67 & 0.67 & 0.67 & 0.71 \\ _RRMSE_ & 36.11 & 37.71 & 39.64 & 36.85 & 37.54 & 37.13 & 37.32 \\ _AIC_ & 460.16 & 460.25 & 464.35 & 464.20 & 464.24 & 464.22 & 470.23 \\ \hline \multicolumn{6}{l}{Models fit with \(S_{\text{fp,all}}\)} \\ \hline \(R^{2}\) & 0.65 & 0.68 & 0.67 & 0.69 & 0.67 & 0.69 & 0.72 \\ _RRMSE_ & 36.38 & 37.70 & 65.40 & 36.85 & 37.88 & 36.90 & 37.75 \\ _AIC_ & 494.26 & 494.34 & 499.44 & 498.29 & 498.35 & 498.29 & 504.34 \\ \hline \multicolumn{6}{l}{Models fit with \(S_{\text{fp,all}}\)} \\ \hline \(R^{2}\) & 0.65 & 0.70 & 0.70 & 0.69 & 0.69 & 0.69 & 0.69 & 0.72 \\ _RRMSE_ & 36.31 & 36.60 & 36.71 & 36.50 & 36.62 & 36.75 & 36.50 \\ _AIC_ & 494.26 & 494.28 & 498.28 & 498.27 & 498.28 & 498.29 & 504.27 \\ \hline \multicolumn{6}{l}{Models fit with \(S_{\text{fp,100,all}}\)} \\ \hline \(R^{2}\) & 0.64 & 0.69 & 0.69 & 0.69 & 0.69 & 0.70 & 0.72 \\ _RRMSE_ & 36.97 & 36.67 & 37.00 & 39.34 & 36.55 & 38.85 & 36.70 \\ _AIC_ & 494.30 & 494.28 & 498.30 & 498.42 & 498.27 & 498.40 & 504.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistics for the different model fits using the “Flight” dataset as well as the weighted spectral signature of vegetation covers or different synthetic pixel sizes. this campaign was surrounded by cloudy days where _PAR_, \(T_{\rm air}\) and _VPD_ dropped, increasing errors as well as the distance between \"Flight\" and \"Daily\" _RRMSE_. In general, MOD1 is more sensible to these changes since it does not include meteorological variables. In July 2015 _RRMSE_ is rather sensitive to changes in \(T_{\rm air}\) and _VPD_, and it is remarkable that MOD4 has difficulties to predict _GPP_hh under large VPD conditions. In addition, on these dates, the metric _RRMSE_ is more sensitive to errors since _GPP_hh is low. Figure 5: Predicted-Observed statistics of the models: MOD1 (**a,b**); MOD2 (**c,d**); MOD3-_PRI_ (**e,f**); and MOD4 (**g,h**). Daily ranges of variation and median of the input variables: \(\textit{NDVI}_{\rm fp}\) (**i**); \(\textit{PRI}_{\rm fp}\) (**j**); photosynthetically active radiation (_PAR_) (**k**); and measured: \(\textit{GPP}_{\rm hh}\) (**l**); _VPD_ (**m**); and \(T_{\rm air}\) (**n**).