## 3 Results
### GPP Models Performance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{a_{3}}\) & \(\mathbf{a_{4}}\) & \(\mathbf{a_{5}}\) & \(\mathbf{a_{6}}\) & \(\mathbf{a_{7}}\) \\ \hline \multirow{2}{*}{MOD1} & \(0.654\in\) & \(-0.112\in\) & & & & & \\  & \((0.592,0.724)\) & \((-0.143,-0.082)\) & & & & & \\ \hline \multirow{2}{*}{MOD2} & \(1.186\in\) & \(-0.208\in\) & & & & & \\  & \((1.071,1.295)\) & \((-0.261,-0.156)\) & & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} MOD3} & \(4.022\in\) & \(-0.553\in\) & \(4.063\in\) & \(0.540\in\) & & \\ \(PRI_{\text{1}}\) & \((1.197,5.761)\) & \((-0.827,-0.190)\) & \((-3.556,5.823)\) & \((0.202,1.047)\) & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} MOD3} & \(-0.163\in\) & \(2.415\in\) & \(2.166\in\) & \(-0.042\in\) & & \\ \(PRI_{\text{515}}\) & \((-3.149,2089)\) & \((-0.237,3.633)\) & \((-0.439,3.995)\) & \((-0.143,1.237)\) & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} MOD3} & \(0.990\in\) & \(-0.071\in\) & \(0.965\in\) & \(1.066\in\) & & \\ \(PRI_{\text{nom}}\) & \((0.849,1.102)\) & \((-0.183,0.315)\) & \((-0.272,1.162)\) & \((0.755,1.329)\) & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} MOD3} & \(1.014\in\) & \(-0.226\in\) & \(0.906\in\) & \(0.826\in\) & & \\ \(CPRI\) & \((0.771,1.480)\) & \((-0.397,-0.108)\) & \((-0.351,1.534)\) & \((0.162,1.426)\) & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} MOD4} & \(1.644\in\) & \(-2.723\in\) & \(2.186\in\) & \(-7.809\in\) & \(11.490\in\) & \(2.362\in\) & \(-0.433\in\) \\ \cline{1-1}  & \((0.375,2.832)\) & \((0.844,134.347)\) & \((0.127,23.907)\) & \((-15.000,6.360)\) & \((1.137,37.015)\) & \((2.119,3.163)\) & \((-0.657,-0.309)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters with 95% confidence intervals for the different models retrieved using the “Flight” dataset. MOD-_PRI_ are shown. Different _PRI_ formulations show almost identical performances. In addition, Figure 5i-n presents the range of variation and the median of the variables used to fit the models using daytime data of each of the \"Daily\" datasets, which integrate data from the 11 overpasses. As can be observed, in the \(\pm\)15 days period around the flight campaigns, the spectral indices \(\textit{NDVI}_{\text{fp}}\) (Figure 5i) and \(\textit{PRI}_{\text{fp}}\) (Figure 5j) as sampled from the same image show small variations in terms of range or median values; nonetheless the second is more variable. Meteorological variables show larger variability: _PAR_ (Figure 5k) keeps ranges relatively constant in the period analyzed while the median shows large variations. Maximum median values match airborne campaigns. _VPD_ (Figure 5m) and \(T_{\text{air}}\) (Figure 5n) show larger variations both in median and ranges compared to PAR. \(\textit{GPP}_{\text{hh}}\) (Figure 5l) also shows a pronounced variability and maximum values not always occur during the campaigns. Predicted-observed statistics compare the performance of two models fit using the \"Flight\" and \"Daily\" datasets. MOD1 \(R^{2}\) (Figure 5a) are the same for the \"Flight\" and the \"Daily\" models since these are just a linear combination of one predictive variable. However, _RRMSE_ (Figure 5b) differ and show that \"Daily\" models perform better in periods where meteorological conditions were different compared to those of the day of the flight. Differences are mainly noticed in the period before flight campaigns, when also maximum \(\textit{GPP}_{\text{hh}}\) reaches lower values than the rest of the month considered in the analysis. For MOD2 (Figure 5c,d), MOD3-_PRI_ (Figure 5e,f) and MOD4 (Figure 5g,h), \"Flight\" and \"Daily\" \(R^{2}\) and _RRMSE_ differences are smaller. Figure 6 shows a deeper analysis on these effects. Each column summarizes data from a single flight campaign. For comparison, we only present the campaigns where the three eddy flux towers were operative: April 2014 (first column), April 2015 (second column) and July 2015 (third column).