## 3 Results
### Comparison of the Different Models  
Values of accuracy metrics for the three models and for midday average fluxes are reported in Table 3. Similarly, Table 4 shows accuracy metrics for daily average fluxes. The values of these two tables directly reflect trends and correlation coefficients previously presented in Figure 4. No exceptions were found in our study, as high values of R\({}^{2}\) corresponded always to low values of RMSE and AIC.  
The best model for the entire growing season considering Landsat VIs was always obtained with LSWI for NEE and EVI for GPP in a direct correlation (model 1), either with midday or daily average fluxes. In contrast, NDVI\({}_{\text{PV}}\) gave lower R\({}^{2}\) values in all models and for both aggregations of flux data (midday and daily) when the whole growing season was considered.  
Overall, separate fits gave higher R\({}^{2}\) and lower RMSE for all models than considering the whole growing season. For the wet phase, NDVI was the best predictor of midday aggregates for both GPP and NEE with all three model types. For daily aggregates, NDVI was the best vegetation index only in models 2 and 3 whereas in model 1, LSWI and GNDVI were the best vegetation indices for NEE and GPP respectively. For the dry phase, MNDWI was the best predictor of midday aggregates of GPP and NEE with all three model types. For daily aggregates, MNDWI was the best VI except in model 2 and model 1 with NEE, where LSWI gave the highest R\({}^{2}\).  
Midday fluxes gave higher R\({}^{2}\) than daily fluxes during the wet phase for the model 1 (direct correlation carbon fluxes-VIs). However, during the dry phase (all models) and also the wet phase with models including PAR or Rg (2 and 3), daily fluxes gave higher R\({}^{2}\) than midday fluxes.  
The bootstrapped R\({}^{2}_{\text{boot}}\) values were very similar to the R\({}^{2}\) values, with very narrow 95% confidence intervals. The only time where the best results differ between R\({}^{2}\) and R\({}^{2}_{\text{boot}}\) was for midday estimate of NEE, which seems to be the best with MNDWI when considering R\({}^{2}\) and LSWI if R\({}^{2}_{\text{boot}}\) were considered instead.  
Based on these accuracy metrics, the best regression models are presented in Table 5, according to the two aggregates (midday and daily), sources of vegetation indices (Landsat and Proba-V) and fluxes (GPP and NEE) for the two phases of the growing season. Direct correlation (model type 1) was found to be the best option for midday average fluxes, except for the estimation of GPP with NDVI\({}_{\text{PV}}\) during the wet phase, where the model type 2 was the best. For daily averages, however, the model type 1 performed better with NEE whereas model types 2 and 3 were the best for GPP.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**R\({}^{2}\)**} & \multicolumn{3}{c}{**R\({}^{2}\)\({}_{\text{best}}\)**} & \multicolumn{3}{c}{**RMSE**} & \multicolumn{3}{c}{**AIC**} \\ \hline
**Model** & **Flux** & **VIs** & **Single** & **Wet** & **Dry** & **Single** & **Wet** & **Dry** & **Single** & **Wet** & **Dry** & **Single** & **Wet** & **Dry** \\ \hline \multirow{3}{*}{Flux = aVI + b} & NEE & **NDVIVp** & 0.36 & 0.59 & **0.62** & 0.36 \(\pm\) 0  
During the wet phase, the best models obtained for GPP gave higher coefficients of determination (R\({}^{2}\)) compared to those obtained with NEE. The opposite was observed for the dry phase.  
Based on the best models reported in Table 5, the Table 6 gives midday and daily estimates of GPP and NEE from NDVI\({}_{\rm PV}\) data for the growing season (March to October) of the years 2014 to 2017. Average flux estimates of the four years are \(-\)711 g C m\({}^{-2}\) growing season\({}^{-1}\) and \(-\)199 g C m\({}^{-2}\) growing season\({}^{-1}\) for GPP and NEE respectively.