## 3 Results
### Flux Maps Using the Best Models  
Figure 5 shows a map of GPP and NEE for the whole study area, computed based on the equations in Table 5. These fluxes represent estimated averages for the periods 15 May 2017 to 19 May 2017 for Landsat and 11 May 2017 to 20 May 2017 for Proba-V. Similarly, Figure 6 shows estimated average of NEE and GPP for the periods 2 July 2017 to 6 July 2017 for Landsat and 1 July 2017 to 10 July 2017 for Proba-V. The dates were chosen to show an example for both wet and dry phases.  
During the wet phase (Figure 5), GPP or NEE estimated from the two different sources of vegetation indices have similar spatial distributions in spite of their different spatial resolution. In the case of midday average fluxes, NEE and GPP seem to have the same distribution with differences only in their values. This is a consequence of the very good linear relationship that was found between midday GPP and NEE in our study. Daily fluxes, however, show more difference between NEE and GPP, as the correlation observed was not as good as that of midday fluxes.  
During the dry phase (Figure 6), the spatial distribution of fluxes is quite different between flux estimates from the two sources of VI (Landsat and Proba-V), with the exception of estimates of daily aggregates of NEE, where the estimate from NDVI\({}_{\rm PV}\) appears like a generalization of the estimate from LSWI.  
A difference can be noticed between the wet and the dry phases: GPP estimates are very low during the dry phase, and the carbon balance (NEE estimates) became positive in some parts of the study area.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Flux Estimates (g C m\({}^{-2}\) Growing Season\({}^{-1}\))**} \\ \hline
**Year** & **GPP** & **GPP\_24** & **NEE** & **NEE\_24** \\ \hline
2014 & \(-\)394.13 & \(-\)709.63 & \(-\)351.76 & \(-\)259.57 \\
2015 & \(-\)391.71 & \(-\)727.15 & \(-\)331.97 & \(-\)212.51 \\
2016 & \(-\)368.15 & \(-\)702.08 & \(-\)312.16 & \(-\)161.44 \\
2017 & \(-\)379.05 & \(-\)703.35 & \(-\)309.71 & \(-\)161.99 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Yearly estimates of GPP and NEE per growing season per year. The mention “_24” represents whole day estimates over the growing season while no mention means midday (11 a.m. to 4 p.m.) estimates.  
\begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Aggregate** & **VI Source** & **Flux** & **Wet Phase** & **Dry Phase** \\ \hline Midday & Landsat & GPP & \(-\)26.34*NDVI + 4.17, R\({}^{2}\) = 0.85 & \(-\)64.9*NDWI \(-\) 30.7, R\({}^{2}\) = 0.63 \\  & Proba-V & & \(-\)0.03*(NDVI\({}_{\rm PV}\)*R\({}_{\rm B}\)) \(-\) 0.71, R\({}^{2}\) = 0.73 & \(-\)19.17*NDVI\({}_{\rm PV}\) + 7.17, R\({}^{2}\) = 0.46 \\  & Landsat & NEE & \(-\)26.03*NDVI + 4.76, R\({}^{2}\) = 0.80 & \(-\)73.81*NDWI \(-\) 33.52, R\({}^{2}\) = 0.69 \\  & Proba-V & & \(-\)19.35*NDVI\({}_{\rm PV}\) + 3.13, R\({}^{2}\) = 0.59 & \(-\)18.49*NDVI\({}_{\rm PV}\) + 7.75, R\({}^{2}\) = 0.62 \\  & Landsat & GPP & \((-\)0.044*NDVI + 0.004*Rg, R\({}^{2}\) = 0.91 & \(-\)29.05*NDWI \(-\) 13.93, R\({}^{2}\) = 0.69 \\  & Proba-V & & \((-\)0.033*NDVI\({}_{\rm PV}\))*Rg, R\({}^{2}\) = 0.80 & \((-\)0.028*NDVI\({}_{\rm PV}\) + 0.01*Rg, R\({}^{2}\) = 0.60 \\  & Landsat & NEE & \(-\)10.72*LSWI \(-\) 0.57, R\({}^{2}\) = 0.58 & \(-\)12.34*LSWI + 0.3, R\({}^{2}\) = 0.86 \\  & Proba-V & & \(-\)6.81*NDVI\({}_{\rm PV}\) + 2.61, R\({}^{2}\) = 0.36 & \(-\)7.97*NDVI\({}_{\rm PV}\) + 4.88, R\({}^{2}\) = 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Best models selection based on accuracy metrics.