## 3 Results
### Model Validation
#### 3.2.3 GPP Maps with Composite Images  
The following figures display the monthly maps of GPP derived with composite images per month (Figure 12). The model well represented the interannual variability and trend of GPP over the study area. GPP is low during the start of the fall and winter months (October-January). At the end of the wet season, when the water availability in the ecosystem is at its maximum [58], the GPP content increases and reaches its maximum during the springtime (April-May). More spatial and temporal details of the variation of GPP can be seen in Figure 11 where a comparison between the Sentinel-2 and MOD17-derived products obtained using a monthly composite image is presented. Figure 10: Vegetation classification maps showing the seven classed identified by k-means algorithm implemented in Google Earth Engine API and ee. python package using a Sentinel-2 L2A mean composite image from the 1 October 2020 to the 31 May 2021. Class 1 and 2 correspond to the dense vegetated area, mainly dominated by the _Cyperus rotundus_, and _Sarccornitea fruticosa_ plants, respectively. Class 3 is a vegetated area mainly dominated by the _Cyperus rotundus_. Class 4 and 5 are sand depression areas mainly characterized by salt marshes and shrubs and salt marshes, respectively. Class 6 and 7 correspond to ponds, temporarily and permanently flooded areas, respectively. The model accuracy in the selected area could not be directly assessed given the lack of in situ measurements outside the climatological footprint. Attempting to quantify the goodness of the model, we compared the predicted GPP within the footprint with the monthly carbon flux measured at the Duque Fuente flux tower (Table 8). From Table 8, we observed that the model generally overestimates the ground measurements of GPP which varies within a range of \(\pm\)13 gC m\({}^{-2}\) month\({}^{-1}\) in comparison to the in situ measurements. A MAE equal to 9.97 gC m\({}^{-2}\) month\({}^{-1}\), RMSE equal to 14.64 gC m\({}^{-2}\) month\({}^{-1}\), and a Sest of 0.24 are found. To improve the estimations of the GPP, we decided to reduce the time window and produce 15-day maps. Reducing the time window allows indeed for a better discretization in time and particularly accounts for the high variation of the CLr index during the spring. Equation (11) assumes that the VI is constant during the months and with high variation of CLr, the GPP can be over or underestimated. The time series of CLr for different pixels within the studied area is shown in Figure 13. GPP maps were, therefore, retrieved for the entire period using a 15-days composite image. The resulting maps are displayed in the figures below (Figures 14 and 15). Figure 11: Vegetation classification over the Dónana park for 2009 (shapefiles provided by ICTS-RBD). The _Balboschoenus maritimus_ vegetation identified in 2009 is displayed in orange. The “[PERSON] et al., 2017” red limited area represents the _Balboschoenus maritimus_ dominated area identified by [PERSON] et al. [58] and used in their research on biomass production. The red limited area has been adopted in our study for the estimation of GPP in the marsh area surrounding the EC tower. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Month** & **GPP Flux Tower** & **GPP Composite Image** \\ \hline October & 32.40 & 45.92 \\ \hline November & 22.61 & 24.70 \\ \hline December & 23.18 & 10.84 \\ \hline January & 12.27 & 14.99 \\ \hline February & 10.74 & 19.52 \\ \hline March & 75.96 & 80.18 \\ \hline April & 186.89 & 187.32 \\ \hline May & 197.25 & 232.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Monthly carbon fluxes retrieved from the Duque Fuente flux tower and predicted for the climatological footprint using a single monthly composite image. GPP in gC m\({}^{-2}\) month\({}^{-1}\). Figure 12: Monthly GPP maps obtained using a single composite image per month. The maps show the spatial and temporal pattern of GPP (gC m\({}^{-2}\) month\({}^{-1}\)).