## 3 Results
### Model Validation
#### 3.2.3 GPP Maps with Composite Images
(**a**) October 2020, (**b**) November 2020, (**c**) December 2020, (**d**) January 2021, (**e**) February 2021, (**f**) March 2021, (**g**) April 2021, (**h**) May 2021. Figure 14: 15-days GPP maps. The maps show the spatial and temporal pattern of GPP (gC m\({}^{-2}\) 15-days\({}^{-1}\)). (**a**) 1–15 October 2020, (**b**) 16–31 October 2020, (**c**) 1–15 November 2020, (**d**) 16–30 November 2020, (**e**) 1–15 December 2020, (**f**) 16–31 December 2020, (**g**) 1–15 January 2021, (**h**) 16–31 January 2021. Figure 13: Time series of the CLr index for different pixels in the upscaling area (pointers) and for the eddy covariance footprint (black dot). The production of maps using 15-day composite images was affected by the high cloud coverage over the study area. This phenomenon can be clearly observed in the maps produced for the second half of February and April 2021. This hampered the estimation of GPP for the considered time range. The 15-day composite image, however, also allowed for better identification of the flooded period. This was the case for the month of January 2021. Comparing the first and second half of the month, we can observe that some pixels were left out in the second half of the month. Those corresponded to a phase of high flood and thus pixels were masked and GPP set to zero in the flooded regions. Table 9 shows the comparison between the 15-day GPP predictions and the ground measurements of GPP. We observed that the model performances slightly improved com  
Figure 15: 15-days GPP maps. The maps show the spatial and temporal pattern of GPP (gC m\({}^{-2}\) 15-days\({}^{-1}\)). (**a**) 1–15 February 2021, (**b**) 16–28 February 2021, (**c**) 1–15 March 2021, (**d**) 16–31 March 2021, (**e**) 1–15 April 2021, (**f**) 16–30 April 2021, (**g**) 1–15 May 2021, (**h**) 16–31 May 2021.  
pared to the monthly prediction, confirming that a smaller time window could improve the estimation of GPP. Finally, a GPP map was produced for the entire period of study (Figure 16). An average GPP of 770.20 gC m\({}^{-2}\) (8 months)\({}^{-1}\) was found. This value is comparable with reported GPP values [58]. When compared to in situ measurements at the Duque Fuente station, a MAE equal to 9.68 gC m\({}^{-2}\) month\({}^{-1}\), an RMSE equal to 13.81 gC m\({}^{-2}\) month\({}^{-1}\), and a S  
Figure 16: GPP map for the period October 2020 to May 2021 over the study area. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Month** & **GPP Flux Tower** & **GPP Composite Image** \\ \hline October 1–15 & 15.45 & 25.70 \\ \hline October 16–31 & 16.96 & 20.54 \\ \hline November 1–15 & 8.96 & 13.88 \\ \hline November 16–30 & 13.66 & 10.63 \\ \hline December 1–15 & 12.15 & 5.38 \\ \hline December 16–31 & 11.04 & 5.41 \\ \hline January 1–15 & 6.92 & 7.23 \\ \hline January 16–31 & 5.36 & 7.11 \\ \hline February 1–15 & 3.29 & * \\ \hline February 16–28 & 7.46 & * \\ \hline March 1–15 & 22.01 & 25.86 \\ \hline March 16–31 & 53.95 & 51.18 \\ \hline April 1–15 & 85.87 & 90.26 \\ \hline April 16–30 & 101.02 & * \\ \hline May 1–15 & 97.93 & 132.93 \\ \hline May 16–31 & 99.32 & 115.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Monthly carbon fluxes retrieved from the Duque Fuente flux tower and predicted for the climatological footprint using a single 15-day composite image.