## 2 Materials and Methods
### Data
#### 2.2.1 Remote Sensing Imagery  
For this study, Level-2A Sentinel-2 (S2) products were used. Images were accessed through the Google Earth Engine (GEE) data catalog \"COPERNICUS/S2_SR\" by means of the python client \"_ee\"_ package ([[https://gee-python-api.readthedocs.io/en/latest/ee.html](https://gee-python-api.readthedocs.io/en/latest/ee.html)]([https://gee-python-api.readthedocs.io/en/latest/ee.html](https://gee-python-api.readthedocs.io/en/latest/ee.html)), accessed on 12 December 2022). All images available in the region of interest were collected, and those correspond to the tiles T29 SQA and T29 SQB (Figure 2). Images with high cloud coverage were removed. The cloud coverage threshold was set at 30%. Specific treatment was applied afterward to remove remaining pixels with high cloud coverage and not belonging to vegetation (e.g., water bodies, snow, temporal floods, or another temporal phenomenon). The cloud mask was applied to the images using the Scene Classification Map (SCL) and the supplementary band QA60. The SCL allows for tracing or marking defective pixels whereas the QA60 band helps distinguish between opaque and cirrus clouds ([[https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/processing-levels/level-2](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/processing-levels/level-2)]([https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/processing-levels/level-2](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/processing-levels/level-2)), accessed on 12 December 2022). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Time Range** & **Description** & **Variables** & **Units** \\ \hline \multirow{8}{*}{Eddy covariance data} & \multirow{8}{*}{from 1 October 2020} & \multirow{8}{*}{Pre-processed eddy covariance} & CO\({}_{2}\) flux & \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) \\  & & & Quality flag CO\({}_{2}\) flux & - \\  & & Pre-processed eddy & Air temperature & K \\  & & covariance & Relative humidity & \% \\  & & measurements. & Vapour pressure deficit & Pa \\  & & Corrected 30-min & Friction velocity & m s\({}^{-1}\) \\  & & estimations of gas & Monin–Obukhov length & M \\  & & analyzer and & Wind speed & m s\({}^{-1}\) \\  & & anemometer data. & Maximum wind speed & m s\({}^{-1}\) \\  & & Wind direction & \({}^{\circ}\) (degrees) \\  & & Variance of the wind component & m s\({}^{-1}\) \\  & & along the v anemometer axis & & m \\ \hline \multirow{4}{*}{Meteorological data} & \multirow{4}{*}{from 1 September 1976} & Daily estimations of meteorological data & Rainfall & mm \\  & & Daily estimations of meteorological data & Maximum temperature & \({}^{\circ}\)C \\  & & from analogic & Minimum temperature & \({}^{\circ}\)C \\  & & instruments. & Mean temperature & \({}^{\circ}\)C \\  & & & Thermal oscillation & \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data collected from the ICTS-RBD monitoring system. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Station** & **Metadata** & \\ \hline \multirow{8}{*}{Duque Fuente flux tower} & Location & 36.9985 N, \(-\)6.4345 E \\  & & Canopy height & 0.7 m \\  & & Displacement height & 0.2 m \\  & & Roughness length & - \\  & & Anemometer sensor height & 3.95 m \\  & & Gas analyser sensor height & 4.03 m \\  & & Tower fetch & 375 m \\  & & Anemometer sensor type & Gill HS-50 \\  & & Gas analyser sensortype & LI-7200 Enclosed CO\({}_{2}\)/H\({}_{2}\)O analyser \\ \hline \multirow{2}{*}{Palacio Donáana meteorological station} & Location & 36.9905 N, \(-\)6.4426 E \\  & & Sensor type & Manual analogic instruments \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Metadata of the ICTS-RBD monitoring system. To remove flooded pixels, at first, we applied the water mask of the SCL on the images; however, the mask also covered and removed flooded regions with standing vegetation or biomass contributing to the GPP of the ecosystems.