## 2 Materials and Methods
### LUE Model
#### 2.2.1 FAPAR Estimation  
A time series of 257 FAPAR scenes was selected from the high-resolution vegetation phenology and productivity product of the Copernicus Land Monitoring Service (CMLS) ([[https://land.copernicus.eu/en/products/vegetation](https://land.copernicus.eu/en/products/vegetation)]([https://land.copernicus.eu/en/products/vegetation](https://land.copernicus.eu/en/products/vegetation)) (accessed on 13 January 2023)) with a spatial resolution of 10 m and a temporal resolution of 5 days. This product uses a 6S radiative transfer model in the canopy methodology implemented in the SNAP Toolbox (SNAP--ESA Sentinel Application Platform v2.0.2, [[http://step.esa.int](http://step.esa.int)]([http://step.esa.int](http://step.esa.int)) (accessed on 12 January 2023)) to estimate FAPAR at 20 m spatial resolution. The methodology of [40] is then applied to obtain a final product at 10 m spatial resolution [41]. This methodology  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ecosystem** & **Sensor** & **Variable** & **Frequency** \\ \hline \multirow{6}{*}{Xeric shrubland} & CNR4 Kipp \& Zonnen 1  & Incoming shortwave radiation & 5 min \\ \cline{2-4}  & LI-7500 DS 2  & Carbon and water storage & 10 Hz \\ \cline{2-4}  & WindMaster 3D 3  & Sonic temperature, three-dimensional & \\  & wind speed and direction & 10 Hz \\ \cline{2-4}  & HMP155A 4  & Air temperature and relative humidity & 5 min \\ \hline \multirow{6}{*}{Marshland} & CNR4 Kipp \& Zonnen 1  & Incoming shortwave radiation & 5 min \\ \cline{2-4}  & LI-7500 DS 2  & Carbon and water storage & 10 Hz \\ \cline{2-4}  & Gill HS-50 3  & Sonic temperature, three-dimensional & \\ \cline{2-4}  & Gill HS-50 3  & 10 Hz & \\ \cline{2-4}  & WXTS20 4  & Air temperature and relative humidity & 5 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor models installed in the eddy covariance systems, measured variables used and frequency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Variable** & **Source** & **Spatial Resolution (m)** & **Number of Images** \\ \hline \multirow{6}{*}{Sentinel-2} & \multirow{6}{*}{FAPAR} & Copernicus High Resolution & \multirow{2}{*}{10} & \multirow{2}{*}{257} \\  & FAPAR & Vegetation Phenology and & & \\ \cline{1-1}  & & Productivity & & \\ \cline{1-1}  & & Boads 8 and 12 & Google Earth Engine & 10 & 150 \\ \hline \multirow{6}{*}{ERA5-Land} & surface\_solar\_radiation\_downwards\_sum, temperature\_2m, temperature\_2m, dewpoint\_temperature\_2m & ECMWF & 11,132 & 1278 \\ \cline{1-1} \cline{2-4}  & & dewpoint\_temperature\_2m & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Remote sensing products used in this work for the whole study period (July 2019 to December 2022), sources and number of images used.  
was implemented to generate biophysical products such as leaf area, index, FAPAR and fractional vegetation cover from multiple sensors. It consists mainly in generating a comprehensive database of vegetation characteristics and associated Sentinel 2 top of canopy (TOC) reflectances. Later, neural networks are trained to estimate the canopy characteristics from the TOC reflectances along with defined corresponding angles that determine the observation configuration. For the massive download of all images for the entire study period and their subsequent clipping to the DBR area, a Python package was created ([[https://pypi.org/project/pyvpp](https://pypi.org/project/pyvpp)]([https://pypi.org/project/pyvpp](https://pypi.org/project/pyvpp)) (accessed on 19 October 2023)). Finally, the resulting images were linearly interpolated pixel by pixel to produce a daily image.