## 2 Materials and Methods
### Evaluation Procedure  
The EC towers installed in both ecosystems were used to evaluate the model. High-frequency data (10 Hz in Table 1) were processed using the express mode of the EddyPro software (version 7.0.9 LI-COR, Lincoln, NE, USA) to generate 30-min averaged measurements of net ecosystem exchange (NEE), sensible heat (H), and latent heat (LE). The processing included several steps: correction for anemometer tilt according to [54]; des spiking, which involved identifying and removing short-term outliers in the time series, often caused by electronic spikes during precipitation; identifying periods of low signal variation relative to the instrumental resolution, common in weak winds and stable conditions; and detecting relatively short periods where the time series remained constant, indicating potential problems [55]. To calculate the GPP, the measured NEE had to be partitioned. A diurnal flux partitioning algorithm [56] was used in this study through the R language package REddyproc [57]. Friction velocity (u*) filtering during periods of low turbulent mixing was also used: the minimum value was estimated according to [PERSON] et al. (2006) [58] and applied to detect NEE saturation with increasing u* [59]. Finally, in REddyProc, a gap filling of the eddy covariance and meteorological data is performed with methods similar to [PERSON] et al. (2005) [60], taking into account the covariation of the fluxes with meteorological variables and the temporal autocorrelation of the fluxes, as described by [PERSON] et al. (2005) [61]. Finally, the EC footprint was estimated according to [PERSON] et al. (2000) [62], with the crosswind extension of [PERSON] et al. (2006) [63], to evaluate the 2D contribution of each point in the ground to the turbulent fluxes recorded by the EC system. The footprint calculation was performed using a Python package ([[https://github.com/hectornieto/footprint-analysis](https://github.com/hectornieto/footprint-analysis)]([https://github.com/hectornieto/footprint-analysis](https://github.com/hectornieto/footprint-analysis)) (accessed on 24 May 2024)).  
In order to compare the GPP data of the EC towers with the images resulting from the LUE models, the daily GPP and footprint data were obtained from the 30-min data. The daily footprint was resampled to the resolution of the images (10 m).  
The 10 m resolution daily footprints were applied to the LUE model images, and then the EC and model data were compared using the coefficient of determination (R\({}^{2}\)) of the linear regressions to indicate the precision of the estimates relative to the observations, the root mean squared error (RMSE) and the normalized root mean squared error (NRMSE) to measure error differences, and the mean bias error (MBE) to indicate cumulative offsets between measured and observed values. These metrics were applied at both daily and monthly scales.  
Finally, to obtain a spatial visualization of the results, the LUE model that yielded the best result was spatially applied, with a spatial resolution of 10 m for both ecosystems for each hydrological year (which ranges from October 1 to September 3 of the following year in the study area [64]). The terrestrial vegetation map of Donana [65] was used to delineate these ecosystems.