## 4 Discussion  
In this work, we assess the effect of different sources of uncertainty in the optically-based modeling of \(GPP_{\text{hh}}\) in a Mediterranean TGE. Different models based on optical and meteorological data have been tested to predict \(GPP_{\text{hh}}\). Despite using detailed EC footprint climatology to spatially match spectral and flux data, none of the models achieved high precision (\(RRMSE\sim 36\%\)). With these results, we try to answer the two scientific questions posed at the end of the Introduction. 1. Results show that, even in this ecosystem, relatively heterogeneous at the small scale but homogeneous at larger scale, the impact of the spatial mismatch between EC and RS footprints on the estimation of \(GPP\) is not very important. This means that when footprint climatology and RS pixels present similar characteristics, the spatial mismatch between EC footprint and optical footprint can become less relevant than other sources of variability or uncertainty. In our case, the impact of non-vegetated surfaces is low, and trees and grasses are quite homogeneously mixed at footprint and mid-low spatial resolution RS scales (Table 3, Figure 4). However, we hypothesize that the mix of trees and grasses hamper the accurate modeling of photosynthesis and \(GPP_{\text{hh}}\) in this type of ecosystem with simple light use efficiency approaches or semi empirical models. 2. Results also suggest that the impact of the temporal mismatch between RS data (i.e., flight overpass) and fluxes was low. Only slight increases of the errors were related to this mismatch. In general, \"Daily\" and \"Flight\" models showed similar performances as soon as they included \(PAR\) (MOD2-MOD4), and differences in \(RRMSE\) were sensitive to variations in meteorological variables. In our site, grasses show a strong phenological variability which might overrule trees  
Figure 8: Mean error of MOD2–MOD4 vs. the difference of different variables respect to their values each half-hour the days of the flight campaigns. The adjusted linear model and Pearson correlation coefficient of the whole dataset are shown with the respective significance degree (\(\boldsymbol{\texttt{{}^{+}}}\)\(p<0.05\); \(\boldsymbol{\texttt{{}^{+}}}\)\(p<0.10\)). In addition, data corresponding of the date of each flight are flagged. contribution to RS and EC signals. Therefore, we hypothesize that larger accuracies or continuous acquisition of RS data would be needed to determine the actual impact of changes in vegetation properties between consecutive mid-temporal resolution remote observations. We analyzed models of increasing complexity, expecting them to become more sensitive to spatial and temporal mismatches between RS and EC data. However, performance increased only slightly once models accounted for incoming _PAR_ (MOD2-MOD4). In addition, errors were related to the magnitude (Figure 7) and/or the differences between the values of meteorological and optical variables when comparing \"Flight\" and \"Daily\" datasets (Table 6, Figure 8). This questions how accurately did models manage to represent \(\varepsilon\) and/or \(f_{PAR}\). Failure to properly model these variables could be explained by the combination in this TGE of two different types of vegetation that present very different dynamics, variabilities and functions. Grass presents quite dynamic seasonal cycles dominated by _LAI_ and pigment content variations, whereas evergreen trees rather modify \(\varepsilon\) and \(f_{PAR}\) than _LAI_. Failure in the modeling of light use efficiency can be differently explained for each model. MOD1-MOD2 ignore \(\varepsilon\) and therefore poor performance was expected. However, MOD1 achieved lowest \(RRMSE\) and maximum efficiency. Nonetheless, this model splits predictions in two groups of extreme \(GPP_{\text{hh}}\) values (not shown) and is not sensitive to intermediate values of \(GPP_{\text{hh}}\). MOD3, includes \(\varepsilon\) as a linear function of different formulations of _PRI_.