## 4 Discussion
### Similar Performance of Sentinel-2 and MODIS on GPP Modelling  
Despite its finer spatial resolution, Sentinel-2 data did not enhance the performance of the empirical GPP models. This result is mainly explained by the homogeneous surrounding vegetation of the EC areas in combination with the quite large area of flux footprint; however, other factors that may have contributed to this result are, e.g., (1) the function fitting model used eliminated minor fluctuations of EVI2 time-series, which might have masked some differences among the input datasets; (2) the use of linear regression models reduced the magnitude difference between Sentinel-2 EVI2 and MODIS EVI2 data. These points are further discussed below.  
A factor that strongly reduced the differences in GPP relationships between Sentinel-2 and MODIS was the homogeneity of the EC sites. We applied the FFP model to more precisely estimate the EC measurements' footprint [35; 39]. The coverage areas of MODIS pixels and Sentinel-2 pixels that are used to represent the flux footprint are quite different at some sites, e.g., SE-Lnn. However, the footprint EVI2 in Sentinel-2 and MODIS scales did not show large differences due to the homogeneity of the surrounding areas of EC measurement.  
As previously mentioned, the satellite daily EVI2 time-series were generated based on double logistic functions, which are robust at handling data gaps and reducing data noise but lead to a loss of daily fluctuations of the EVI2. Therefore, the day-by-day fluctuations in the output GPP were all mainly generated by \(\mathrm{T_{a}}\) or PPFD. Since the same environmental datasets were applied to the GPP models for all satellite data, the differences in the GPP outputs between the satellite datasets at each site only reflect the differences in fitted EVI2. Thus, the seasonal shape and magnitude of EVI2 make up the principal differencesbetween the GPP of the different satellite datasets (Figure A1 in Appendix A). Our previous experiences show that Sentinel-2 MSI EVI2 time-series perform better than MODIS in capturing ground multispectral measured EVI2 time-series [65]. However, in the current study, the effects of the differences in seasonal shape and magnitude of EVI2 time-series on GPP modelling at each site were reduced by the intercepts (\(a\)) and slopes (\(b\)) in the linear regression models. The intercepts reduced the bias of estimations, while the slopes reduced the differences in the amplitudes of the estimations.  
In a recently published study, [PERSON] et al. [11] applied a very high resolution (VHR) hyperspectral sensor on a UAV platform, to study the effects of spatial resolution on the model performance in a willow field at a 30 minutes' flux basis. They found that the performance of footprint weighted simulations to represent the measured flux started to decline as resolutions exceeded 10 m, while performance was high and relatively constant from 0.03 m to 10 m. They compared their results with the typical scales of the canopy structure and concluded that the spatial resolution should be in the same order of magnitude as the typical crown sizes and distances between the plant rows. While this high requirement might be typical for a homogeneous study site as the willow plantation, the rule of thumb might be general [11]. Yet the spatial resolutions of satellite-based remote sensing techniques are currently much coarser.