## 4 Discussion
It is possible that our method of partitioning vegetation into \"green\" and \"brown\" failed to capture the full dynamics of leaf pigmentation over the season, leading to an apparent change in LUE. Variation in canopy structure (e.g., leaf rolling or leaf inclination angle over the season) could also contribute to this hysteresis. These possibilities warrant further investigation and should be considered in future efforts to model fluxes from optical sensors, particularly if we are to apply the LUE model to all phases of the season. In a similar study, [PERSON] _et al._[78], documented that \(\varepsilon\) could decline in the senescence phase due to low soil moisture and high vapour pressure difference (VPD). Such stress in the late season can explain why a unique equation might not be accurate for modeling seasonal CO2 fluxes. Other studies [80; 81] reported that grassland plants quickly respond to changes in moisture conditions and that seasonal patterns of \(\varepsilon\) are regulated by water availability [82]. Thus, \(\varepsilon\) could decrease from the green-up phase to the maturity phase owing to increasing water stress (and decreasing temperatures) [79]. This finding revealed that environmental stresses that are not related to chlorophyll content or change in structure, cannot always be fully detected using NDVI alone [64]. We conclude that the hypothesis of a constant \(\varepsilon\) for our ecosystem seems violated over the entire season (May-October), but holds for half season periods. Obtaining an adequate optical sampling within the eddy covariance footprint can be a challenging issue [35; 45; 83]. Due to the high variability of the eddy covariance footprint, which varies with wind speed and direction [84], it is not always feasible to directly couple optical and flux footprints, so approximations (e.g., Figure 2) are often necessary. Preliminary results of the flux footprint analysis for our site suggest a typical flux footprint area slightly larger than 1 ha [41]. In our study, the 1 ha grid NDVI\({}_{680,800}\) measurements were intended to approximate the footprint of the eddy covariance tower in size, but not necessarily in shape due to the temporal dynamics of the flux footprint. Because NDVI\({}_{680,800}\) involved intensive manual sampling, it was limited to periodic measurement and could not provide a sufficiently dense time series to allow close comparison with the continuous flux measurements. Similarly, the spatial coverage of the MODIS NDVI (pixel size nominally 250 m \(\times\) 250 m) provided a reasonable sample of the eddy covariance footprint but could not capture its full temporal dynamics. MODIS NDVI provided a sufficiently frequent time series that (once interpolated to daily data) allowed comparison with seasonal flux measurements. Of the three NDVI methods, the proxy NDVI from the automated phenology station provided the most detailed time series, matching the sampling frequency to that of the flux measurements. However, the proxy NDVI footprint was limited to a relatively small area (estimated at a few tens of meters squared), which may have reduced its effectiveness in modeling the CO\({}_{2}\) uptake form the much larger and more dynamic flux footprint. These differences in \"optical footprint\" among NDVI methods undoubtedly influenced our results, presumably contributing to the slight contrasts in seasonal patterns (Figure 5) and the different strengths of correlation between NDVIs and biomass (Figure 6) or net CO\({}_{2}\) flux (e.g., Figures 7 and 8). However, a separate analysis of flux footprint assumptions revealed that the errors due to sampling footprint size and shape were much smaller than differences due to site (E3 _vs._ E5) or season due to the uniform landscape of this site [41]. For other sites with less uniform vegetation stands, the selection of NDVI sampling method and footprint can presumably introduce significant error into the LUE model.