## 4 Discussion
### Advantages of Sentinel-2 High-Spatial-Resolution Data for GPP Modeling  
The Sentinel-2 network provided data with a higher revisit period (less than five days) than the Landsat data (16 days), and it also contained a sensor with 20-m spatial resolution (resampled to 30-m spatial resolution in this study). The high-spatial-resolution data provided more detailed information on the physiological variance in vegetation. Figure 7 shows that the Clr value was much higher in forestland than in grassland, which indicated that the canopy chlorophyll content in the forest was higher than that in grassland. The maximal Clr value was found to the north of the tower at the YNC site, which is a tree-covered area with a Clr value that was 1.6 times higher than that in the other grassland area (Figure 7e). Additionally, Clr can precisely describe the physiological conditions and seasonal changes in vegetation; thus, it is suitable for high-spatial-resolution GPP mapping (Figures 8 and 9). The reason for the high CV at grass sites was the trees in the \(3\times 3\)-km range near the central towers of RIG and YNC. On the other hand, the \(\text{GPP}_{\text{EVI}}\) and \(\text{GPP}_{\text{Clr}}\) at CUM, which is spatially heterogeneous, corresponded better to \(\text{GPP}_{\text{EC}}\) than to the MODI7A2H product. Furthermore, the high spatial resolution of \(\text{GPP}_{\text{Clr}}\) also indicated the high spatial variance in seasonal changes in GPP at the CUM site, because grass and forest vegetation have different growing speeds and different GPP conditions. This result indicated that the high-spatial-resolution Sentinel-2 data were suitable for characterizing the intra-annual spatial changes in GPP variance. Conversely, at the sites where there was more homogeneous land-cover type, such as the WOM and YNC sites, the relationships between \(\text{GPP}_{\text{MOD}}\) and \(\text{GPP}_{\text{EC}}\), \(\text{GPP}_{\text{Clr}}\), and \(\text{GPP}_{\text{EC}}\) were similar. With a single type of vegetation near the flux tower, the vegetation growing had a similar trend under the same environment and nutrient conditions; thus, the spatial resolution of the VI input did not affect the GPP estimation accuracy. The networking systems of Sentinel-2A and B have a higher revisit period than the 16-day Landsat data, which enhanced the tracking ability of mid-term or long-term GPP variance. At the YNC grassland site, which is a dry area with few cloudy days, Sentinel-2 provided data with an average revisit period of three days. After the launch of Sentinel-2B, the YNC site had almost no missing data at the 16-day interval (Figures 2 and 3). Because there was greater availability of cloud-free data at this site, the spatio-temporally continuous VIs also exhibited good performance at the eight-day standard interval. Under these circumstances, the correlation between VI \(\times\) PAR and \(\text{GPP}_{\text{EC}}\) was much higher than that modeled at the 16-day standard interval. At the YNC site, the \(R^{2}\) between Clr and GPP increased from 0.67 to 0.89, whereas the rRMSE decreased by 11% (Table 4). This result suggested that, with the greater number of effective images obtained by Sentinel-2, \(\text{GPP}_{\text{VI}}\) was more representative of \(\text{GPP}_{\text{EC}}\) because VIs can capture the mid-term changes in the vegetation pigment pool. The launch of Sentinel-2B increased the revisit period of the Sentinel-2 network, providing more effective data during spring and summer for the EBF sites. There were at least two effective images for all seasons except autumn at the EBF sites. However, although Sentinel-2B shortened the revisit period, the data were still less effective at the 16-day standard interval during the autumn. There was only one effective image at the WOM site and two at the TUM site during the autumn of 2018.