# GPP of a Chinese Savanna Ecosystem during Different
## 3 Results  
Temperature and Precipitation and the Relationship between the \(\text{GPP}_{\text{EC}}\) and Remote Sensing VIs at the Yuanjiang Station  
The highest average daytime temperature at the Yuanjiang station from 2015 to 2018 exceeded 35 \({}^{\circ}\)C (Figure 3a) and ranged from 5 to 36 \({}^{\circ}\)C. This average temperature range is similar to that of African and Mediterranean savannas [18; 59]. In contrast to Mediterranean savannas, where rainfall is mainly concentrated in fall and winter, rainfall in the Yuanjiang savanna is mostly concentrated in summer from July to October, with distinct wet and dry seasons (Figure 3b). The VIs extracted in the GEE from the HLS and MODIS satellite remote sensing data and the GCC values extracted from the near-surface remote sensing data are plotted as time series. In general, all the remote sensing-based VIs exhibited distinct seasonal variations and were consistent with the temporal variability in the GCC values (Figures 4 and 5). The annual EVI amplitudes for the HLS data ranged from 0.1 to 0.6, whereas those for the MODIS data ranged from 0.2 to 0.6. Both the HLS and MODIS data exhibited large annual EVI amplitudes that exceeded the minimum requirement for reliable phenology detection, with a savanna EVI amplitude of 0.02 [28]. Visually, the HLS data showed a more consistent phenological signal from year to year; i.e., the HLS data were closer to the GCC in terms of temporal resolution and amplitude, so a more complete curve of phenological change could be obtained (Figure 4). However, the MODIS-based EVI and LSWI, which are linked to VPM simulations, were not synchronized in terms of curve change. For example, during the 3-year period from 2016 to 2018, the peaks of the MODIS-based EVI and LSWIlagged behind those of the GCC, whereas the peaks were ahead of those of the GCC in 2015 (Figure 5). In contrast, for the HLS data, the EVI and LSWI were better synchronized in terms of curve change and were also more centralized toward the GCC data. Moreover, the HLS-based EVI and LSWI peaks were more synchronized than those of the GCC (Figure 4). This is one of the reasons why HLS data have been used as a seamless fusion of remote sensing data and phenocam photos in other phenology studies [60]. The time series of all the GCC and VI data provide the basis for all the following comparisons and analyses. To further evaluate the biological significance and performance of VIs in GPP simulation, simple linear regression analyses between the GPPE  
Figure 4: Time series of the daily GCC derived from phenocam images and HLS-based VIs (NDVI, LSWI, and EVI) at the Yuanjiang savanna site from September 2015 to December 2018. Because the Sentinel-2 satellite was launched in June 2015, the HLS time series data began in September 2015 after quality control. Figure 3: Variations in temperature (**a**) and precipitation (**b**) at the Yuanjiang station from 2015 to 2018.  
between the GPP\({}_{\text{EC}}\) and MODIS-based VIs were performed (Figure 6). For both sets of satellite data, linear regression analysis revealed that the GPP\({}_{\text{EC}}\) exhibited better linear relationships with both the LSWI and EVI than with the NDVI. The relationships between the HLS-based VIs (NDVI, LSWI, and EVI) and the GPP\({}_{\text{EC}}\) (Figure 6a-c, R\({}^{2}\) = 0.2, 0.32, and 0.32, respectively) were comparable to those between the MODIS-based VIs and the GPP\({}_{\text{EC}}\) (Figure 6d-f, R\({}^{2}\) = 0.14, 0.17, and 0.22, respectively). This finding also demonstrates that for high-resolution satellite data, the simple linear regression model approach to GPP model prediction does not provide a comparative advantage [61] and that an alternative model structure (e.g., the VPM) is needed for savanna GPP model simulations to reduce the uncertainty [62]. Figure 5: Daily GCC derived from phenocam images and MODIS-based VIs (NDVI, LSWI, and EVI) at the Yuanjiang savanna site from 2015 to 2018.