# GPP of a Chinese Savanna Ecosystem during Different
## 4 Discussion
### Relationship between Phenophases and GPP Remote Sensing Modeling in Savanna Ecosystems  
In contrast to broadleaf evergreen forests at the same latitude, savannas are climatically driven ecosystems because of the presence of annual herbs and a significant proportion of deciduous species [59]. Accurate phenological stage division is essential for measuring the dynamics of savanna ecosystems, especially the exchange of carbon and water between the canopy and the atmosphere [35]. In this study, remote sensing satellite data were used to simulate the savanna GPP; however, deriving savanna phenophases via remote sensing satellite data would increase the systematic uncertainty [11]. Therefore, we used more accurate phenocam-derived GCC values to extract phenophases, while recognizing that GPP changes in ecosystems with different phenophases also represent a popular research topic [35]. Numerous studies [6; 30] have demonstrated that the GCC can be used to monitor canopy development and identify phenophases, with notable correlations with remote sensing VI time series (Figures 4 and 5). The phenocam-extracted GCC color index can clearly reflect the phenological signals of Yuanjiang savanna vegetation color change (Figure 10). However, one of the important findings of this study is that the VPM performs differently during the different phenophases at the growing and senescence stages of savanna canopy greening. For the VPM performance comparison, the differences between the different remote sensing data sources were significant in terms of the green-up period metrics but not in terms of the senescence period metrics (Figures 12 and S4). For example, during the green-down phenophases, both the HLS VPM (Figure 11e-h) and the MODIS VPM (Figure 12e-h) performed well. The HLS VPM overestimated the GPP in spring and summer more than it did in autumn and winter (Figures 11 and S4). However, the MODIS VPM significantly overestimated the GPP throughout the growing season (Figures 12 and S4). We suggest that the reason for this model performance difference is, first, the different natures of savanna phenology during the two phenophases. Notably, ref. [33] reported that the interannual variability in savanna vegetation phenology during the senescence phase was low in southern Africa, whereas the complexity of savanna cover variability and phenology was much greater during the green-up phase. This complexity of the vegetation phenology and cover change during the spring and summer periods places greater demands on the accuracy of the input data used in the VPM. Notably, the low complexity during the GPP senescence period can be effectively simulated by the HLS and MODIS VPM, whereas the high spatial resolution of the HLS VPM is superior to that of the MODIS VPM for the more variable green-up growing period [37]. Improving the spatial resolution of vegetation indices can effectively reduce the seasonal error of VPM simulations. Notably, before their use for GPP simulation purposes, other studies have shown that HLS data can be suitably integrated with phenocam-derived GCC data for extracting phenological periods, curve fitting, and delineation [60]. This also shows that HLS data are more suitable than MODIS data when combined with near-surface remote sensing cameras for the study of phenology indicators and GPP (Figure 4). One of our main concerns was the ability of the HLS and MODIS data to drive the GPP models during the different savanna phenophases but not the absolute accuracy of the prediction model. Our study is consistent with [33] in that the VPM-based GPP estimates (GPP\({}_{\text{VPM}}\)) can track the seasonal dynamics of the EC-based GPP in semiarid and semihumid savannas in southern Africa. Both the HLS and MODIS VPMs can track the seasonal dynamics of the EC-based GPP during the different phenophases in the Yuanjiang savanna (Figures 6 and S4). Moreover, the structural composition of savanna species in Yuanjiang is similar to that in Africa [59].