# GPP of a Chinese Savanna Ecosystem during Different
## 4 Discussion  
### HLS Data Play a Significant Role in Improving Savanna GPP Estimation  
Coarse-resolution remote sensing data can lead to uncertainty in vegetation productivity estimates [62]. Observations with a finer spatial resolution should enhance regional GPP estimations by more effectively capturing variations in heterogeneous landscapes [64]. While the MODIS VPM and MODIS GPP products provide valuable GPP estimates that are widely available in space and time, GPP estimation in the face of complex and heterogeneous land cover types, such as river valley savannas, benefits from the use of finer-spatial-resolution imagery. Effectively combining observation data from in situ stations with remote sensing satellite data for more accurate GPP estimation is a research goal that  
Figure 12: Linear regression equations with an intercept of 0 for the MODIS VPM GPP simulations compared with the EC data during the different phenophases from 2015 to 2018 (**a–d**) for the green-up phenophase of each year and (**e–h**) for the green-down phenophase of each year.  
has been addressed in many studies [23; 65]. In our study, we validated the performance of the use of HLS data in modeling the GPP in a Chinese savanna through the harmonization of satellite data and long-term observation data from our flux station.  
We found GPP overestimation in the Yuanjiang savanna both by the VPM using HLS data and by the VPM using MODIS data. The HLS VPM results indicated lower overestimation than the MODIS VPM results did, with an overestimation of 46% from 2015 to 2018 (Table 2). In contrast, the overestimation of the MODIS VPM results reached 91%. Our MODIS VPM results greatly differed from those in Mediterranean savannas [18], and the \(\mathrm{GPP_{MODIS-VPM}}\) at the two Mediterranean-type savanna sites was very close to the \(\mathrm{GPP_{EC}}\) during the study period (slope: 0.83-1.15) [18]. This difference may be due to multiple factors influencing savanna types, VPM simulations, and EC measurements. The employed VPM differed from that used in [18]. The newer version of the VPM was established by adjusting the EVI coefficients and removing the factor (\(\mathrm{P_{scalar}}\)) accounting for the effect of leaf longevity from the earlier model [15], and the new EVI coefficients caused an increase in \(\mathrm{FPAR_{chl}}\) estimates for the same EVI values. In addition, and more importantly, the Yuanjiang savanna exhibits low annual productivity [66]. The mean multiyear EC values of the two Mediterranean savannas, namely, US-Ton and ES-LMa, were 1035.58 and 1033.44 g C m\({}^{-2}\), respectively, whereas the mean multiyear EC value at the Yuanjiang station was 596 g C m\({}^{-2}\). Moreover, our previous studies at the Yuanjiang station revealed that this savanna is highly spatially heterogeneous and that very small precipitation variations could cause changes in the structure and composition of plant communities, influence the structure and composition of tropical savannas, and affect the function and stability of the Yuanjiang savanna ecosystem [41]. With respect to VPM, high-resolution remote sensing data inputs could improve estimations for ecosystems with high spatial heterogeneity [62]. With the integration of HLS data from two high-spatial-resolution satellites, namely, Landsat (with a resolution of 30 m) and Sentinel-2 (with a resolution of 10-60 m), the VPM significantly improved the MODIS VPM results. Notably, overestimation was reduced from 91% to 46% (Table 2). The HLS VPM results also indicated a significant improvement in the GPP estimation accuracy for this savanna, which is highly spatiotaneous. Seasonal GPP estimation for savannas is likely a function of the data availability and/or quality [19]. Thus, with the release of more high-resolution data in this decade [67], high-spatiotemporal-resolution satellite observations are likely to constitute a continued research direction for GPP estimation and analysis in the future [65].