# GPP of a Chinese Savanna Ecosystem during Different
## 4 Discussion
### Advantages of the VPM over Conventional Remote Sensing Products in Savanna GPP Studies  
The HLS VPM GPP estimates were more consistent with the ground flux observation-based GPP values than were the MODIS VPM GPP estimates and MOD17 GPP product. MOD17A2, as a relatively mature and stable conventional GPP product, still plays an important role in GPP studies involving large- or global-scale data, and the accuracy of GPP estimation in savannas was verified in this study. Moreover, we considered that this might be related to the underestimation of MOD17A2 offset by the low annual GPP value of the Yuanjiang savanna. This occurred because MOD17A2 has been considered to underestimate the GPP in other savanna types [18; 33]. However, MOD17A2 is unsuitable for tracking seasonal GPP dynamics or the relationship between GPP and phenology. The performance of GPP\({}_{\text{MOD17A2}}\) during the different phenophases greatly fluctuated, and the coefficients of determination between the simulated values and the EC values were very low (Figure 13). Our findings agree with those of [68]; notably, it is difficult for MODIS satellite GPP products to reflect the seasonal dynamics of savannas. Instead, the VPM plays a major role in small- and medium-scale studies, especially in combined studies of remote sensing satellite sensors and flux stations, and increasing the accuracy of HLS data can improve such small- and medium-scale studies. Both the VPM and MOD17 products are models based on the LUE concept [11]. However, they exhibit two main differences. First, the LSWI, which reflects the vegetation canopy water content, was used as a water stress factor in the VPM. In contrast, in the MOD17 algorithm, the atmospheric moisture indicator vapor pressure deficit (VPD) is considered [69]. Under drought conditions, the VPM performs better in capturing the effect of water stress on the GPP, which can be attributed to the higher sensitivity of the LSWI (Figure 6). In contrast, the VPD cannot capture the effect of water stress on the GPP well under severe drought conditions because it does not incorporate soil water deficits into canopy gas exchange [69]. Second, the VPM considers the EVI (chlorophyll or foliar greenness), whereas the MOD17A2 algorithm considers the NDVI to estimate the FPAR. Previous studies have shown that the relationship between the EVI and GPP is better than that between the NDVI and GPP (Figure 6) in various ecosystems [33; 70]. Compared with VPD-based water stress, LSWI-based water stress better captures the effect of soil moisture on GPP. The observed GPP representation differences may also be associated with the input data of the MOD17A2H product for computing the GPP. The use of meteorological data from the Global Modeling and Assimilation Office (GMAO)/National Aeronautics and Space Administration (NASA) set by the MOD17A2H product may not adequately represent the dynamics of soil moisture in savannas [12]. We found that the HLS VPM is more closely related to the phenological changes in the savanna than the MOD17A2 product is (Figures 11 and 13), which can also indicate the potential of high-resolution satellite data such as HLS data [23] in the study of vegetation and carbon phenologies in savannas. The value of the maximum photosynthetic efficiency is important for applying the savanna LUE model. The most significant limitation of the MODIS GPP algorithm is its improper characterization of the LUE. It relies on lookup tables to determine the maximum LUE for a given vegetation type, after which this value is adjusted downward on the basis of environmental stress factors via interpolated meteorological data. In other savanna ecosystems in Africa and North America, the MOD17A2 product also exhibited poor performance [18; 33]. The LUEmax value for savannas in MOD17A2 is low and fixed. Moreover, the meteorological parameters of its algorithm cannot be flexibly adjusted in combination with the observation data of the local flux tower.