# GPP of a Chinese Savanna Ecosystem during Different
## 4 Discussion
### Uncertainties, Limitations, and Implications of GPP Simulation  
The simulation of GPP in savanna ecosystems is fraught with uncertainties arising from multiple factors, including model structure, input data, and the inherent variability of the ecosystems being studied [62]. One significant source of uncertainty in our study stems from the LUE models used to estimate GPP. Different representations of factors such as FPAR and environmental stresses (e.g., temperature, water availability) in LUE models can lead to varying GPP estimates. Notably, the LSWI derived from HLS data may provide more accurate representations of water stress conditions than the MODIS-based LSWI does because of the finer spatial resolution of HLS imagery [23]. Previous studies have highlighted discrepancies in the performance of different model structures across various ecosystems, which adds to the uncertainty in GPP estimates [72]. The VPM model structure has been shown to reduce simulation errors in savanna ecosystems more effectively than the MOD17A2H model [19; 71]. Our analysis revealed that the effects of input variables on GPP can differ significantly depending on the type of vegetation, meteorological conditions, and hydrological status [62]. For example, in the Yuanjiang savanna, the LUEmax is constant across the VPM and MOD17 models, which could lead to overestimations of GPP. In contrast, the VPM's LUEmax is calibrated on the basis of flux values during peak periods. However, our previous research revealed that the LUE in savannas varies throughout the growing season, increases in summer and autumn, and decreases in winter and spring [73]. This suggests that LUEmax might also change seasonally. This overestimation is particularly notable in the MOD17A2H product, where the LUEmax is derived from a lookup table on the basis of vegetation type, which fails to account for the seasonal variations in LUE observed in savanna ecosystems. These oversimplifications highlight the need for more dynamic and locally calibrated LUE parameters in future models [39]. The spatial resolution of remote sensing data plays a crucial role in the accuracy of GPP simulations. Studies have shown that the GPP derived from fine-spatial-resolution remote sensing data can reduce GPP simulation uncertainties by 20% [74]. However, the use of traditional vegetation indices (Figure 6) in LUE models may still contribute to GPP overestimations. Emerging technologies such as solar-induced chlorophyll fluorescence (SIF) offer promising alternatives by providing more direct measures of photosynthetic activity, potentially reducing the reliance on conventional vegetation indices [75]. SIF also offers a new perspective for future remote sensing studies of GPP simulations in savanna ecosystems. Some studies suggest that GPP models may overestimate productivity in low-productivity savanna ecosystems such as the Yuanjiang savanna, likely due to the unique ecological characteristics of these regions where more sterile conditions occur frequently (Figure 3) [76]. Another source of uncertainty is ground validation data. Despite adhering to rigorous flux measurement protocols [66], errors in eddy covariance measurements due to factors such as footprint heterogeneity remain [77]. Expanding the network of flux towers across similar savanna ecosystems and integrating machine learning approaches with repeated model simulations could be a potential solution for addressing these uncertainties in the future. Since the Yuanjiang Ecological Station is currently the only flux research site in China that represents a savanna ecosystem and given the limited distribution of savannas in China, research on GPP remote sensing simulations in these ecosystems is scarce. As a result, it is challenging to obtain comparative GPP remote sensing validations from other savanna regions in China. Therefore, the presented conclusion concerning the accuracy of HLS GPP simulation may not be transferable to other savanna regions in China or the Indochina Peninsula.