## 5 Conclusions  
In this study, in order to improve the GPP simulation under water stresses by LUE models, we constructed three different water stress improvement schemes with a fused SM function \(f(SM)\) as the core. An attempt was made to explore which scheme best optimizes the simulation of GPP by the two LUE models during drought periods on different ecosystem types. The SWOT analysis provides a clearer understanding of the strengths and weaknesses of each scheme, highlighting their suitability for different ecosystems (Table 3). The main findings are as follows:  
1. At most grass-type sites, VPM outperformed the TL-LUE model during drought periods, which is associated with the fact that LSWI responded better to drought than VPD at grass-type ecosystems. In contrast, at forest ecosystem sites, the TL-LUE model performed better, with VPD being more representative of water stress in forest ecosystems during drought periods. 2. At grass-type sites, incorporating SM information by using a sigmoid-type SM correction function \(f(SM)\) led to clear improvements for both LUE models, in particular with the scheme multiplying \(f(SM)\) with LSWI. The TL-LUE model exhibited a maximum increase in R\({}^{2}\) of 0.27 (+54%) and a maximum decrease in RMSE of 0.40 gCm\({}^{-2}\) day\({}^{-1}\) (\(-\)33%). VPM showed improvements in R\({}^{2}\) as large as 0.32 (+64%) with a decrease in RMSE at a maximum of 0.93 gCm\({}^{-2}\) day\({}^{-1}\) (\(-\)41%). 3. In comparison, for forest ecosystems, it was more challenging to improve LUE models with SM under water stresses (for TL-LUE: \(\Delta\)R\({}^{2}\) = \(-\)0.04-0.08, \(\Delta\)RMSE = +0.07\(\--\)0.37 gCm\({}^{-2}\) day\({}^{-1}\); and for VPM, \(\Delta\)R\({}^{2}\) = 0.01-0.08, \(\Delta\)RMSE = \(-\)0.01-\(\--\)0.39 gCm\({}^{-2}\) day\({}^{-1}\)), since forests are less sensitive to surface SM variation. In addition, in contrast to grass-type ecosystems, using VPD as a moisture indicator in VPM can better improve forest GPP simulations under water stresses than using LSWI. The significance of this study lies in the construction and comparison of three different water stress improvement schemes, demonstrating how incorporating SM information can improve GPP simulation under water stress across different ecosystem types. This provides valuable insights for future research, showing that different water stress schemes should be applied to different ecosystem types, particularly grasslands and forests, to improve GPP simulation accuracy and adaptability. Specifically, the improvement schemes incorporating SM information showed significant results for grassland ecosystems, offering a reference for future ecological modeling and climate change studies. While this study has achieved promising results, there is still room for improvement. Future research could explore how these improvement schemes can be extended to other ecosystem types, especially considering regional and climatic variations in model performance. Additionally, as more soil and meteorological data become available, the methods used in this study can be further refined. Given the impact of climate change on water avail ability in ecosystems, future work should focus on incorporating climate change factors into models to further improve GPP simulation accuracy and predictive capabilities. In conclusion, this study significantly improves GPP simulation during drought periods by incorporating SM information into LUE models. The findings indicate that SM plays a particularly critical role in grassland ecosystems, whereas forest ecosystems require more adaptable moisture indicators and methods. These results provide an important theoretical foundation and practical significance for future ecological modeling, climate change research, and related policy development.