## 5 Conclusions
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/16020297/s1](https://www.mdpi.com/article/10.3390/16020297/s1)]([https://www.mdpi.com/article/10.3390/16020297/s1](https://www.mdpi.com/article/10.3390/16020297/s1)). Conceptualization, Y.L. and W.H. ; Validation, J.L. ; Formal analysis, Y.L. ; Investigation, W.H. ; Resources, W.H. ; Writing--original draft, Y.L. ; Writing--review & editing, Y.L., J.L. and H.C.; Supervision, J.L. and W.H. ; Funding acquisition, J.L. and W.H. All authors have read and agreed to the published version of the manuscript. This research is funded by the National Natural Science Foundation of China (Grant No. 42401439) and the Open Funding Project of the State Key Laboratory of Remote Sensing Science (Grant No. OFSLRRSS202012). The datasets used in this paper are available from open resources. Eddy covariance data for the FLUXNET sites utilized in this study are available from the FLUXNET2015 dataset. Eddy covariance data for the remaining sites were obtained from the ICOS website. The LAI data are from the Global Land Surface Satellite. The landcover data were derived from the European Space Agency Climate Change Initiative ([[https://cds.climate.opernicus.eu/](https://cds.climate.opernicus.eu/)]([https://cds.climate.opernicus.eu/](https://cds.climate.opernicus.eu/)), accessed on 8 December 2024). The reflectance data are available in MODIS Surface Reflectance products (MOD9A1). The field capacity and wilting point are derived from a set of 1 km resolution global soil parameter data. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Scheme 1** & **Scheme 2** & **Scheme 3** \\ \hline \multirow{4}{*}{Strengths} & Best performance for grass-type ecosystems. Simple and effective for capturing surface soil moisture. & Best overall performance for both grasslands and forests. Performs well during non-drought and drought periods. & Balanced improvement across ecosystems. \\  & Less effective for forests & Best overall performance & Balanced improvement across ecosystems. \\  & (minimal improvement at forest sites). Limited in representing leaf moisture. & (e.g., soil moisture and LSWI data). Slightly lower performance for grasslands at some sites. & Effective in grasslands under certain conditions. \\ \hline \multirow{4}{*}{Weakness} & Less effective for forests & High data requirements & Inferior to Scheme 1 and Scheme 2 at most sites. \\  & (minimal improvement at forest sites). Limited in representing leaf moisture. & (e.g., soil moisture and LSWI data). Slightly lower performance for grasslands at some sites. & Less significant improvements during drought periods. \\ \hline \multirow{4}{*}{Opportunities} & Can be widely applied in drought-prone grasslands. & Promising for ecosystem-level GPP modeling in diverse regions. Can be further improved with advanced data fusion. & Useful for regions with mixed water stress impacts. \\  & Less evidence  
**Acknowledgments:** We sincerely acknowledge [PERSON] and [PERSON] from Beijing Normal University for their support in conducting our research and for their valuable comments on our manuscript. We also acknowledge [PERSON] from Nanjing University for her support in sharing TL-LUE model codes. In addition, we acknowledge [PERSON] from Hohai University and [PERSON] from China University of Geosciences, Beijing for their support in conducting our research and comments on our manuscript. We sincerely acknowledge the FLUXNET team and data collectors for sharing these valuable in situ flux data. **Conflicts of Interest:** The authors declare no conflict of interest.