## 6 Conclusions  
In this study, we investigated and evaluated the performance and robustness of DLF and the HGAM in vegetation phenology extraction from NDVI/EVI/GCC time series across seven PFTs and proposed a phenology-based framework to estimate \(\varepsilon_{0}\) for improved GPP estimation in the LUE model. The results showed that using the HGAM method and EVI data was suited for phenology extraction in forest and cropland ecosystems; wetland phenology extraction preferred the integration of camera photos and the HGAM method; and grassland phenology favored EVI and the DLF method. Based on land surface phenology and light-response curves, we estimated phenology-based \(\varepsilon_{0}\) for the different ecosystems. The results showed that the ranges of \(\varepsilon_{0}\) for different ecosystems were divergent. However, we found high consistency in the seasonal dynamics of \(\varepsilon_{0}\) across these ecosystems. Based on the point, four phenology-based GPP products were estimated from phenology-based \(\varepsilon_{0}\) and four different forms of FAPAR and compared with VPM GPP. In different types of ecosystems, the improved model showed varying degrees of improvement compared to the original model. Generally, the good agreement between the GPP\({}_{\text{phe-based}}\) and GPP\({}_{\text{EC}}\) in our study implies that the maximum LUE estimated in the phenological stages reduces the uncertainty of the parameter to a certain extent and provides a substantial improvement in GPP estimations for cropland, grassland, and wetland.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15164002/s1](https://www.mdpi.com/article/10.3390/rs15164002/s1)]([https://www.mdpi.com/article/10.3390/rs15164002/s1](https://www.mdpi.com/article/10.3390/rs15164002/s1)), Figure S1.1-S1.21 presents the key phenological stages extracted by HGAM and DLF at the seven flux tower sites. Figure S2.1-S2.21 presents the phenological camera photographs corresponding to the key phenological stages at the seven flux tower sites. Figure S3 presents the linear correlation between the flux tower observation GPP and the four FAPAR proxies.  
Conceptualization, H.C., Y.L. (Yulong Lv) and D.H.; methodology, H.C., D.H. and F.L.; software, Y.L. (Yulong Lv), P.S. and J.G.; validation, Y.L. (Yulong Lv), P.S. and Y.L. ([PERSON]); formal analysis, H.C., Y.L. (Yulong Lv), D.H. and X.G.; data curation, Y.L. (Yulong Lv), P.S. and J.W.; writing--original draft preparation, Y.L. (Yulong Lv) and P.S.; writing--review and editing, H.C., F.L. and Y.H.; supervision, H.C., D.H. and F.L.; funding acquisition, C.C., F.L. and D.H. All authors have read and agreed to the published version of the manuscript.  
This study was funded by a grant from the State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, the Joint Funds of the National Natural Science Foundation of China under grant U22A20567, and the Jiangxi Provincial Natural Science Foundation under grant 20224 BAB213038.  
The flux site observations are from FLUXNET, the phenology camera data are from PhenoCam, and other data presented in this study are available on request from the corresponding author.  
The authors would like to sincerely thank the anonymous reviewers for their constructive comments and valuable suggestions to improve the quality of this article.  
The authors declare no conflict of interest.