## 5 Conclusions  
In this study, we used continuous tower-based SIF and EC flux observations to investigate the response characteristics of the relationship between GPP and SIF to drought stress in a Chinese cork oak plantation. We compared the differences in the responses of SIF and GPP to drought stress, analyzed the GPP-SIF relationship under different drought stress levels, and evaluated the roles of both physiological and non-physiological components in and the influence of drought synergistic factors on the GPP-SIF relationship under drought stress. The results showed that SIF was less sensitive to drought stress than GPP, and the non-linear relationship between SIF and GPP decreased as the drought stress level increased. At the physiological level, the higher the drought stress level, the stronger the coupling between LUE and \(\Phi\)F. The red SIF captures GPP more accurately than the far-red SIF under drought stress. The contribution of the canopy structure to SIF and GPP was small. The contribution of radiation to SIF and GPP decreased as the level of drought stress increased, while the contribution of the physiological components increased. The close physiological connection between GPP and SIF is the basis for the use of SIF to capture the dynamics of GPP under drought. VPD was the key contributing factor to the decrease in GPP-SIF coupling among the drought synergistic factors, and considering the effectsof VPD can improve the estimation accuracy of GPP from SIF under drought stress. Our results provide a new idea with which to improve the accuracy in estimating GPP from SIF under environmental stress. This study focused on a single Chinese cork oak plantation, so the results for other species or forest ecosystems may differ. Nevertheless, our findings enhance the understanding of the GPP-SIF relationship under drought and will contribute to improving GPP models in the context of climate change.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs17122017/s1](https://www.mdpi.com/article/10.3390/rs17122017/s1)]([https://www.mdpi.com/article/10.3390/rs17122017/s1](https://www.mdpi.com/article/10.3390/rs17122017/s1)), Figure S1: Seasonal dynamics of PWSI in the Chinese cork oak plantation; Figure S2: Frequency diagram of PWSI; Figure S3: Heatmap of the correlations between environmental factors and canopy structural factors; Figure S4: Heatmap of the correlations between environmental factors and K and R\({}^{2}\) of GPP-SIF; Table S1: The changes in SIF\({}_{760}\), SIF\({}_{687}\), GPP, \(\Phi\)F\({}_{760}\), \(\Phi\)F\({}_{687}\), and LUE with each increase in the level of PWSI\({}_{\text{bin}}\); Table S2: The changes in the relationship of GPP-SIF and LUE-\(\Phi\)F with each increase in the level of PWSI\({}_{\text{bin}}\); Table S3. GPP estimation models based on SIF incorporating VPD and differences in the coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE) across water stress gradients; Table S4. Differences in the coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE) for SIF-GPP models based on different VPD incorporation methods.  
Conceptualization, Q.P.; methodology, C.H. and X.W.; software, X.C. and M.H.; validation, Q.P. and X.C.; writing--original draft preparation, Q.P.; writing--review and editing, S.S. and J.Z.; visualization, Q.P. and C.H.; supervision, S.S. and J.Z.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CAFYBB2022 ZA00102.  
Data will be made available on request.  
We thank [PERSON] and [PERSON] from the Chinese Academy of Forestry for contributing to the field measurements and data analysis.  
The authors declare no conflicts of interest.