## 5 Conclusions  
This study examined the potential for using SIF to monitor dynamic changes in NPP induced by drought, focusing on two long-term drought events in the HHH Plain from 2010 to 2020. Advanced GOSIF and GLASS NPP data were used to construct a SIF/NPP ratio index, quantifying the relationship between SIF and NPP. This relationship was examined across different time periods, severity levels, and temporal scales. We also examined the sensitivity of SIF to five environmental factors that influence dynamic changes in NPP. The analysis yielded the following conclusions.  
Firstly, SIF can serve as an indicator for monitoring dynamic changes in NPP induced by drought. In long-term time series analysis during drought periods, SIF exhibits a high spatiotemporal correlation with NPP (R\({}^{2}>0.8\), \(p<0.01\) between GOSIF and GLASS NPP). Across different drought events and varying levels of drought severity, SIF accurately captures the dynamic changes in NPP induced by drought.  
Secondly, the SIF/NPP ratio index can reflect seasonal variations and accurately quantify dynamic changes in SIF and NPP during drought periods. Across different drought events and levels of severity, the SIF/NPP ratio remains relatively stable during the mid to late stages of summer, autumn, and winter drought events (with SIF/NPP close to the mean ratio of 1.5189). However, it shows significant fluctuations during the early stages of spring and early summer droughts, and during the transitions between spring-summer and summer-autumn (with larger deviations from the mean ratio of 1.5189).  
Thirdly, SIF exhibits higher sensitivity to vegetation greenness indices affecting NPP changes (with R\({}^{2}>0.8\), \(p<0.01\) for SIF and NDVI, and R\({}^{2}>0.8\), \(p<0.01\) for SIF and LAI). SIF is also sensitive to the ratio of absorbed photosynthetically active radiation and surface temperature (with R\({}^{2}>0.5\), \(p<0.01\) for SIF and FAPAR, and R\({}^{2}>0.6\), \(p<0.01\) for SIF and LST), showing localized temporal lag in the southern part of the HHH Plain. However, SIF is less sensitive to precipitation.  
In conclusion, SIF demonstrates promising potential for monitoring dynamic changes in NPP during drought events. In future studies, we aim to further investigate the relationship between SIF and NPP by incorporating ground flux observations and other data sources, explore the impact of different canopy conditions, topography, and spatial heterogeneity on SIF/NPP relationship, and integrate machine learning and other advanced methods to enhance understanding of the relationship between SIF and NPP.  
Conceptualization, [PERSON] and J.L.; methodology, [PERSON] and Y.W.; formal analysis, Y.W., J.H., T.S., Y.T. and Y.G.; writing--original draft preparation, J.L. and Y.W.; writing--review and editing, [PERSON], Y.W. and J.H.; visualization, Y.W., T.S. and J.H.; project administration, J.L.; and funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by The Natural Science Foundation of Anhui Province (No. 2108085 MD129) and the Anhui New Era Education Quality Engineering Project (Graduate Education) (No. 2022 zywjxalk039). The paper was also supported by Major Scientific Research Projects in Higher Education Institutions in Anhui Province (Grant No. 2022 AH040338) and the National Natural Science Foundation of China (No. 41571400).  
The data used to support the findings of this study are available from the corresponding author upon request via email.  
The authors declare that they have no conflict of interest.