## 4 Discussion  
### Potential for SIF to Monitor Changes in NPP Dynamics under Drought Events  
Drought events are an important factor causing losses in vegetation NPP. Many studies have investigated the impact of drought on NPP, showing that drought can cause severe losses in NPP [65]. However, real-time monitoring of drought-induced dynamic changes in NPP is difficult, as large-scale NPP data cannot be obtained in real time. The rapid development of chlorophyll fluorescence remote sensing has provided a new perspective for monitoring drought-induced changes in vegetation productivity. As a fluorescence signal indicating vegetation photosynthesis, SIF responds promptly to physiological changes induced by drought [66]. Our study conducted at an 8-day time scale demonstrates the significant potential of SIF for monitoring dynamic changes in NPP during drought events. During drought events in early spring, summer, autumn, and winter, the dynamic changes in SIF and NPP were not equal. In spring and winter, the dynamic changes in SIF were greater than those in NPP, whereas in summer the dynamic changes in NPP were synchronous. SIF can accurately reflect the dynamic changes in NPP induced by drought. However, during the spring-summer and summer-autumn transitions, when the physiological state of vegetation changes rapidly, the changes in SIF and NPP were not synchronous, and SIF could not accurately reflect the dynamic changes in NPP induced by drought. In addition, we explored the ability of SIF to monitor the dynamic changes in NPP induced by drought at different time scales. The results showed that as the duration of drought increases, the ability of SIF to monitor the dynamic changes in NPP becomes stronger. The ability of SIF to monitor the dynamic changes in NPP in long-term drought events was better than that in short-term drought events, which is consistent with the results of previous studies on the response of SIF at different drought timescales [67; 68]. Moreover, SIF exhibits a good response to environmental variables influencing NPP changes. Previous studies have demonstrated the influence of meteorological factors and vegetation canopy structure on the dynamic changes in \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**1 Days of Lag** & **8** & **16** & **24** & **32** & **40** & **48** & **56** & **64** & **72** & **80** & **88** & **96** & **104** \\ \hline \hline
2 R2 & 0.42 & 0.36 & 0.34 & 0.48 & 0.46 & 0.52 & 0.54 & 0.53 & 0.56 & 0.55 & 0.64 & 0.72 & 0.68 \\ \hline \hline \end{tabular} \({}^{1}\) Number of days lag; 2 correlation coefficient.  
\end{table} 
Table 7: Correlation between SIF and FAPAR at different lags in the southern HHH Plain from May to December 2019. Figure 16: Results of the correlation analysis between SIF and LST lags of 16 (**a**), 32 (**b**), 48 (**c**), and 64 days (**d**) for the period May to December 2019. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**1 Days Lag** & **8** & **16** & **24** & **32** & **40** & **48** & **56** & **64** \\ \hline \hline
2 R2 & 0.58 & 0.66 & 0.72 & 0.78 & 0.75 & 0.84 & 0.82 & 0.77 \\ \hline \hline \end{tabular} \({}^{1}\) Number of days lag; 2 correlation coefficient.  
\end{table} 
Table 8: Correlation between SIF and LST at different lags in the southern HHH Plain from May to December 2019. NPP [69; 70] In our study, SIF showed a good overall response to the PAR absorption ratio and LST, with some regions showing a temporal lag. This spatiotemporal heterogeneity in response is similar to the spatiotemporal heterogeneity of NPP and climate factors [71]. However, SIF showed a poor response to PRE, which may have been due to a time lag in the response of vegetation photosynthesis to PRE [72; 73; 74]. In addition, SIF responded well to the vegetation canopy structure and greenness. Overall, SIF can accurately capture the changes in environmental variables that cause NPP changes, further verifying its ability to monitor dynamic changes in NPP induced by drought events [75].