## 2 Materials and Methods
### Ability of SIF to Monitor Drought-Induced Changes in NPP Dynamics  
In our previous studies, we calculated the monthly integrated standardized drought index (mISDI) for the HHH Plain. The mISDI is a drought index obtained through a cube algorithm that considers various factors, such as the drought index, LST, land cover, topography, and vegetation. Compared with traditional drought indices, the mISDI incorporates the impact of drought on vegetation and anomalies in soil moisture and the thermal environment, providing a new perspective for drought monitoring and studying the stress of drought on vegetation [59]. Based on the mISDI calculation results and run theory, the drought events during 2010-2020 were identified and quantified [60] (Table 1). Run theory is a common method used to identify drought events. In our study, events with an mISDI \(<-1\) and a duration of over one month were considered drought events, and the duration, intensity, and severity of the droughts were calculated [61; 62]. Drought events are listed in Table 2 below. \begin{table}
\begin{tabular}{c c} \hline \hline
**mISDI** & **Drought Degree** \\ \hline \(0.5\leq\) mISDI & moist \\ \(-1\leq\) mISDI \(\leq 0.5\) & normal \\ \(-2\leq\) mISDI \(\leq-1\) & mild drought \\ \(-3\leq\) mISDI \(\leq-2\) & moderate drought \\ \(-4\leq\) mISDI \(\leq-3\) & severe drought \\ \(-5\leq\) mISDI \(\leq-4\) & extreme drought \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Degree of drought according to the mISDI values. After identifying the drought periods, we conducted a correlation analysis between GOSIF and GLASS NPP for the four drought events. The pixel-wise correlation analysis results (Figure 6) showed that during the drought events from October 2013 to September 2014 and from May 2019 to December 2019, there was a strong correlation between SIF and NPP (overall R\({}^{2}>0.8\), \(p<0.05\)). However, there was no significant correlation between SIF and NPP during the drought event of May to June 2020. For the drought event spanning October to December 2020, there were evident spatiotemporal heterogeneities in the dynamic changes in SIF and NPP. The correlation analysis of the dynamic changes between SIF and NPP during the drought events revealed that a higher correlation was observed over longer drought periods. Therefore, we selected two drought events, namely, from October 2013 to September 2014 and from May to December 2019, to investigate the capability of SIF in monitoring the dynamic changes in NPP and explore its potential in monitoring NPP dynamics during prolonged drought periods. We constructed SIF/NPP ratio time series with an 8-day temporal resolution during the drought periods and analyzed the dynamic changes in SIF/NPP ratios to examine the monitoring capacity of SIF for drought-induced NPP dynamics. During this process, a few GLASS NPP values were proximate to zero; in the division calculation, these values would cause abnormally large SIF/NPP results, leading to some interference and complexity in the analysis of the relationship between SIF and NPP. Therefore, we removed some SIF/NPP ratios when the GLASS NPP was <0.1 g \begin{table}
\begin{tabular}{c c c} \hline \hline
**Drought Start and End Time** & **Degree of Drought** & **Duration of Drought in Months** \\ \hline October 2013 to September 2014 & mild drought & 12 \\ May 2019 to December 2019 & mild drought & 8 \\ May 2020 to June 2020 & mild drought & 2 \\ October 2020 to December 2020 & mild drought & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Drought events from 2010 to 2020. Figure 6: Results of the pixel-by-pixel correlation analysis between GOSIF and GLASS NPP (**a–d**) and significant values (**e–h**) from October 2013 to September 2014, May to December 2019, May to June 2020, and October to December 2020.