## 2 Materials and Methods
### Response of SIF to the Primary Environmental Factors Affecting NPP  
Through the discussion of overall drought events and drought events of varying severity in the HHH Plain, we can preliminarily determine that SIF has a certain potential for monitoring the dynamic changes in NPP induced by drought. In real-world environments, dynamic changes in NPP are influenced by various factors. To more accurately assess the ability of SIF to monitor drought-induced NPP dynamics, we considered both meteorological factors and vegetation physiological canopy factors. We selected five factors that influence or reflect dynamic changes in NPP and herein discuss the sensitivity of SIF to these factors. This further enabled us to evaluate the potential of SIF in monitoring the dynamic changes in NPP induced by drought. We calculated the standard anomalies of various factors during the drought period at 8-day intervals as the dynamic change values of environmental factors. The obtained values then underwent analysis (Tables 3 and 4). Subsequently, we constructed long-term time series of environmental factors during the drought period and performed pixel-wise correlation analysis with the standard anomaly GOSIF time series. The results (Figure 12) showed that, across different drought events, SIF exhibited consistent response levels to the same indicator while showing variations in response to other factors. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factors** & \({}^{\mathbf{1}}\)**MIN** & \({}^{\mathbf{2}}\)**MAX** & \({}^{\mathbf{3}}\)**MEAN** & \({}^{\mathbf{4}}\)**STD** \\ \hline FAPAR & \(-2.2666\) & \(3.0376\) & \(-0.0103\) & \(0.6904\) \\ LAI & \(-1.3499\) & \(3.3313\) & \(0.1910\) & \(0.3694\) \\ LST (\({}^{\circ}\)C) & \(-1.2484\) & \(0.6149\) & \(-0.1245\) & \(0.2077\) \\ NDVI & \(-3.5988\) & \(3.3204\) & \(0.1431\) & \(0.6047\) \\ PRE (mm) & \(-0.7556\) & \(1.7718\) & \(-0.1118\) & \(0.3525\) \\ \hline \hline \end{tabular} \({}^{1}\) MIN, minimum; \({}^{2}\) MAX, maximum; \({}^{3}\) MEAN, mean; and \({}^{4}\) STD, standard deviation.  
\end{table} 
Table 4: Statistical data of time series for the standardization of abnormal values for various factors from May to December 2019. Figure 11: Correlation between GOSIF and GLASS NPP during drought events of 40 (**a**), 80 (**b**), 120 (**c**), 160 (**d**), and 200 days (**e**) from May to December 2019. Among the five indices, SIF exhibited the strongest response to LAI, with R\({}^{2}\) greater than 0.8 for both drought events in the HHH Plain; the response to NDVI was weaker than that to LAI but overall superior to the other three factors. SIF exhibited similar spatial heterogeneity patterns in response to the FAPAR and LST. During the drought event from October 2013 to September 2014, SIF showed higher responsiveness to both indices in the central and northern regions of the HHH Plain, with R\({}^{2}\) greater than 0.6 in most areas. The southern part of the HHH Plain exhibited comparatively low response levels. During the drought event from May to December 2019, SIF demonstrated higher responsiveness to FAPAR in the northern and eastern parts of the HHH Plain than in other regions. SIF showed a higher response to LST in the northern region (correlation coefficients mostly \(>\) 0.4) and lower values in the southern region. SIF showed a poor response to precipitation. In both drought events, SIF exhibited relatively high responsiveness to precipitation in only a few areas of the northern and central regions of the HHH Plain, while overall correlation coefficients in other regions remained low. Therefore, we conducted a time lag analysis between SIF and these three factors. Considering that the weaker correlations were primarily observed in the southern region of the HHH Plain in the pixel-wise analysis, we referred to the results of time lag analysis with the strongest correlation coefficients in the southern region as a reference to determine the lag periods between SIF and different parameters at the local level.