## 4 Discussion
### Potential Application of SIF in Drought Monitoring  
In this study, SIF and VIs of winter wheat were monitored over a long period of time during the growing season to investigate the differences in SIF and VI responses to different water stresses. This study will provide valuable support for the application of SIF in drought monitoring.  
Through ANOVA, we found that SIF was sensitive enough to light drought, while NDVI, NDRE and OSAVI was significantly reduced only in moderate and severe drought (Figure 11). This is consistent with the findings of a previous study [27]. They compared the effects of drought on SIF and the Enhanced Vegetation Index (EVI) of Amazonian forests, and they noted that SIF in Central Amazonia significantly decreased during the dry season, while the EVI showed relatively little change. A shortcoming of traditional Vis is that they reflect changes in the chlorophyll content rather than directly monitoring the photosynthesis of vegetation. In contrast, SIF is considered to be a direct probe of photosynthesis and therefore more suitable for drought monitoring.  
Some studies have pointed out that, when the LAI exceeds 5, the NDVI will be saturated, and it will not be able to capture changes in vegetation [43]. Therefore, the NDVI is not effective for drought monitoring during periods of dense vegetation growth. The NDRE can be used to monitor the growth of crops at the mature stage, while the OSAVI is more effective in identifying the chlorophyll content of plants at the early growth stage. However, our research found that the NDRE and OSAVI also could not monitor agricultural drought in time. Since SIF and vegetation photosynthesis are closely related, SIF still has the ability to accurately monitor vegetation changes even when the LAI is high and vegetation canopy cover is high. Therefore, compared with the NDVI, NDRE and OSAVI, SIF is more suitable for drought monitoring in areas with high vegetation cover.  
By analyzing the seasonal variation patterns of SIF, the NDVI, NDRE and OSAVI, it can be found that SIF responds to drought more quickly, while the NDVI, NDRE and OSAVI only respond to drought at a longer time scale (Figure 12). This indicates that SIF can monitor the occurrence of drought quickly, while the NDVI, NDRE and OSAVI are suitable for monitoring drought over a long period of time. This is consistent with the findings of [PERSON] et al. [28]. [PERSON] et al. [28] showed that the correlation between F760/PAR and the SM was better for shorter time lags but not significant at longer time lags compared to the NDVI. The late response of the NDVI to changes in the SM may be due to the influence of changes in the chlorophyll content. Furthermore, background signals such as soil color and shading also have significant effects on the NDVI [44]. In addition, the vegetation type also affects the response of SIF, the NDVI, NDRE and OSAVI to drought. Unlike vegetation types such as crops and grasslands, SIF, the NDVI, NDRE and OSAVI of mixed forests did not show significant decreases under drought conditions [45]. In summary, the NDVI, NDRE and OSAVI cannot track the occurrence of early drought in a timely manner, while SIF can capture the characteristics of drought-induced changes in vegetation physiology in real time and is a valuable indicator for agricultural management and disaster monitoring.