## 4 Discussion
### Reasons for the Different Performances of SIF and VIs in GPP Monitoring under Drought Conditions  
Although SIF and NDVI generally show consistent responses to drought, there are also some differences between them (Figure 5). Compared to NDVI, SIF exhibits better correlations with temperature and precipitation, which means that SIF is more sensitive to the variation in temperature and precipitation. In other words, SIF contains more environmental information than NDVI. This is because environmental factors, such as temperature and vapor pressure deficit (VPD), are closely related to light use efficiency (LUE) and ecosystem productivity, resulting in a stronger relationship between GPP and SIF than that between GPP and NDVI [58]. Studies have shown that both SIF and NDVI can effectively track changes in SM, and this high [PERSON] is due to the higher biomass turnover rate in drier grassland. Long-term or severe drought not only inhibits grassland photosynthesis but also affects stomatal conductance, photosynthetic enzyme activity, and so on[PERSON] During drought, physiological and canopy structure parameters such as chlorophyll content, leaf area index, and fPAR show significant changes. SIF exhibits a stronger response to water stress compared to NDVI, which is consistent with previous research. [PERSON] et al. (2015) found that in agricultural or predominantly grassland areas, SIF is more sensitive to drought than NDVI [59].  
This study elucidates the differences in the potential of SIF and NDVI in grassland ecosystems to capture GPP loss under drought conditions. By exploring the relationship of SIF and NDVI with GPP, we found that SIF captures GPP variations better than NDVI (Figure 7). At a reduced scale, SIF outperforms traditional VIs in monitoring grassland productivity in response to drought, primarily because SIF also contains information about VPD or soil moisture effectiveness [60]. Due to its close association with vegetation photosynthesis, SIF is capable of revealing the impact of environmental stress on vegetation photosynthesis. As an indicator of vegetation photosynthesis, SIF provides a more intuitive and accurate reflection of vegetation growth status compared to reflectance-based traditional VIs, while NDVI primarily indicates vegetation coverage and chlorophyll content, without a direct relationship to vegetation photosynthesis [61]. Since SIF incorporates environmental information, it can help monitor variations in photosynthesis under extreme climatic conditions. We attribute the superior capability of SIF in monitoring ecosystem productivity compared to NDVI to its stronger sensitivity to environmental factors. Future research should reconsider the limitations of GPP estimation based solely on VIs.  
Furthermore, downscaled SIF with finer spatial resolution than GOME-2 SIF can reveal detailed variations in photosynthesis. The low resolutions of GOME-2 SIF limit the accuracy of estimating vegetation productivity under drought conditions. In contrast, downscaled SIF products have a better spatial resolution (0.05\({}^{\circ}\)), allowing for better differentiation between grassland and other land use types. Downscaled SIF also has a shorter temporal resolution (8 days), capturing the changes in vegetation due to drought better than other SIF data [32]. Our study area is mainly grassland in the Xilingol League, which is relatively homogeneous and meets the research requirements. Exciting, FLEX (300 m) SIF will play an even more important role in monitoring vegetation photosynthesis under the backdrop of climate change.