## 5 Conclusions  
In this research, the performance of SIF and vegetation indexes in monitoring and evaluating the effects of the well-known 2019 drought in the MLRYR on vegetation was compared and analyzed, aiming to provide a decision-making reference for regional drought remote sensing monitoring. The main results are presented as follows:  
1. MODIS GPP can reflect the GPP variation in the MLRYR effectively. The GPP, GOSIF, NDVI, EVI, and NIRv all exhibited significant increasing trends during 2000-2020. When compared to VIs, the spatial distribution characteristics of GOSIF and GPP were most similar, and GOSIF was most correlated with GPP in both annual (linear correlation, R\({}^{2}\) = 0.87) and monthly (polynomial correlation, R\({}^{2}\) = 0.976) time scales.
2. From July to December, the PPT, SM\({}_{\text{surf}}\), SM\({}_{\text{root}}\), and SPEI in 2019 were generally below the averages during 2011-2020 and reached their lowest point in November, while those of Tem, LST, and PAR, on the contrary, were higher. Similar results could also be verified from the standardized anomalies of the above variables on the spatial.
3. The differences between the monthly averages of 2019 and 2011-2020 for SIF and VIs are not significant on a temporal scale. Spatial distributions of standardized anomalies of SIF and VIs were consistent during August-October 2019. In November and December, however, the regional difference in SIF anomaly was small, and that of VIs still changed significantly.
4. When vegetation was entering the senescence stage in November and December, the VIs had an obvious delayed response in monitoring vegetation's physiological state compared with SIF, while the VIs could better indicate meteorological drought conditions compared with SIF.
5. The distribution characteristic of the GPP standardized anomaly during the 2019 drought was more similar to that of GOSIF, especially obvious in November and  
Figure 15: Monthly variations of MODIS GPP, GOSIF, NDVI, EVI, and NIRv in 2019.  
December, which exhibited the superior ability of SIF in capturing and quantifying drought-induced GPP losses.  
This study revealed that SIF has good performance in drought monitoring at the normal vegetation growth stage and can evaluate drought-induced GPP losses. Subsequent studies should focus on how to bring the above potential into play and then solve the scientific problem of how to apply SIF to monitor and evaluate the intensity of drought events systematically, e.g., the construction of a drought stress fluorescence monitoring index based on SIF.  
Conceptualization, M.L. and R.C.; Software, X.S., P.X., F.N. and C.W.; Validation, M.L., R.C. and X.S.; Formal Analysis, M.L. and R.C.; Data Curation, M.L., R.C., X.S., P.X., F.N. and C.W.; Writing--Original Draft Preparation, M.L. and R.C.; Supervision, M.L. and R.C.; Funding Acquisition, M.L., R.C. and Y.J.; Writing--Review and Editing, Y.J., S.S. and A.R.M.T.I. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Anhui Provincial Natural Science Foundation (2108085 QD157; 1908085 QD171), National Natural Science Foundation of China (41905100), National Key Research and Development Program of China (2018 YFD0300905), Anhui Agricultural University Science Foundation for Young Scholars (2018 zd07), Anhui Agricultural University Introduction, and Stabilization of Talent Fund (yj2018-57).  
Not applicable.