## 5 Conclusions  
This study compared the relationships between SIF and NDVI among different land-cover types and in different seasons and evaluated their applicability for monitoring GPP and grain yields in China. The results can provide a reference for the selection of NDVI or SIF as vegetation-monitoring indicators in different applications. The main findings of this study can be summarized as follows. 1. The NDVI and SIF change trends showed that decreasing vegetation trends were mainly concentrated in the northern part of Xinjiang, in part of the QTP, and in a small part of northeastern China. The NDVI and SIF were more closely related in all the analyzed land-cover type regions than ENF and EBF areas. 2. The spatial distribution characteristics of the relationships between the SIF (NDVI) and the GPP were similar among different seasons, and the correlations were smaller in southern China and northwestern China than in the other regions of China. In general, the correlations between the SIF (NDVI) and the GPP were smaller in the forest vegetation regions during summer than in other seasons. Moreover, the SIF had a stronger correlation with the GPP than the NDVI did in different seasons. 3. The PCC and R\({}^{2}\) values obtained between the SIF and the per-unit grain yield were generally higher than those obtained between the NDVI and the per-unit grain yield. The percentage changes in the per-unit grain yield and SIF were similar, indicating that the SIF could capture the effect of the drought on the grain yields better than the NDVI could. Furthermore, we also need to be fully aware of the following limitations of this study, which are mainly related to three aspects. First, the temporal extent of the SIF data is still relatively short and, thus, cannot describe long-term vegetation changes. As the temporal extent of the available SIF data is extended, SIF will be more widely used. Second, desert vegetation plays an important role in combating desertification, but neither NDVI nor SIF can effectively reflect the changes in the GPP in BSV regions in the northwestern part of China. It is therefore important that future investigations analyze desert vegetation indicators. Third, the suitable vegetation-indicator selection may differ among different applications. As more high-resolution satellites are launched, more precise and reliable datasets can be used in future research to further analyze and verify our results. Nevertheless, notwithstanding the above limitations, the outcomes of this study can provide a reference for selecting suitable vegetation indicators in future vegetation studies. Conceptualization: H.S. ; data curation: Z.Z. ; formal analysis: Z.Z. ; funding acquisition: H.S. ; methodology: Z.Z. and Y.D. ; supervision: H.S., Q.F. and S.L. ; validation: Z.Z., Y.D. and Y.W. ; writing--original draft: Z.Z. ; writing--reviewing and editing: [PERSON] and S.L. All authors have read and agreed to the published version of the manuscript. This study was supported by National Natural Science Foundation of China (51909117), the Natural Science Foundation of Shenzhen (JCYJ20210324105014039), the Guangdong Provincial Key Laboratory of Soil and Ground Water Pollution Control (2017B030301012), and the State Environmental Protection Key Laboratory of Integrated Surface-Water-Groundwater Pollution Control. The GOSIF data are available at [[https://globalecology.unh.edu/data/GOSIF.html](https://globalecology.unh.edu/data/GOSIF.html)]([https://globalecology.unh.edu/data/GOSIF.html](https://globalecology.unh.edu/data/GOSIF.html)), accessed on 20 October 2021. The NDVI and GPP data are available at [[https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)]([https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)), accessed on 21 October 2021.