## 5 Conclusions  
In this study, we evaluated the performance of the three remotely sensed products MYD17A2H, GOSIF, and OCO-2 SIF when estimating GPP. We modeled and compared remotely sensed product-derived GPP estimates with GPP\({}_{\text{EC}}\) data from 23 flux towers and eight vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands). The GOSIF dataset yields the best results in the three datasets, though it leans towards overestimating GPP for deciduous broadleaf forests and grasslands. The MYD17A2H dataset leans towards underestimating GPP by 1.157 to 3.884 g\(\text{cm}^{-2}\text{day}^{-1}\) for all vegetation types, especially for croplands, grasslands, and evergreen needleleaf forests. The insufficient amounts of OCO-2 SIF make it difficult to establish good relationships between OCO-2 SIF and EC GPP for different vegetation types. In terms of the seasonal cycle, GOSIF and MODIS GPP demonstrate a good consistency with GPP\({}_{\text{EC}}\). Seasonal cycles of MYD17A2H and GOSIF show advanced trends compared with GPP\({}_{\text{EC}}\) for grassland and evergreen needleleaf forest. Though the amount of OCO-2 SIF data is small, its seasonal cycle basically follows the trend of GPP\({}_{\text{EC}}\). Observation modes of OCO-2 SIF exert an evident influence on estimating GPP. After applying a unified extraction range for the three remotely sensed products in the spatial registration process, the results still show that the ability of GOSIF data to estimate GPP is better than that of MYD17A2H and OCO-2 SIF. This also suggests better performances of SIF in estimating GPP than those of the LUE model. The MYD17A2H product has a long time series and is thus suitable for monitoring the seasonal cycle change of vegetation, as well as total amounts of GPP. The GOSIF dataset has a large amount of data with a high temporal and spatial resolution, which is useful for estimating global GPP and monitoring the vegetation growth status. The data volume of OCO-2 SIF is relatively small, but OCO-2 provides the first opportunity to estimate GPP at an ecological scale. With the development of spaceborne high-resolution spectrometer techniques, similar satellite-derived SIF products would demonstrate very promising prospects in monitoring GPP at an ecological scale.  
Conceptualization, R.Q. and G.H.; methodology, R.Q. and G.H.; software, R.Q.; validation, R.Q.; writing--original draft preparation, R.Q. and G.H.; writing--review and editing, R.Q., G.H., X.M., H.X., T.S. and M.Z.; visualization, R.Q.; funding acquisition, G.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grant No. 41971283, 41801261, 41827801), the National Key Research and Development Program of China (2017 YFC0212600), and the Postdoctoral Science Foundation of China (2017T100580).  
**Acknowledgments:** We thank the OCO-2, MODIS, and Ameriflux teams for making the SIF, MYD17A2H, and GPP data available. We thank [PERSON] and [PERSON] for providing GOSIF data. We also thank anonymous reviewers and editors for their kindly comments and suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.