## 5 Conclusions  
Within this research, we assessed the performance of two Sentinel-3 OLCI products (FAPAR and OTCI) combined with MERRA2 meteorology reanalysis data to estimate GPP through four GPP models (the MODIS GPP algorithm, EC-LUE model, GR model, and VI model) at the site and regional scales during 2017 and 2018. The following major conclusions can be drawn from the study:  
(1) The relationship between \(\text{GPP}_{\text{MODIS-GPP}}\), \(\text{GPP}_{\text{EC-LUE}}\) and \(\text{GPP}_{\text{EC}}\) is obvious for all sites. \(\text{GPP}_{\text{MODIS-GPP}}\) exhibited the best performance with \(\text{GPP}_{\text{EC}}\) at the US-Ton site (R\({}^{2}\) = 0.73), and the weakest performance for the US-Rws site (R\({}^{2}\) = 0.41). In addition, \(\text{GPP}_{\text{MODIS-GPP}}\) and \(\text{GPP}_{\text{EC-LUE}}\) were underestimated or overestimated at several sites, such as the US-Bi2 site, US-GLE site, US-WCr site and US-PFa site. Compared with \(\text{GPP}_{\text{MODIS-GPP}}\) was superior to \(\text{GPP}_{\text{EC}}\) at the US-Ton site, US-Var site, and US-Bi2 site, and \(\text{GPP}_{\text{EC-LUE}}\) was superior at the US-Ton site, and the US-Var site;  
(2) Compared with \(\text{GPP}_{\text{EC}}\), the performance of \(\text{GPP}_{\text{GR}}\) and \(\text{GPP}_{\text{VI}}\) varied across different biome types. Good performances were obtained across deciduous broadleaf forests, mixed forests, grasslands, and croplands, and low R\({}^{2}\) values were obtained across evergreen needleleaf forests, shrulands, and woody savannas;  
(3) At the regional scale, \(\text{GPP}_{\text{MODIS-GPP}}\) and \(\text{GPP}_{\text{EC-LUE}}\) both showed a very high spatial\(-\)temporal consistency with \(\text{GPP}_{\text{MOD17}}\). The GPP value was high in the western coastal area and low in southwestern shrubland across California. The annual mean GPP value and maximum daily GPP exhibited a strong correlation with \(\text{GPP}_{\text{MOD17}}\) in broadleaf forests, mixed forests, grasslands and croplands, but a relatively weak correlation in evergreen needleleaf forests and shrulands.  
Conceptualization, F.Z.; methodology, F.Z.; software, F.Z.; validation, F.Z., and Z.Z.; formal analysis, F.Z.; investigation, F.Z. and Z.Z.; resources, F.Z.; data curation, F.Z.; writing\(-\)original draft preparation, F.Z.; writing\(-\)review and editing, Z.Z. and L.Z.; visualization, F.Z.; supervision, Y.L. and L.Z.; project administration, F.Z. and Z.Z.; funding acquisition, Y.L. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2017 YFB0503500), the National Natural Science Foundation of China (41930104), and the Natural Science Foundation of Jiangsu Province (SBK2019022628).  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors would like to thank the AmeriFlux community for the eddy covariance data they provided for free, as well as the science team members who produce and manage the MODIS, MERRA2 meteorology reanalysis data and Sentinel-3 data.  
The authors declare no conflict of interest.