## 4 Discussion
At the regional scale, GPP\({}_{\text{MODIS-GPP}}\) and GPP\({}_{\text{EC-LUE}}\) agreed quite well with GPP\({}_{\text{MOD17}}\). For the annual mean GPP, GPP\({}_{\text{MODIS-GPP}}\) and GPP\({}_{\text{EC-LUE}}\) were relatively high in the western coastal areas, where the biome types were mainly evergreen forests. The low GPP\({}_{\text{MODIS-GPP}}\) and GPP\({}_{\text{EC-LUE}}\) were obtained in grasslands. The finding aligned with a previous study [61], where evergreen forests exhibited strong photosynthesis and open shrublands were the least productive. Forest ecosystems have a relatively higher maximum daily GPP, and open shrublands have the lowest maximum daily GPP compared with other vegetation types. Notably, the maximum daily GPP across grasslands in the northeast was larger than in central areas, and the high sensitivity to soil moisture may be the cause of this phenomenon [61]. The inconsistency between the annual mean GPP and maximum daily GPP may be mostly due to the influence of temperature and rainfall on the length of the different growing seasons [61]. In addition, we further analyzed the uncertainty in simulating GPP using Sentinel-3 OLCI products and MERRA2 meteorology reanalysis data in this study. Several factors can influence the GPP estimation using the MODIS-GPP algorithm and EC-LUE model. First is the inconsistency of the spatial resolution between Sentinel-3 and meteorology reanalysis data. The errors may have been derived from the interpolation of MERRA2 meteorology reanalysis data from 0.5\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\) to 300 m. Second is the coarse classification of vegetation types on land cover maps. Boston University's UMD classification scheme in the MOD12Q1 land cover dataset classifies croplands as one category and does not distinguish between C3 and C4 crops. C3 and C4 crops possess various photosynthetic pathways and light use efficiencies [88; 89]. Because the C3/C4 mixing ratio for each cropland pixel is unknown, we simply used the single \(\epsilon_{\text{max}}\) and meteorological parameters to estimate GPP on a large scale. Therefore, GPP\({}_{\text{MODIS-GPP}}\) and GPP\({}_{\text{EC-LUE}}\) are underestimated or overestimated in croplands. Thirdly, the image data quality is also a significant factor influencing GPP estimation. Due to atmospheric contamination (i.e., clouds, aerosols), Sentinel-3 OLCI products have more missing data and low reliability during winter. Future work should be focused on spatial\(-\)temporal fusion techniques, which can further enhance the temporal and spatial continuity of Sentinel-3 OLCI products by fusing Sentinel-2 images with Sentinel-3 OLCI to achieve more accurate regional and large-scale GPP estimation [98; 99]. Although our results showed that GPP\({}_{\text{MODIS-GPP}}\), GPP\({}_{\text{EC-LUE}}\), GPP\({}_{\text{GR}}\) and GPP\({}_{\text{VI}}\) did not always perform better than GPP\({}_{\text{EC}}\) or GPP\({}_{\text{MOD17}}\), it needs to be emphasized that the objective of our study was not to distinguish which model was superior for GPP estimation. Our study aimed to demonstrate the potential of integrating Sentinel-3 OLCI products and MERRA2 meteorology reanalysis data to estimate GPP at site and regional scales. Our results also demonstrated that the LUE model and VI-driven model were suitable for ESA's Sentinel-3 data. The Sentinel-3 Sea and Land Surface TemperatureRadiometer (SLSTR) provides land surface temperature (LST) products with a higher temporal resolution. In future research, the integration of SLSTR LST products and OLCI FAPAR or meteorology reanalysis data in other models for estimating GPP should be conducted. For a wider application of Sentinel-3 OTCI products, future work requires more in situ data to explore the OTCI and GPP's relationship. Moreover, further investigations should focus on combining other remote sensing data or satellite-driven models with OTCI-based models. Previous studies have found that meteorological factors could improve VI-driven GPP estimation models [23].