## 3 Results
### Effect of Observation Modes and Bands on Correlating OCO-2 SIF with GPP\({}_{\text{EC}}\)  
In order to explore the relationship between OCO-2 SIF and GPP\({}_{\text{EC}}\) in different modes and bands, we analyzed the relationship between SIF and GPP\({}_{\text{EC}}\). Table 6 shows the parameters of SIF757-GPP\({}_{\text{EC}}\) and SIF771-GPP\({}_{\text{EC}}\) linear models for the different OCO-2 observation modes. It can be concluded that the amount of SIF data in nadir mode and glint mode is greater than that of the target mode in North America [19]. Thus, because nadir and glint mode were observed every 16 days, more SIF data were obtained. Meanwhile, glint mode had the least number of observations. Moreover, the spatial resolution of data in nadir mode is the highest, so the correlation between SIF data in nadir mode and GPPEC is more efficient [32]. For different vegetation types, the R\({}^{2}\) of relationships between SIF and GPP varies with the observation mode. The amount of OCO-2 SIF data in nadir mode is the largest and there is a strong linear correlation with GPP\({}_{\text{EC}}\). Because nadir and glint mode were observed every 16 days, more SIF data were obtained. In general, SIF757 and SIF771 data showed a strong correlation with GPP in all observation modes [11; 13; 24]. The linear correlation between SIF757 and GPP is stronger than that between SIF771 and GPP. It could be responsible for the fact that 757 nm is closer to the peak of the SIF spectrum than SIF771. For WET, the correlation between SIF and GPP of US-Tw1 and US-Tw4 of the glint mode is low, mainly due to the small amounts of SIF in glint mode. Figure 6: The seasonal trends of tower GPP and GOSIF data from 2015 to 2017 for different vegetation types: (**a**) CRO, (**b**) CSH, (**c**) DBF, (**d**) ENF, (**e**) GRA, (**f**) MF, (**g**) OSH, and (**h**) WET. Cyan square indicates flux tower GPP and the orange square indicates GOSIF data. The Impact of the Extraction Range on Relationships Between the Remotely Sensed Product and \(\text{GPP}_{\text{EC}}\)  
In this study, we extracted 0.1-degree GOSIF data around each flux tower, 5 km MODIS GPP data, and 25 km SIF757 data, to establish relationships with \(\text{GPP}_{\text{EC}}\). Then, we compared the performance of GPP estimates from SIF757, MODIS GPP, and GOSIF. However, the different ranges of each data extraction result in a different proportion of heterogeneity between pixels, which may affect the results obtained when comparing the three data estimates' GPP accuracy [19]. Therefore, on the one hand, in order to compare the accuracy of SIF757, MODIS GPP, and GOSIF, GPP was estimated in the same research range. On the other hand, in order to explore whether the extracted range would affect the relationship of SIF757, MODIS GPP, and GOSIF, we also extracted GOSIF and MODIS GPP data within 25 km around each flux tower. Then, the relationship of SIF757, MODIS GPP, and GOSIF in the same extraction range was analyzed. Table 7 shows the linear relationship between GOSIF, SIF757, and MODIS and \(\text{GPP}_{\text{EC}}\) after extending the extraction range to 25 km. In general, the linear relationship between GOSIF and \(\text{GPP}_{\text{EC}}\) is the best, followed by MODIS and OCO-2 SIF. However, the correlation between GOSIF data and \(\text{GPP}_{\text{EC}}\) was low of GRO. This may be due to the CRO's \(\text{GPP}_{\text{EC}}\) data in 2017 with errors, which we can see from Figure 6. On the whole, those results confirm that GOSIF yields the best performance in estimating GPP, regardless of the extraction range and vegetation types. However, the R\({}^{2}\) of three models slightly decreased to different extents after extending the extraction range, highlighting the significance of