## 3 Results
### Agreement between \(\text{GPP}_{\text{OTCI}}\) and \(\text{GPP}_{\text{EC}}\)  
The time series of \(\text{GPP}_{\text{EC}}\) and \(\text{GPP}_{\text{OTCI}}\) that was derived from the three OTCI-driven models in 2017-2018 are presented in Figure 7. Overall, the day-to-day variation in the time series of GPP is  
Figure 5: Scatterplots between \(\text{GPP}_{\text{EC}}\) and \(\text{GPP}_{\text{OLCI-FAPAR}}\) (GPP for the Ocean and Land Colour Instrument FAPAR product) for all sites in 2017 and 2018. Significance levels: \(p<0.001\). (**a–g**) represent the sites the same as Figure 2.  
Figure 6: Temporal dynamics of \(\text{GPP}_{\text{EC}}\) and \(\text{GPP}_{\text{OLCI-FAPAR}}\) for all sites in 2017 and 2018.  
evident, whether in GPPEC or GPPOTCI. The consistency in temporal dynamics between GPPEC and GPP from models 1, 2, and 3 were high at most sites. For instance, the growth process of alfalfa from growth to harvest to regrowth was tracked well by the GPP estimated from the OTCI-driven models. However, at the US-Rws site, the GPP obtained from model 3 matched well with the GPPEC while the GPP derived from both models 1 and 2 failed to capture the variation trend. This mismatch can be attributed to the fact that when GPPEC started to decline, the modeled GPP from models 1 and 2 continued to increase. The magnitude of the GPP derived from models 1, 2, and 3 varies considerably across sites. Model 2 gained the maximum value compared with models 1 and 3 among all sites in 2017 and 2018. It is worth noting that the GPP produced by model 3 was close to the GPPEC at the two shrubland sites (US-Rls and US-Rws). At the DBF site, the GPP values modeled at models 2 and 3 were highly consistent, whether in magnitude or in variation trend during the growth period. The reason is that the FAPAR inputted in model 3 is nearly equal to 1, which has a minimal effect on GPP estimation.  
The scatter plots between the GPPOTCI based on three OTCI-driven models and the GPPEC for each site are shown in Figure 8. The performance of the three models varies at different sites, thereby demonstrating that the applicability of the three OTCI-driven models is dependent on the biome types. For instance, insignificant correlations of model 1 were obtained at the US-Rws site, whereas the strongest relationship was exhibited with the GPPEC at the US-WCr site. Even for the same type, their performance also differs due to disparate input parameters. For example, model 3 performed much better than model 1 and 2 at the US-Ton, US-Rls and US-Rws sites. Obviously, the inclusion of FAPAR into the model lead to this result. Additionally, strong relationships were established between GPPEC and all the OTCI-driven models at the US-Bi2, US-WCr, and US-KFS sites. At the Bi1 site, a strong to weak relationship was observed from model 3 to model 1. To sum up, model 3, which includes APAR data, provided a significant relationship with GPPEC at all sites, and model 1 is superior to the other two at several sites.  
Figure 7: Temporal dynamics of GPPEC and three OTCI-driven GPPOTCI for all sites in 2017 and 2018.  
Figure 8: Scatterplots between \(\mathrm{GPPE}_{\mathrm{EC}}\) and \(\mathrm{GPPTCTI}\) for all sites in 2017 and 2018. The left column represents model 1: \(\mathrm{GPP}=f(\mathrm{OTCI})\), the middle column represents model 2: \(\mathrm{GPP}=f(\mathrm{OTCI}\times\mathrm{PAR})\), and the right column represents model 3: \(\mathrm{GPP}=f\left(\mathrm{OTCI}\times\mathrm{PAR}\times\mathrm{FAPAR}\right)\). (**a**–**g**) represent the sites the same as Figure 2.