## 4 Discussion  
### Importance of Solar Radiation Intensity for Better SIF Modelling  
The biggest problem in existing SIF prediction models is that the influence of \(\Phi_{SIF}\) is not well considered. \(\Phi_{SIF}\) is mainly determined by heat dissipation (\(NPQ\)) and the fraction of PSII reaction centers (\(q_{L}\)) [32]. \(NPQ\) increases as PAR increases, while \(q_{L}\) decreases with increased PAR [59; 74; 75]. Therefore, the solar radiation intensity should be taken into account for better SIF modelling. The relationship between cos (SZA) and PAR is approximately linear for clear-sky conditions. Based on the above relationship, we have drawn a conceptual diagram that briefly indicates the relationship between cos (SZA\({}_{0}\)) and NPQ/\({}_{\mathrm{GL}}\), as Figure 9. For satellite observations, only retrievals at clear-sky conditions were available; therefore, in this study, the cosine of the solar zenith angle at noon (cos (SZA\({}_{0}\))) was used to represent the seasonal variation of solar radiation intensity.  
The results presented in this paper have confirmed that cos (SZA\({}_{0}\)) can be used successfully as a proxy for solar-radiation-intensity information to produce better modelling based on satellite SIF data. A certain degree of correlation was found between cos (SZA\({}_{0}\)) and tower-based and satellite SIF apparent yield. The apparent SIF yield increased as cos (SZA\({}_{0}\)) increased. Analysis of the importance of different variables using the RF approach clearly showed that cos (SZA\({}_{0}\)) was the most important variable for the modelling of \(SIF_{normalized}\). Therefore, our results were consistent with those of [PERSON] et al. [32], which emphasized that, in theory, the solar radiation intensity is the main factor affecting the apparent SIF yield.  
Figure 8: Comparison of TROPOMI SIF against continuous TanSat SIF for August 2018. The black dotted diagonal line depicts the 1:1 relationship. The black solid line depicts the fitted line. Only the results for the range 0–3 mW m\({}^{-2}\)\(\upmu\)m\({}^{-1}\) sr\({}^{-1}\) are shown.  
The seasonal trend in SIF at the Niwot Ridge site, where the majority of the forest consisted of needleaved evergreens, was investigated using both continuous TanSat and tower-based SIF measurements made by [PERSON] et al. (2016), and also compared with the tower-based NDVI observations, as illustrated in Figure 10. Here, we used the daily mean clearness index (CI) (only CI \(>0.6\) was used) to determine whether it was clear skies (2016). We calculated the daily SIF under clear-sky conditions, if there are more than three samples a day. We found that the seasonal trend in the continuous TanSat SIF values was consistent with that observed in the tower-based SIF data. The SIF signal reached its peak in summer and remained low in winter. In the first growing season, the slightly difference may be caused by observation error. The low values of SIF for evergreen needleleaf forest in winter season may be caused by snow cover (2016). Combined with in-situ APAR and tower-based SIF under clear-sky conditions, the high value of TanSat SIF may be due to the fact that the condition of cloudy days was not considered in the model. The differences between TanSat SIF and tower-based SIF may also be due to the time scale and geographic coverage. The TanSat SIF dataset consisted of 0.05-degree instantaneous SIF values acquired at 13:30 local time, whereas the tower-based SIF values used were the daily averages for the station. NDVI was relative stable for all seasons around 0.8. Compared with NDVI, it was found that the SIF can better track seasonal changes in vegetation, which indicates that the spatially continuous TanSat SIF contains more physiological information related to plant photosynthesis.