## 4 Discussion
### Potential of Model SIF from MODIS and Meteorological Data  
Simulated SIF results based on only EVI and EVI combined with temperature were compared. When simulated using only EVI, the EVI data of 2018 were selected, the liner regression model of Figure 5 was employed, and the simulated SIF of each 16-day interval (SIF\({}_{\text{Modeled}}\)) was obtained. Taking OCO-2 of 2018 as the observed value, scatter plots of SIF\({}_{\text{Modeled}}\) and SIF\({}_{\text{OCO-2}}\) are shown in Figure 12. The obtained R\({}^{2}\) value was 0.16 (RMSE = 0.22 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\), P \(<\) 0.001) on Julian day 257 and 0.46 (RMSE = 0.29 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\), P \(<\) 0.001) on Julian day 193. Similar regression effects on Julian days 209, 225, and 241 were found as EVI remained stable and reached its maximum value. Across all data, R\({}^{2}\) = 0.58 (RMSE = 0.14 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\), P \(<\) 0.001). EVI, as an index of greenness, can reflect vegetation canopy and leaf area and explain a part of SIF. [PERSON] and [PERSON] [28] used MODIS EVI and meteorological factors to simulate SIF and found a similar conclusion that temperature affects plant photosynthesis and SIF. However, in boreal forests, we only found a significant correlation between SIF and temperature under similar meteorological conditions. As Figure 10 shows, SIF had a significant relationship with daily mean temperature at the 16-day time scale (except for Julian day 257). Multi-regression models were built using EVI and temperature (Table 1), and simulated results are shown in Figure 13. As there was no significant correlation between temperature and SIF on Julian day 257, SIF values from Julian day 145 to 241 were simulated. The scatter plot information between OCO-2 observed data and the multi-regression model simulated SIF is shown in Table 1. The R\({}^{2}\) values improved compared to Figure 12, suggesting that EVI and temperature explained a large proportion of the variation in SIF. Figure 12: _Cont._  
For the boreal forest, our predictive multi-regression model (R\({}^{2}\) = 0.71, RMSE = 0.13 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)) performed slightly better than that of [PERSON], et al. (2018) (R\({}^{2}\) = 0.46, RMSE = 0.065 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)) and [PERSON] and [PERSON] (2018) (R\({}^{2}\) = 0.67, RMSE = 0.07 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)). Obviously, our result had a higher R\({}^{2}\) and RMSE; a higher R\({}^{2}\) indicates higher simulation accuracy, but a larger RMSE means greater simulation error. In this study, the larger RMSE was the result of a small number of training samples. As mentioned above, to avoid spatial autocorrelation, 200 SIF points were randomly selected for each 16-day interval, and a small training sample has a larger prediction error ([PERSON] et al., 2018; [PERSON] et al., 2018). All three studies used a similar spatial resolution of 0.05\({}^{\circ}\) or 1 km (but different data processing methods). The different simulation accuracies of the three studies were also due to different data processing: [PERSON], et al. (2018) used the mean value of 5 points for the same land cover type to improve the accuracy of SIF; [PERSON] and [PERSON] (2018) used EVI, PAR, vapor pressure deficit (VPD), and air temperature to model SIF, and both studies simulated SIF for all the land cover types. Our research focused on only the boreal forest, and we did not find any significant correlation between environmental conditions and SIF, except for air temperature; this study only used EVI and air temperature to simulate SIF at a 1 km spatial resolution.