# Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems
## 3 Results  
### Seasonal Patterns of SIF and GPP and Their Relationship  
Due to instrument failures, there were some data gaps for alpine meadow spectral and flux data in both the 2019 and 2020 growing seasons, but the available data could cover most growing stages of alpine meadow. Figure 2 illustrates the seasonal variations of SIF\({}_{\text{Red}}\), SIF\({}_{\text{Far-red}}\), GPP, and NDVI. At the seasonal scale, they show consistent and obvious \"single-peaked\" seasonal patterns. NDVI reaches its maximum value for the whole growing season at about 205-220 days of day of year (DOY), and the corresponding SIF\({}_{\text{Red}}\), SIF\({}_{\text{Far-red}}\)and GPP also reach their maximum values during the same period. There is relatively small variation in the seasonality of photosynthetically active radiation (PAR), with generally high PAR values over the entire growing season. The relationships between \(\text{SIF}_{\text{Red}}\) and \(\text{SIF}_{\text{Far-red}}\) with GPP in alpine meadows were analyzed with half-hourly and daily averaged datasets. Both a linear function model  
Figure 2: Seasonal patterns of (**a**) the canopy solar-induced chlorophyll fluorescence at the far-red band (\(\text{SIF}_{\text{Far-red}}\)). (**b**) the canopy solar-induced chlorophyll fluorescence at the red band (\(\text{SIF}_{\text{Red}}\)), (**c**) gross primary productivity (GPP), (**d**) photosynthetically active radiation (PAR), (**e**) normalized difference vegetation index (NDVI) at AR site in the growing seasons in 2019 (**left**) and 2020 (**right**). The dark large dots represent daily averaged data, and the light small dots represent half-hourly averaged data. (\(y=kx\)), with the intercept of the linear function set to zero, and a nonlinear function model (\(y=(p_{1}x+p_{2})/(x+p_{3})\)) were adopted to fit SIF and GPP relationship (Table 1). The differences in R\({}^{2}\) between the nonlinear and linear fitted function models for the SIF\({}_{\text{Red}}\)-GPP relationship are small with regard to both half-hourly and daily averaged data and are larger for the SIF\({}_{\text{Far-red}}\)-GPP relationship, especially concerning the half-hourly averaged data, where the R\({}^{2}\) of the fitted results of the nonlinear function model is 12.86% higher than that of the linear function model. The difference in RMSE between the nonlinear and linear fitted function models for both SIF\({}_{\text{Red}}\)-GPP and SIF\({}_{\text{Far-red}}\)-GPP is obvious; for the half-hourly data, the RMSE of the linear fitted results for SIF\({}_{\text{Red}}\)-GPP and SIF\({}_{\text{Far-red}}\)-GPP are 31.49% and 59.44% higher than the corresponding nonlinear fitted results, respectively, and for the daily averaged data, 24.89% and 64.93% higher, respectively. Although for both SIF\({}_{\text{Red}}\)-GPP and SIF\({}_{\text{Far-red}}\)-GPP, the RMSE of the linear fitted results are higher than the nonlinear fitted results for both the half-hourly and daily averaged data, considering the R\({}^{2}\) of the fitted results and the scatter plot distribution of the data together (Figure 3), the SIF\({}_{\text{Red}}\)-GPP relationship tends to be more linear than the SIF\({}_{\text{Far-red}}\)-GPP relationship. The R\({}^{2}\) of the linear fitted SIF\({}_{\text{Red}}\)-GPP relationship and that of the nonlinear fitted SIF\({}_{\text{Far-red}}\)-GPP relationship are also close. These results indicate that the performance of SIF\({}_{\text{Red}}\) and SIF\({}_{\text{Far-red}}\) were comparable for monitoring GPP in an alpine meadow.