## 3 Results
### The Impacts of Growth and Environmental Parameters on Seasonal SIF  
To elucidate the relationship of seasonal SIF patterns (including F760, AF760, and Fy760) with environmental and growth parameters, we applied principal component analysis (PCA) based on the daily measurements of wheat and maize, respectively (Figure 6). Two dominating factors were identified using PCA as PC1 and PC2, which were responsible for 37.07% and 31.68% for wheat and 34.12% and 26.94% for maize, respectively, of the variance in all parameter distributions (Figure 6). Figure 5: Daily patterns of F760 (first column), AF760 (second column), and Fy760 (third column) from wheat (**a–c**) and maize (**d–f**) compared with respective solar radiation (SWR). The circles in the figure represent the hourly values that were averaged from the 15-day measurements for wheat and maize. The detailed time periods of wheat were 28/3–11/4, 14/4–28/4 and 12/5–19/5, and the time periods of maize were 29/7–11/8, 12/8–26/8, 27/8–10/9, and 11/9–24/9. The fourth pattern of wheat was only calculated based on the measurements from a week because of the terminal of the field campaign. As described in Figure 6a,b, non-right angles were found between F760 and all environmental parameters both for wheat and maize, showing that all these parameters played important roles in controlling seasonal patterns of F760. Their impacts on wheat seasonal F760 was in the order of Rh, SWR, VPD, SM, and Ta (r values: \(-\)0.55, 0.43, 0.35, 0.08, and 0.06, respectively), while their impacts on maize F760 was in the order of Ta, SWR, SM, Rh, and VPD (r values: 0.43, 0.14, \(-\)0.09, 0.03, and 0, respectively). Moreover, the correlation between Rh and wheat F760 and the correlation between Ta and maize F760 were significant (\(p<0.05\)), indicating that Rh and Ta were vitally important environmental drivers for the seasonal variation of F760 from wheat and maize, respectively. Similarly, PCA outputs showed that all growth parameters (LAI, Chl, MTA, and RWC) are also important for driving the seasonal patterns of F760 due to their non-right angles with F760. However, LAI and MTA were believed to be more important drivers than other growth parameters for seasonal wheat F760 because of their significant correlations (0.63 and \(-\)0.63, respectively; \(p<0.05\)). Unlike wheat, maize F760 only significantly and negatively correlated with MTA (\(-\)0.53, \(p<0.05\)), which means that MTA was vitally important for the seasonal pattern of maize F760. Figure 6: Relationships of environmental/growth parameters with F760, AF760, and Fy760 at the seasonal scale. Left column is wheat and right column is maize. (**a,b**) are PCA results and (**c,d**) are Pearson correlation results. The relationships between variables in PCA is indicated by vector angle: if the vector angle between two variables is acute, they were positively correlated; else if the angel is obtuse, these two variables were negatively correlated (right angle represents uncorrelated). The smaller the included angle was, the higher the correlation it showed. The significant correlation between each of the two variables (\(p\)-value \(<0.05\)) in (**c**) and (**d**) is shown in italics/bold. Only the measurements synchronously collected with growth parameters were used for PCA and Pearson correlation analysis. The effects of these environmental and growth parameters on AF760 and Fy760 were different from F760. Specifically, the impacts of SWR on AF760 and Fy760 became weaker according to the observation of their nearly right angles in the PCA coordinates (Figure 6a). Such weaker impacts were further confirmed by the lower correlations of SWR with AF760 and Fy760 (\(-0.2\) and \(0.01\), Figure 6c). In addition, both VPD and Rh also showed weaker correlations with AF760 and Fy760 (VPD: \(-0.28\) and \(-0.05\); Rh: \(-0.06\) and \(-0.05\), respectively; Figure 6c), possibly due to the covariation of VPD and Rh with SWR (r values: \(0.99\) and \(-0.76\), \(p<0.05\)).