## 3 Results
### The Impacts of Growth and Environmental Parameters on Diurnal SIF  
To assess the diurnal SIF patterns using PCA, we only analyzed the relationship between hourly SIF and environmental parameters because growth parameters (e.g., LAI, Chl, or RWC) in a day could be considered as constant. Results of PCA showed that SWR was the most important environmental driver for the diurnal variation of F760, both in wheat and maize (r values: \(0.55\) and \(0.51\), \(p<0.01\), Figure 7). Other environmental parameters showed secondary impacts on diurnal F760 with the order of Rh, VPD, Ta, and SM for wheat (r values: \(0.39\), \(0.29\), \(0.13\), and \(0.11\), respectively) and the order of Ta, VPD, Rh, and SM for maize (r values: \(0.37\), \(0.21\), \(-0.09\), and \(0.02\), respectively; Figure 7). By normalizing F760 with SWR and L685, the impacts of SWR on AF760 and Fy760 distinctly became weaker both for wheat and maize (r values: \(0.1\) and \(0.09\) for wheat, \(0.06\) and \(0.01\) for maize, respectively; Figure 7). The impacts of VPD and Rh on the variation of AF760 and Fy760 in wheat were also decreased, as well as the impact of VPD and Ta on maize AF760 and Fy760 (Figure 7). Nevertheless, the impacts of Ta on wheat AF760 and Fy760 were larger relative to F760, and similarly with the increased effect of Rh on maize AF760 and Fy760 (Figure 7). Furthermore, for wheat, the effect of SM on Fy760 became larger but vanished on AF760.  
From the diurnal patterns of SIF, we found that SIF at noon time could further reflect the variation of diurnal SIF with the development of phenological stages (Figure 5). Thus, relationships between SIF at noon time and growth parameters were further analyzed through analyzing the Pearson correlation. Based on the analysis over wheat measurements, we found that F760, AF760, and Fy760 at noon time were mainly affected by LAI (r values: \(0.56\), \(0.53\), and \(0.54\), Figure 8a). The considerable impact of Chl on F760 and AF760, but negligible impact of Chl on Fy760, were observed (r values: \(0.51\), \(0.5\), and \(0.13\), respectively; Figure 8a). The impact of RWC was significant on AF760 and Fy760 but insignificant on F760 (r values: \(0.64\), \(0.9\), and \(0.08\), respectively; Figure 8a). However, based on maize measurements, we found that most growth parameters played negligible roles for driving diurnal variation of F760, AF760, and Fy760. Only Chl showed a significant correlation with Fy760 (r value: \(0.27\), \(p<0.01\); Figure 8b), and therefore played an important role in driving the diurnal pattern of Fy760.  
Figure 8: Relationships of growth parameters with F760, AF760, and Fy760 at noon at the diurnal scale. Left is wheat (**a**), while right is maize (**b**). The significant correlation between each of the two variables (\(p\)-value \(<\) 0.05) is shown in italics/bold.  
Figure 7: Relationships of environmental/growth parameters with F760, AF760, and Fy760 at the diurnal scale. Left column is wheat and right column is maize. (**a**,**b**) are PCA results and (**c**,**d**) are Pearson correlation results. Relationship between variables in PCA is indicated by a vector angle: if vector angle between each two variables was 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**) was shown in italics/bold.