## 3 Results
### Effects of Weather Conditions on GPP Estimation Using PRI and SIF  
To clarify the complex effects of the CI on GPP estimation using SIFTOI_FULL_PSII and \(\Delta\)PRI, we first examined how the CI affected the ratio of GPPEEC to SIFTOI_FULL_PSII and \(\Delta\)PRI. From Figure 5, we can see that the ratio of GPPEEC to SIFTOI_FULL_PSII obviously decreased with an increasing CI for the half-hourly (r = \(-\)0.65, \(p\) < 0.01) and daily (r = \(-\)0.80, \(p\) < 0.01) data. However, the ratio of GPPEEC to \(\Delta\)PRInorm (the normalized \(\Delta\)PRI) showed an increased trend with an increasing CI for the half-hourly (r = 0.45, \(p\) < 0.01) and daily (r = 0.31, \(p\) < 0.01) data.  
In addition, we investigated the connection between the CI and the ratio of GPPEEC to GPPTOT_APRI (Figure 6). The ratio of GPPEEC to GPPTOT_APRI exhibited a non-linear correlation with the CI for both the half-hourly (R\({}^{2}\) = 0.21) and daily (R\({}^{2}\) = 0.25) scales.  
Figure 4: The relationship between GPPEEC and the modeled GPP. (**a**,**e**) show the relationships between GPPEEC and GPPTOC for half-hourly data and daily data, respectively, and the relationships between GPPEEC and GPPTOT for half-hourly data and daily data are shown in (**b**,**f**). (**c**,**g**) show the relationships between GPPEEC and GPPTOC_PRI for half-hourly data and daily data, respectively, and the relationships between GPPEEC and GPPTOC_APRI for half-hourly data and daily data are shown in (**d**,**h**). The CI value is represented by the color scale. The best-fit line is shown by the solid line, while the short, dashed line is the 1:1 line.  
Moreover, we performed partial correlation analysis to investigate the impacts of the CI on GPP estimation using \(\text{SIF}_{\text{TOT}\_\text{FULL}\_\text{PSI}}\) and \(\Delta\text{PRI}\) by controlling various environmental parameters and structural indicators (Table 1). It can be seen that the ratio of \(\text{GPP}_{\text{EC}}\) to \(\text{GPP}_{\text{TOT}\_\text{FULL}}\) exhibited a significant correlation with the CI by controlling the near-infrared reflectance (NIR) of the vegetation portion (NIRv), NDVI, PAR, Ta, and VPD. This indicates that the effects of the CI should be integrated into GPP estimation models based on \(\text{SIF}_{\text{TOT}\_\text{FULL}\_\text{PSI}}\) and \(\Delta\text{PRI}\).  
Figure 5: Dependence of the \(\text{GPP}_{\text{EC}}\)/\(\text{SIF}_{\text{TOT}\_\text{FULL}\_\text{PSI}}\) on the clearness index (CI) ((a) for half-hourly data, (c) for daily data). The correlations between \(\text{GPP}_{\text{EC}}\)/\(\Delta\text{PRI}_{\text{norm}}\) and CI for half-hourly data (**b**) and daily data (**d**). The best-fit line is shown by the solid blue line.  
Figure 6: Relationships between the ratio of \(\text{GPP}_{\text{EC}}\) to \(\text{GPP}_{\text{TOT}\_\text{FRI}}\) and CI for half-hourly data (**a**) and daily data (**b**). The best-fit line is shown by the solid blue line. The asterisk refers to a multiplication sign.  
In addition, we also used an RF model to evaluate the effects of the CI on the modeling of GPP using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\). The different relationships between \(\text{GPP}_{\text{EC}}\) and \(\text{GPP}_{\text{RF}}\) and \(\text{GPP}_{\text{RF\_CI}}\) revealed the influence of the CI on GPP estimation. When considering the effects of the CI, the GPP estimation accuracy was improved (R\({}^{2}\) increased from 0.72 to 0.77 and the RMSE decreased from 7.60 to 6.94 \(\upmu\text{mol}\text{e}\text{m}^{-2}\text{s}^{-1}\), Figure 7a,b). Therefore, the effects of the CI should be considered in GPP estimation based on \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\) for soybean.