## 4 Discussion
### Potential Influences of Weather Conditions on GPP Estimation Using PRI and SIF  
Although combining SIF and PRI has been proven to be beneficial for quantifying GPP based on ground, airborne, or satellite observations [27; 31; 32; 36], the potential impact of weather conditions on GPP estimation using SIF and PRI has yet to be investigated based on tower-based measurements. In this study, we examined the impacts of weather conditions on GPP estimation using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) (broadband photosystem-level SIF for photosystem II) and \(\Delta\text{PRI}\) (formed of early morning and midday PRIs) and explored the usefulness of combining weather conditions to improve soybean GPP estimation. The results showed that the CI can be used to improve GPP estimation using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\) (Table 2, Figure 7). Using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\) for GPP estimation can cause carbon assimilation in bright conditions, while the quantum yield of SIF stays comparatively stable [14]. In addition, \(\Delta\text{PRI}\) is representative of NPQ and generally exhibited a downward trend under increasingly bright conditions [29; 34]. Some studies have reported that the APAR is more related to SIF than GPP [11; 18; 56]. The \"diffuse light fertilization effect\" and interactions between other cloud-related environmental factors may cause the increased uptake of carbon under cloudy conditions [57; 58; 59; 60].  
In this study, we found that the ratio of GPP to \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) obviously decreased with an increasing CI (r = \(-\)0.65 and \(p<\) 0.01 for the half-hourly data, r = \(-\)0.80 and \(p<\) 0.01 for the daily data, Figure 5), which agrees with previous studies [48]. However, the ratio of \(\text{GPP}_{\text{EC}}\) to \(\Delta\text{PRI}_{\text{norm}}\) (the normalized \(\Delta\text{PRI}\)) 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. This indicates that the effects of the CI on GPP estimation using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\) are complex. The ratio of \(\text{GPP}_{\text{EC}}\) to \(\text{GPP}_{\text{TOT\_APRI}}\) exhibited a non-linear correlation with the CI along both the half-hourly (R\({}^{2}\) = 0.21) and daily (R\({}^{2}\) = 0.25) scales. In addition, based on partial correlation analysis by controlling various environmental parameters and the structural indicators (Table 1), the impact of the CI on GPP estimation using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\) was significant. Therefore, the impact of the CI should be incorporated into GPPestimation models using \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\). Nevertheless, the potential underlying mechanisms of linking \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\) to GPP need to be explored in the future.  
It should be noted that the CI is associated more with the proportional intensity of solar incident radiation [48; 49]. The CI can be used to differentiate between weather conditions, such as sunny and cloudy days. However, the CI is a weather condition. The precipitation index (for example, the simple daily intensity index and the SDII) and light intensity can also be used to characterize weather conditions. Future research needs to investigate the impact of other conditions on GPP estimation based on SIF and PRI.