## 4 Discussion
### The SIF-GPP Relationship on Different Timescales  
A significant defect of the integration of satellite SIF data with ground-based GPP data is that the compatibility of temporal resolution and the more complex correlations between SIF and GPP on smaller timescales may be ignored. Ground simultaneous observation can make up for the defect. Our research found that SIF and GPP have different correlations with different time scales which is partly different from the results of previous studies. On the 30 min timescale, we found a good linear correlation between SIF and GPP and on cloudy days the correlation coefficient was larger and the points more concentrated. SIF and GPP linear regression was larger on cloudy days that exhibit a higher proportion of diffuse PAR, which is consistent with findings for maize [21], rice paddy [19], and mixed broad-leaved forest [49]. Middle and lower parts of the canopy can receive more radiation under cloudy conditions [5; 47], enabling the middle and lower canopy to participate inSIF emission and photosynthesis. However, the influence of measurement techniques must be taken into account in that tower-based spectrometers will predominantly receive the SIF signal that was emitted from the top canopy leaves and escaped from the inside canopy [57; 58], meanwhile, GPP divided from EC measurements comes from a larger area, and the carbon released by the internal leaves will eventually escape from the canopy [59]. The inconsistency of the footprints may be the cause of some unsatisfactory results, especially on sunny days. The SIF tower was built in the upwind direction of the EC system tower to minimize this source of error. Besides, a more efficient use of diffuse PAR resulted in the rise of GPP on cloudy days [50]. This may explain the increase in apparent SIF\({}_{\text{yield}}\) on cloudy days. In addition, this study has inconsistent results with previous studies [21; 47] and possible explanations are given in the following analysis. We found that with the aggregation of timescales, the correlation of SIF-GPP deteriorated. The 30 min datasets capture the important correlations on the daily scale, with details removed only when examining daily mean SIF and GPP. We found GPP /SIF was also driven by PAR on the seasonal scale, the correlation deterioration between SIF and GPP from the daily to the seasonal scale was due to the seasonal fluctuation of PAR. We also found that the correlation between SIF and GPP on sunny days exhibited contrast correlation under different PAR conditions. On the 30 min timescale, SIF and GPP both increased with an increase of PAR up to 1250 \(\upmu\text{mol}\cdot\text{m}^{-2}\cdot\text{s}^{-1}\), then GPP decreased with the increase of PAR, which is the opposite to SIF. This is mainly due to the fact that the high radiation is usually accompanied by high temperature during the midday /afternoon period, which may cause the plant photosynthesis \"depression\" in noon and afternoon, where NPQ is not affected by instantaneous stress and SIF increases with PAR, presenting the nonlinear relationship between SIF and GPP [31]. In the subsequent analysis, we find that the change of the relationship mainly depends on the magnitude of PAR, similar results were reported in a previous study [30]. In many studies, APAR is considered to be the main factor driving GPP/SIF changes [60; 61]. Our research further confirms the key role of APAR in affecting the SIF-GPP relationship. In addition to APAR, although we tried to avoid the difference caused by the growth rhythm, the growth rate in early June was still significantly lower than other phases, which might be due to the early drought in spring at the study site [42] instead of low precipitation in June, and this indirectly led to the unusual negative correlation between GPP and PAR on the daily scale. We also found that another part of the reason for the weakening of the correlation is that SIF and GPP have different responses to different meteorological factors.