## 3 Results
### Comparison between Instantaneous SIF and Daily SIF Across Space and Time  
To investigate whether the upscaled \(\mathrm{SIF_{daily}}\) can reduce the variations in the \(\mathrm{SIF_{ins}}\)-\(\mathrm{SIF_{daily}}\) correlation across space and time, we compared \(\mathrm{SIF_{ins}}\) (at 9:30) and the upscaled \(\mathrm{SIF_{daily}}\) using two upscaling methods (i.e., the cos-based and PAR-based methods) with the measured \(\mathrm{SIF_{daily}}\) under different weather conditions and different latitudes (simulated data). Figure 6 displays the correlations between \(\mathrm{SIF_{ins}}\) and upscaled \(\mathrm{SIF_{daily}}\) with the measured \(\mathrm{SIF_{daily}}\) for sunny and cloudy days at the two sites. On sunny days (labeled with blue symbols in Figure 6), the \(\mathrm{SIF_{ins}}\)-\(\mathrm{SIF_{daily}}\) correlations for different days were relatively stable, with \(\mathrm{R^{2}}\) values larger than 0.9. In this case, the relevance of the measured \(\mathrm{SIF_{daily}}\) with upscaled \(\mathrm{SIF_{daily}}\) was similar to that with \(\mathrm{SIF_{inst}}\): the \(\mathrm{R^{2}}\) values for the three kinds of SIF were basically the same and the RRMSE values for \(\mathrm{SIF_{daily}}\) using the two upscaling methods were comparable and both less than 15%. On cloudy days (labeled with black symbols in Figure 6), the \(\mathrm{SIF_{ins}}\)-\(\mathrm{SIF_{daily}}\) correlations changed remarkably across different days; \(\mathrm{R^{2}}\) was approximately 0.7. Note that these variations can only be reduced by upscaling \(\mathrm{SIF_{inst}}\) using the PAR-based method; \(\mathrm{R^{2}}\) for the PAR-based upscaled \(\mathrm{SIF_{daily}}\) was significantly larger than that for \(\mathrm{SIF_{inst}}\). However, upscaling \(\mathrm{SIF_{inst}}\) using the cos-based method made no improvement for the \(\mathrm{SIF_{ins}}\)-\(\mathrm{SIF_{daily}}\) correlation on cloudy days; \(\mathrm{R^{2}}\) for this method was almost equal to that for \(\mathrm{SIF_{inst}}\), and the corresponding RRMSE value was significantly greater than that for the PAR-based method. Apart from weather conditions, latitudes were also an influential factor on the variations in \(\mathrm{SIF_{ins}}\)-\(\mathrm{SIF_{daily}}\) correlations across space and time (see Figure 1). Figure 7 displays the correlations between \(\mathrm{SIF_{ins}}\) and upscaled \(\mathrm{SIF_{daily}}\) with the measured \(\mathrm{SIF_{daily}}\) for four different latitudes simulated using the observations at the XTS and DM sites. Considering that the \(\mathrm{R^{2}}\) value of the regression without an intercept is very close to that with an intercept, we conducted the regression with a zero intercept to better compare the slopes of the \(\mathrm{SIF_{ins}}\)-\(\mathrm{SIF_{daily}}\) correlations among the four latitudes. The corresponding \(\mathrm{R^{2}}\) values and coefficient of variation (CV) of the regression slopes for the four latitudes were also calculated. The variations in the \(\mathrm{SIF_{ins}}\)-\(\mathrm{SIF_{daily}}\) correlations across different latitudes can be significantly reduced by upscaling the \(\mathrm{SIF_{inst}}\) to a daily scale using both the cos-based and PAR-based methods. Specifically, the regression slopes for the upscaled \(\mathrm{SIF_{daily}}\) exhibited substantially smaller variations than those for the \(\mathrm{SIF_{inst}}\) (CV = 0.034 for the cos-based method). For the PAR-based method, the regression slopes tended to be normalized at a constant value, with a CV of 0.01. However, for individual latitudes, only the PAR-based method corrected the influence of weather conditions on the \(\mathrm{SIF_{ins}}\)-\(\mathrm{SIF_{daily}}\) correlations, with impressively larger \(\mathrm{R^{2}}\) values than those of the \(\mathrm{SIF_{inst}}\) but the cos-based method did not achieve this effect with nearly the same \(\mathrm{R^{2}}\) values as those for the \(\mathrm{SIF_{inst}}\).