## 4 Discussion
### Retrieval Evaluation  
Through improvements in the retrieval window and Doppler shift correction of satellite spectra, our data-driven method achieved significantly improved sensitivity to Fs. From the same set of satellite spectra, we retrieved SIF products with higher intensity and credibility, as demonstrated by the monthly validation results at 0.1\({}^{\circ}\) grid cells. As the spatiotemporal resolution (especially spatial resolution) decreases, the differences in fit between the two SIF products also diminish. This is because averaging multiple retrieval results effectively reduces random noise following a normal distribution; specifically, the noise of the average of n retrieval results is expected to be \(\frac{1}{\sqrt{\text{n}}}\) times the noise of a single retrieval result, which weakens the advantage of our retrieval results in signal-to-noise ratio.  
At lower spatiotemporal resolutions, the limitations of our retrievals in scenarios with very low SIF intensities become more apparent. This is due to the increased variability in satellite spectra introduced by the inclusion of O\({}_{2}\) absorption bands, which slightly weakens the forward model's ability to explain the TOA SIF spectra, potentially leading to a slight increase in noise levels in the retrieval results. Given that the nonlinear transformation NDVI has a higher weight in low-vegetation areas, the difference in GOF between the two SIF products and NDVI becomes more pronounced at lower spatiotemporal resolutions.  
GOF can be easily affected by outliers and may not comprehensively reflect the relationship between SIF products, GPP, and VI. Therefore, we present intensity-frequency distribution images of the original retrieval results for both SIF products, as well as for GPP and EVI in Figure 14. The two SIF products show good consistency in terms of intensity range, indicating that the differences in grid-based products are only related to the intensity-frequency distribution of single retrieval results. Our retrieval results have a noticeably higher proportion in high-SIF areas, which aligns more closely with the intensity-frequency distribution images of EVI and GPP. This further demonstrates the enhanced sensitivity of our data-driven method.  
Figure 14: The intensity distribution of the existing daily average SIF (**a**) and the proposed daily average SIF (**b**) is presented for the entire year of 2019 under 10.5 Km \(\times\) 10.5 Km spatial resolution. Additionally, the intensity distribution of monthly MODIS Enhanced Vegetation Index (EVI) (**c**) under 0.5\({}^{\circ}\) grid cells and annual GPP (**d**) under 500 m SIN grid is displayed for the entire year of 2019.  
The intensity-frequency curve of the Caltech SIF product is steep, making it prone to averaging SIF retrieval results of different intensity levels when generating composite products at certain spatiotemporal resolutions. This leads to a significant reduction in the intensity range and contrast of the composite SIF product. This explains the significant differences in intensity range between the two SIF products shown in Figures 8-10, as well as the better differentiation of our retrieval results for different vegetation communities.