## 3 Results
### Verification of the Accuracy and Reliability of the 1 km SIF  
The 2018-2019 TanSIF and 2018-2020 GOSIF time scales were aggregated, and to maintain the spatial and temporal consistency of the SIF data, the downscaled 1 km SIF products were resampled to 0.05\({}^{\circ}\) for correlation validation analysis, to ensure the accuracy of the 1 km SIF. Figure 13 presents the density plots of the validated correlation, where (a) and (b) show the fit between the downscaled SIF with TanSIF/GOSIF for the first year,  
Figure 11: Validation at the 0.05\({}^{\circ}\) spatial scale from October 2018 to May 2019.  
Figure 12: Validation at the 0.05\({}^{\circ}\) spatial scale from October 2019 to May 2020.  
respectively, while (c) shows the fit between the downscaled SIF and GOSIF for the second year. The upper part and the right sections of the plot represent the data trends of the downscaled 1 km SIF and other SIF satellite products, respectively. The results show that the R\({}^{2}\) values exceed 0.8, with RMSE and MAE both below 0.1. Figure 13 shows that the downscaled 1 km SIF products are consistent with TanSIF and GOSIF products in data trends, indicating that the downscaled SIF products have good consistency with TanSIF and GOSIF products.  
GPP (ecosystem primary productivity) is one of the key parameters of terrestrial ecosystems, which directly reflects the efficiency of vegetation in utilizing light energy. [PERSON] et al. highlighted that total primary productivity is a major component of carbon flux in the Earth's ecosystems [64]. [PERSON] et al. were the first to explore the coupling between satellite-observed SIF data and the GPP, and subsequent studies have shown a good linear relationship with GPP from SIF data obtained from ground-based observations, tower-based, and satellite platforms [65]. GPP can be expressed as the product of photo-synthetically active radiation absorbed by the canopy (APAR) and the true light energy use efficiency (LUEp) by the light energy utilization rate model, namely  
\[GPP=APAR\times LUE_{P}=PAR\times fPAR\times LUE_{\text{pmax}}\times f(stress) \tag{5}\]  
In addition, LUE\({}_{\text{pmax}}\) is the maximum light energy utilization, and f(stress) is the environmental limiting factor. The linear relationship between SIF and GPP can be obtained by means of the linear relationship between SIF and GPP in Equation (6) by association with Equation (5). The equation can be expressed as  
\[GPP=SIF\times\frac{LUE_{p}}{\Phi_{SIF}}\times\frac{1}{\varepsilon}=SIF\times \frac{LUE_{\text{p}}}{SIF_{yield}} \tag{6}\]  
A linear relationship between SIF and GPP can be seen from Equation (6). To verify the reliability of the downscaled SIF, the coupling relationship of SIF-GPP is examined. For this purpose, the GOSIF GPP dataset, estimated based on GOSIF products, was selected for this experiment. This dataset utilizes eight different forms of SIF-GPP relationships to estimate the GPP at 91 eddy covariance flux sites worldwide, and the ensemble-averaged eight-day GPP shows a strong correlation with the flux tower GPP. The dataset has a temporal resolution of 8 days and a spatial resolution of 0.05\({}^{\circ}\), providing a continuous dataset. In this experiment, two years of downscaled 1 km SIF products were synthesized and resampled to match the spatial resolution of the GOSIF GPP. The GPP data were also synthesized into one year for consistency. The results obtained by correlation analysis are  
Figure 13: Density fitting map of 1 km SIF and other SIF satellite products verified. (a) 2018–2019 TanSIF. (b) 2018–2019 GOSIF. (c) 2019–2020 GOSIF.  
shown in Figure 14. Figure 14a,b show the fitted plots of SIF versus GPP for the first and second years, respectively. It can be observed that the two-year SIF and GPP show a strong correlation and the trend distribution above and to the right of the scatter plot is the same, which verifies the reliability of the downscaled 1 km SIF product.