## 3 Results
### Evaluation of the \(f_{\text{esc\_GPR-SR}}\) Model Using Field-Measured Data  
The \(\Phi_{\text{SIF}}\) in Equation (1) remains relatively unchanged under high-light and stress-free conditions [49; 50]. As a result, there is a strong correlation between APAR\({}_{\text{green}}\) and SIF\({}_{\text{total}}\), which is stronger than at the canopy level, taking into account canopy scattering and reabsorption [15; 26]. We use the field-measured data from healthy and non-stressed vegetation to study the correlation of APAR\({}_{\text{green}}\)-SIF at both canopy and leaf levels to assess the performance of both downscaling methods.  
To verify the improved performance of the model that accounts for soil reflectance in SIF downscaling, the samples with FVC \(\leq\) 0.8 from the ground-measured dataset were selected for comparison and validation. Figure 10 reveals the correlation between APAR\({}_{\text{green}}\) and the canopy-level SIF, the leaf-level SIF calculated by the NIRv/FAPAR model, and the f\({}_{\text{esc\_GPR-SR}}\) model for various species. In comparison with the APAR\({}_{\text{green}}\)-SIF\({}_{\text{canopy}}\) relationship, the R\({}^{2}\) of the correlation between SIF after downscaling using both models and APAR\({}_{\text{green}}\) increases significantly and the slopes of the linear regression lines of APAR\({}_{\text{green}}\)-SIF\({}_{\text{leaf}}\) for various species become more similar, indicating that downscaling SIF from canopy level to leaf level eliminates the impact of varying canopy structures among species and reduces the species dependence of the APAR\({}_{\text{green}}\)-SIF correlation. Our f\({}_{\text{esc\_GPR-SR}}\) model performs better in this aspect, as reflected by the fact that the R\({}^{2}\) of the relationship between APAR\({}_{\text{green}}\) and SIF\({}_{\text{leaf}}\) estimated by the f\({}_{\text{esc\_GPR-SR}}\) model improves from 0.921 to 0.937 and the RMSE declines from 0.904 to 0.656 mW/m\({}^{2}\)/nm in comparison with the results of the NIRv/FAPAR model.  
The R\({}^{2}\) and RMSE values of the linear correlation between APAR\({}_{\text{green}}\) and SIF for samples of various species with FVC \(\leq\) 0.8 appear in Table 6. The results indicate that the R\({}^{2}\) values of the APAR\({}_{\text{green}}\)-SIF\({}_{\text{leaf}}\) relationship for different species increase after the SIF downscaling. Compared with the NIRv/FAPAR model, the RMSE of SIF\({}_{\text{leaf}}\) for different species calculated by our f\({}_{\text{esc\_GPR-SR}}\) model is smaller, indicating that the f\({}_{\text{esc\_GPR-SR}}\) model can better estimate the leaf-level SIF. For vegetables and crops, although the R\({}^{2}\) value slightly decreases after accounting for soil reflectance, the RMSE is still smaller than that of the NIRv/FAPAR model. For gold coin grass and winter wheat, the SIF\({}_{\text{leaf}}\) calculated by the f\({}_{\text{esc\_GPR-SR}}\) model displays a better linear correlation with APAR\({}_{\text{green}}\).  
Figure 9: Comparison of \(f_{\text{esc}}\) in the near-infrared band (760 nm) simulated by the DART model (f\({}_{\text{esc}}\) (DART)) with that estimated by the NIRv/FAPAR model (f\({}_{\text{esc}}\) (NIRv/FAPAR)) and the f\({}_{\text{esc\_GPR-SR}}\) model (f\({}_{\text{esc}}\) (GPR-SR)). R\({}^{2}\) is the correlation coefficient of linear regression, and RMSE is the root mean square error between f\({}_{\text{esc}}\) (DART) and the f\({}_{\text{esc}}\) values calculated by f\({}_{\text{esc}}\) estimation models.  
Overall, the outcomes indicate that the \(\text{f}_{\text{esc\_GPR-SR}}\) model shows better performance in SIF downscaling for different species, reducing the species dependence of \(\text{APAR}_{\text{green}}\)-SIF for sparse vegetation to a greater extent in contrast to the NIRv/PAPR model.