## 5 Conclusions  
Accurate estimation of the canopy fluorescence escaping probability is important for SIF application, but the current algorithms cannot well deal with the influence of soil reflectance, especially for sparse vegetation. In this work, a correction factor estimated using the GPR algorithm with soil reflectance and NDVI was introduced into the widely used NIRv/FAPAR model for better estimation of \(\mathrm{f_{esc}}\) in the near-infrared band. The new method we proposed, the \(\mathrm{f_{esc\_GPR-SR}}\) model, was evaluated using simulation data and ground-measured data. The validation results of two simulation datasets from the SCOPE model and the DART model demonstrate that the performance of the \(\mathrm{f_{esc\_GPR-SR}}\) model in estimating \(\mathrm{f_{esc}}\) is significantly better than that of the NIRv/FAPAR model, particularly for sparse vegetation, and our \(\mathrm{f_{esc\_GPR-SR}}\) model can also effectively eliminate the influence of direction effects. Moreover, the validation results using the in situ measured data also prove that, compared with the NIRv/FAPAR model, our \(\mathrm{f_{esc\_GPR-SR}}\) model can better reduce the species dependence of \(\mathrm{APAP_{green}}\)-SIF and eliminate the effect of canopy structure difference in multiple species. This study highlights the significance and advantages of considering soil reflectance in \(\mathrm{f_{esc}}\) modeling and presents a more accurate \(\mathrm{f_{esc}}\) estimation model, which will be useful for further studies on the SIF-GPP relationship.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15184361/s1](https://www.mdpi.com/article/10.3390/rs15184361/s1)]([https://www.mdpi.com/article/10.3390/rs15184361/s1](https://www.mdpi.com/article/10.3390/rs15184361/s1)), Figure S1: Soil spectra called \"loam_gravly_brown_dark\" used in the DART model (from the DART model database \"Lambertian.db\"); Figure S2: (a) Nadir and (b, c) side view of the 3-D canopy of maize simulated using the DART model. The size of the scene is 1.5 m \(\times\) 1 m, and 20 maize are planted in two rows, with 10 in each row. Figure S3: Training performance (Predicted vs. Actual) of machine learning algorithms based on 5-fold cross-validation method when the input parameters are soil reflectance at 780 nm and NDVI; Figure S4: Comparison of \(\mathrm{f_{esc}}\) in the near-infrared band (760 nm) estimated by the NIRvH2/FAPAR model with the \(\mathrm{f_{esc}}\) simulated by the SCOPE model. R\({}^{2}\) is the correlation coefficient of linear regression, and RMSE is the root mean square error between \(\mathrm{f_{esc}}\) (SCOPE) and the \(\mathrm{f_{esc}}\) values calculated by the NIRvH2/FAPAR model; Table S1: The main parameters of the GPR model when using MATLAB to fit Gaussian process regression.  
**Author Contributions:** Conceptualization, X.L. and L.L.; methodology, M.Q. and X.L.; software, M.Q.; formal analysis, M.Q.; investigation, M.Q.; resources, X.L., S.D. and L.L.; data curation, M.Q. and S.D.; writing--original draft preparation, M.Q.; writing--review and editing, X.L., L.L., L.G. and R.C.; visualization, M.Q.; funding acquisition, X.L. and L.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China, grant number 2022 YFF1301900, and the National Natural Science Foundation of China, grant number 42071310.  
**Data Availability Statement:** Data will be made available upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.