## 4 Discussion
### Influence of Remotely Sensed \(i_{o}\) and fAPAR on the Estimation SIF Escape Probability  
In general, remotely sensed canopy interceptance (Figure 6a) led to a relatively accurate SIF escape probability compared to the simulated canopy interceptance (i.e.,'real' value, see Figure 4b). Interestingly, the use of remotely sensed canopy interceptance slightly improved the estimation accuracies for configurations with the natural background spectrum. This phenomenon was explained by the joint influences of canopy interceptance and canopy reflectance on the estimation of SIF escape probability, which we term the contamination compensation effect. Firstly, SIF escape probability was generally overestimated by the use of canopy reflectance (\(NIR_{v}\) or \(NIR_{T}\)), because canopy reflectance is contaminated by the reflectance of the background (see Figure 4). Then, the use of overestimatedcanopy interceptance (Figure S3a-c) partly reduces the estimation errors caused by the contaminated canopy reflectance, especially for the sparse forest canopies. On the contrary, for these configurations with a non-reflecting background spectrum, because there is no more contamination caused by the background reflectance, the estimation accuracy of SIF escape probability was mostly reduced (Figure 6a). However, it should be noted that the above analysis was based on the assumption that \(G(\theta)\) was fixed to 0.5, as for global-scale remote sensing applications [13; 14; 25; 33], and LAI and CI were set to simulation values (i.e.,'real' value). The introduction of artificial constant value errors on LAI*CI has a remarkable influence on the estimation accuracy of SIF escape probability (and total emitted SIF), with a clear overestimate of SIF escape probabilities if LAI*CI is underestimated (Figure 6b). To better explain this trend, canopy interceptance was plotted using LAI*CI with corresponding artificial errors (Figure S3a-c). It demonstrated that an underestimated LAI*CI implies an underestimated canopy interceptance, which amplifies the overestimation induced by the contaminated canopy reflectance. An overestimated LAI*CI tends to produce an overestimated canopy interceptance, which is somewhat compensated by the contaminated canopy reflectance. The errors of satellite-retrieved LAI and CI products, including theoretical and physical errors, are inevitable [42]. These will be more significant for forest biomes where the accurate estimation of LAI remains challenging with an uncertainty relatively higher than for other biomes [43; 44; 45]. In a recent study, [PERSON] et al. [14] demonstrated that total emitted SIF, estimated using satellite-retrieved LAI and CI products, was better correlated with GPP for all C3 sites combined, but was less correlated for forest sites. The above phenomenon could be partly attributed to the high error and uncertainty of satellite-retrieved LAI and CI products. Our result (see Figure 7) also demonstrated the high uncertainty of estimation of canopy interceptance in the forest using LAI and CI, especially for underestimated LAI*CI. Furthermore, \(G(\theta)\) was fixed to 0.5 for all biomes, which can also be a potential source of error because it only holds for homogeneous canopy with spherical leaf orientation distribution [25; 33]. Overall, the estimation of canopy interceptance, and the SIF escape probability or total emitted SIF, using satellite-retrieved LAI and CI products is still questionable and needs further investigation. Owing to the high uncertainty of estimated canopy interceptance using satellite-retrieved LAI and CI products, [PERSON] et al. [12] proposed to use fAPAR to approximate canopy interceptance instead of canopy interceptance. Our simulation results support this approach since it leads to comparable and even better estimation of SIF escape probability than the method using canopy interceptance (Figure 3).