## 4 Discussion  
### Comparison of Predicted and Actual Error  
To evaluate the validity of the error assessment in this study, we estimated the SSR inversion error based on the actual geometric parameters for the ASTER multi-angle SG remote-sensing images obtained using our model and compared them with the actual SSR inversion error reported in [PERSON] et al. (2018).  
[PERSON] et al. (2018) applied ASTER remote-sensing imagery to the SSR estimation model based on multi-angle SG and then inverted the sea surface wind speed based on the estimated SSR. Finally, certain buoy wind speed data from the National Data Buoy Center (NDBC) and inversion wind speed data were selected for comparative analysis. The selected buoy and center positions of the remote-sensing image were within 30 km. The time difference of the buoy data within half an hour was matched and a total of 6 sets of matching data were obtained. A set of actual errors between the estimated and actual wind speeds was obtained through comparisons.  
Using the proposed error analysis simulation model, we were able to predict, in advance, the SSR error estimated for the six ASTER remote-sensing images. The relationship between SSR and wind speed (Equation (11)) can be used to convert the estimated SSR error into sea surface wind speed error. First, according to the characteristics of the ASTER sensor, the error (\(\Delta I\)) of the simulated SG radiance is input into the simulation model. Currently, we consider that \(\Delta I\) consists of two parts: the inherent sensor error and the radiation correction error. We set \(\Delta I=I*5\%+0.0005\) as described in the error transfer simulation model above. The predicted error was compared with the actual generated error, whose comparison result is shown in Figure 15.  
Figure 14: Statistical results of \(\rho_{10}\) and \(\rho_{20}\) at different sun zenith angles (\(\theta_{0}\)).  
The predicted and actual errors were found to have a positive correlation (correlation coefficient = 0.85) but there is a large absolute deviation between them. This large absolute deviation is possibly due to the smaller simulated SG radiance error (\(\Delta I\)) input into the simulation model compared with the true SG radiance error, which results in a small prediction error. On the other hand, [PERSON] et al. [20] only used a simple atmospheric correction to process the ASTER images. They considered effects from Rayleigh (Lr) and aerosol scattering (La) but ignored both whitecap reflection (Lwc) and water-leaving radiance (Lw). Rayleigh scattering correction was performed using the single scattering model developed by [PERSON] [24] and both the aerosol optical thickness and scattering phase functions in the aerosol scatter correction were determined using empirical parameter methods. Based on [PERSON] et al. [24], [PERSON] et al. [20] considered that their model had only a 10% error for the calculation of a single Rayleigh scattering. Atmospheric correction of ASTER remote-sensing images is a complex but non-negligible process. Oversimplified atmospheric corrections may lead to large errors in the inversion of sea surface and actual wind speeds. Therefore, the actual error is much larger than the prediction error. To further improve the estimation accuracy of the simulation model, we analyzed \(\Delta I\) at different conditions.