## 4 Results and Discussion  
### Validation of the Sea Surface Emission Models in the ECS  
It is known that the prediction discrepancies of the different sea surface emission models (especially, TSM model and empirical model of [25]) are quite significant for _SSS_ retrieval. For the TSM model, the choice of the cutoff wavenumber and the factor a0 can affect the prediction of brightness temperature significantly. The a0 value is always assumed to vary from 0.004 to 0.008, and in some studies, the DV spectrum is multiplied with a factor of 2 [16; 32; 44]. In this paper, the TSM model with four typical combinations of factors was used to predict the brightness temperature and compare with the in-situ measured data, and the choices of the factor and a0 values are shown in Table 2. The influence of different factor and a0 values on the predicted brightness temperature is shown in Figure 5, in which the _SST_ and _SSS_ were fixed as 15 \({}^{\circ}\)C and 35 psu, and the cutoff wavenumber was set as \(k_{c}=k_{0}/3\). Clearly, the predicted brightness temperature obtained from the model M1 (DV spectrum multiplied with the factor 2, hereafter DV2) is higher than those from the models M2, M3 and M4, especially for the horizontal polarization component. Comparing to vertical polarization, the horizontal polarization is sensitive to the wave spectrum, and the differences among the models M1, M2, M3 and M4 can reach up to 2 K at nadir, and increase to 10 K with an incidence angle at 60\({}^{\circ}\), which is too large for _SSS_ retrieval. The differences between the models M1, M2, M3 and M4 are less than 2 K for vertical polarization, which is reasonable because the vertical polarized brightness temperature is less influenced by the sea surface roughness effect. Although the differences are small for vertical polarization, it is still too large for _SSS_ retrieval. Thus, these factors need to be determined by fitting the model predictions to the in-situ measurements. On the other hand, due to the limited brightness temperature measurements in coastal waters of China, the regional bias of the empirical model in the ECS is still unclear and needs to be examined.  
Table 3 presents the results for different sea surface emission models obtained by comparing the model-predicted and in situ brightness temperatures. The M4 model had the largest root-mean-square difference (rms; up to 3 K) for horizontally polarized brightness temperature, which could lead to 3 psu or a higher bias in _SSS_ retrieval. Although the wave spectrum was multiplied with a factor of 2 to fit with the measured brightness temperature at higher frequencies [18], as done previously [16; 32], the DV2 spectrum would overestimate the brightness temperature values. The rms values were the lowest for horizontally polarized brightness temperature with the M2 model and for vertically polarized brightness temperature with the M3 model. According to the mean biases, the M2 model slightly underestimated the brightness temperature, whereas the M3 model overestimated the brightness temperature. The underestimation or overestimation of the brightness temperature by these models might be attributed to the choice of cutoff wavenumber. Thus, the cutoff wavenumber needs to be considered in the model (as discussed in a later section). Furthermore, the empirical models developed based on the WISE campaigns show the regional biases in the ECS, and the coefficients in these models also need to be optimized.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Factor** & \(\mathbf{a}_{0}\) \\ \hline M1 & 2 & 0.008 \\ M2 & 1 & 0.008 \\ M3 & 1 & 0.006 \\ M4 & 1 & 0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the two scale model (TSM).  
Figure 5: Predicted brightness temperature (TB) obtained from the TSM model with different parameters.