## 3 Results  
The classification results derived from the ML, MD, and SVM methods for the GF-3, OHS, and synergetic data sets in the YRD are presented in Figure 8. First, a larger amount of noise deteriorates the quality of GF-3 classification results, and many pixels belonging to the river are misclassified as saltwater (Figure 8a,d,g), indicating that the GF-3 fails to separate different water bodies (e.g., river and saltwater). Second, the OHS classification results (Figure 8b,e,h) are more consistent with the actual distribution of wetland types, proving the spectral superiority of OHS. However, there are many river noises in the sea that are probably attributed to the high sediment concentrations in shallow sea areas (see Figure 1). Third, the complete classification results generated by the synergetic classification are clearer than those of GF-3 and OHS data separately (Figure 8c,f,i). Similarly, some unreasonable distributions of wetland classes in the OHS classification also exist in the synergetic classification results, which reduces the classification performance. For example, river pixels appear in the saltwater, and _Suueda salsa_ and tidal flat exhibit unreasonable mixing. Overall, the ML and SVM methods can produce a more accurate full classification that is closer to the real distribution. The accuracy results obtained by different classification methods for different data sets are shown in Tables 4-7 and Figures 9-11. The OAs obtained by the ML, MD, and SVM methods for the GF-3 data are 52.5%, 55.7%, and 80.7%, and the Kappa coefficients are 0.36, 0.41, and 0.70, respectively. The above classification accuracy is the lowest of all classification processes, possibly due to common wetland structural conditions. In contrast, the OAs with the ML, MD, and SVM methods for the OHS data are 96.7%, 87.6%, and 95.6%, and the Kappa coefficients are 0.95, 0.82, and 0.94, respectively. This may be attributed to the increased spectral separation capacity of the biochemical characteristics of wetland types. Subsequently, the classification accuracy after data fusion is improved by approximately 30% compared with the GF-3 data alone. This is mainly due to the consideration of the biophysical and biochemical changes that occur with the change in the phenology of wetland types. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accuracy Metric** & **Data** & **ML** & **MD** & **SVM** \\ \hline \multirow{3}{*}{Overall Accuracy} & GF-3 & 52.5\% & 55.7\% & 80.7\% \\  & OHS & 96.7\% & 87.6\% & 95.9\% \\  & GF-3+OHS & 97.3\% & 89.0\% & 97.2\% \\ \hline \multirow{3}{*}{Kappa Coefficient} & GF-3 & 0.36 & 0.41 & 0.70 \\  & OHS & 0.95 & 0.82 & 0.94 \\ \cline{1-1}  & GF-3+OHS & 0.96 & 0.84 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy assessment results obtained by ML, MD, and SVM methods for GF-3, OHS, and synergetic data sets. Figure 8: Classification results obtained by ML, MD, and SVM methods for GF-3, OHS, and synergetic data sets in the YRD. (**a**) GF-3 ML, (**b**) OHS ML, (**c**) GF-3 and OHS ML, (**d**) GF-3 MD, (**e**) OHS MD, (**f**) GF-3 and OHS MD, (**g**) GF-3 SVM, (**h**) OHS SVM, (**i**) GF-3 and OHS SVM. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Wetland** & **Saltwater** & **Farmland** & **River** & **Shrub** & **Grass** & **Suaeda salsa** & **Tidal Flat** \\ \hline GF-3 ML & 91.46 & 56.07 & 4.58 & 48.93 & 58.37 & 0.96 & 38.02 \\ GF-3 MD & 92.47 & 57.2 & 16.67 & 48.47 & 40.98 & 0.44 & 43.92 \\ GF-3 SVM & 88.2 & 58.4 & 5.18 & 55.14 & 60.49 & 0 & 87.93 \\ \hline OHS ML & 99.98 & 84.82 & 86.25 & 91.26 & 96.03 & 55.38 & 99.93 \\ OHS MD & 100 & 89.6 & 47.06 & 79.48 & 93.32 & 1.4 & 98.5 \\ OHS SVM & 99.82 & 82.45 & 84.18 & 98.66 & 96.46 & 16.53 & 99.81 \\ \hline GF-3+OHS ML & 99.94 & 89.06 & 88.52 & 89.76 & 94.88 & 72.65 & 99.5 \\ GF-3+OHS MD & 99.97 & 95.78 & 46.71 & 63.78 & 70.19 & 2.63 & 97.82 \\ GF-3+OHS SVM & 99.73 & 90.41 & 85.9 & 100 & 92.15 & 24.46 & 99.83 \\ \hline \end{tabular}
\end{table}