## 7 Discussion and Conclusions
Most decomposition methods for estimating various scattering components rely on quad-pol SAR observations [28, 46]. Although dual-pol observations have more information than single-pol SAR images, the ability to distinguish between the different vegetation types is somewhat limited. The only operational polarimetric SAR satellite system currently is the C-band Radarsat-2, but its polarimetric sensitivity over tropical and subtropical wetlands is not better than that of the X-band wavelength data [37, 68]. It is impressive that the short wavelength of X-band TSX decomposition products yielded very good sensitivity for vegetation characterization, particularly the detail with which the mangroves along the tidal channels were mapped. The high sensitivity indicates that the scattering behaviors around the mangroves in SAR observations can be very helpful in discriminating mangroves from other vegetation types. We interpret that the high sensitivity of the X-band TSX data were very suitable for the characteristics of vegetation cover in Everglades wetlands. In upland vegetated areas, longer wavelength radar observations have proven more useful for classifying the vegetation because of increased canopy penetration depth [69, 70, 71]. Thus, a mix of polarimetric SAR systems could provide stronger capabilities for mapping a greater variety of vegetation types. We observed that our decomposition method can provide better classification accuracy (93.0%) than [PERSON]'s decomposition approach (84.5%), which is based on rotated coherency matrix [32]. Both of the classification results revealed similar patterns, as shown in Figure 8c,f, but [PERSON]'s method (Scenario 5) resulted in a relatively overestimated water class. We can also detect a greater portion of the scrub class in Scenario 5, compared with Scenario 3. The higher probability to be classified as prairie in region A of Figure 8d can be explained by more dominant surface and volume scattering components of [PERSON]'s method. The better classification accuracy using H&W decomposition may result from the characteristics in which the wetland environment was considered [36]. The H&W decomposition has limitation in that it produces more negative single scattering component compared other decomposition methods. However, it has an advantage to provide more double bounce scattering component from both co- and cross-pol SAR observations at the wetland environment. These characteristics might be useful to discriminate tall mangrove forest or scrub from other herbaceous vegetation such as prairie. We will further investigate the performance of our decomposition method for other study areas comparing other decomposition results. Our classification results showed that a SAR feature-based approach offers good potential for vegetation mapping, even though multispectral and hyperspectral remotely sensed images have been widely utilized to map wetland vegetation [72; 73; 74; 75]. We achieved a mapping accuracy of more than 85% when only SAR features were used. However, accuracy of more than 90% was achieved when SAR features were used for vegetation classification following the application of multispectral bands to develop vegetation object boundaries (image segmentation). Where both multispectral and SAR data are available, they can be used in combination to improve vegetation mapping, but where persistent cloud cover limits the availability of multispectral data, as is often the case in tropical and subtropical wetland environments, the high accuracy level we attained with only SAR data demonstrates the value of SAR systems for mapping these globally important resources. The authors would like to thank the German Aerospace Center for access to the TSX data. This study was supported by KOPRI project of PE15040. This work was also funded through NASA Cooperative Agreement No. NNX08 BA43A (WaterSCAPES: Science of Coupled Aquatic Processes in Ecosystems from Space) grants.