## 1 Introduction  
Tropical and subtropical wetlands are among the most productive ecosystems on Earth, providing numerous ecosystems services, including critical habitat for a variety of fauna and flora, energy and nutrients for coral reefs, and protection of near-shore areas from natural disasters such as storm surge or tsunami [1,2]. Tropical and subtropical wetlands include both inland freshwater and coastal saltwater wetland types. The Everglades, which is a World Heritage Site, International Biosphere Reserve, and a Wetland of International Importance, is the largest natural region of subtropical wilderness in the United States. Over the past century, the Everglades wetlands have been threatened by severe environmental stresses induced by climate change, human population growth, urban expansion, and agricultural and other land conversion. With the recognition of its global importance, various restoration plans have been authorized to protect the Everglades. Protecting the wetlands requires detailed assessments of their vegetation distribution in terms of vegetation types and vegetation changes over time. Previous vegetation classifications of the Everglades were mainly conducted using airborne- and space-based images. The conventional approaches of visual interpretation techniques with aerial photographs and optical satellite images were adopted to generate detailed vegetation maps [3,4]. A related technique was the use of stereo-plotters with color infrared aerial photography to classify the vegetation in Water Conservation Area 2A (WCA-2A) of the Everglades [5]. Hyperspectral imagery has been regarded as a powerful tool for vegetation mapping due to its fine spectral resolution. Data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) have been used to map vegetation over the Everglades using spectral angle mapper and neural network classifiers [6,7]. [PERSON] [8] suggested that IKONOS imagery was likely to be useful for detecting large, dense stands of invasive trees in Everglades National Park (ENP). It is important to note that most of these approaches have relied on airborne-based optical imagery, which is costly, time-consuming, and limited by the weather condition. Remotely sensed Synthetic Aperture Radar (SAR) observations have been widely used for monitoring tropical and subtropical wetlands because they can collect images through clouds, rain or fog. In addition, SAR images are sensitive to biomass and flooded vegetated structures [9,10]. Wetland interferometric SAR (InSAR) techniques, which can measure water-level changes with high spatial resolution in the aquatic environments with emergent vegetation, have been used to detect surface flow patterns over wetland areas [11, 12, 13, 14]. Interferometric coherence, phase and amplitude were used by [PERSON] _et al._[15] in conjunction with a coastal land classification map to study changes in intensity of vegetation returns over a season. Several studies have reported on successful wetland vegetation mapping of various wetland areas [16, 17, 18, 19, 20]. However, because most of these classification studies relied on multi-temporal single polarization radar observations, they showed a limited capability to discriminate vegetated wetlands compared with other results from multi-spectral images. As an alternative approach, data fusion with multi-sensor and multi-temporal optical and SAR data has been reported to improve wetland characterization [21]. As multi-polarimetric SAR observation systems have been developed, more backscattering coefficient information over wetlands could be used for vegetation classification mapping. As indicated in previous studies, more abundant backscattering information of multi-polarimetric SAR observations becomes helpful for more accurate classification of vegetated wetlands [22, 23, 24, 25, 26]. Polarimetric SAR (PolSAR) decompositions are useful for discriminating and mapping the Earth's surfaces according to scattering behaviors [27, 28].