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In this work, fully polarized SAR (PolSAR) data are exploited to characterize halophyte plants in lakeside saline wetland environments thanks to their different scattering properties. To this aim, several polarization signatures and morphological profiles (MPs) are used as inputs to the proposed "random MS model forest" (RM5MF) and "classification via random forest regression" (CVRFR) classifiers. The experimental results show that parameters such as pedestal height (PH), as well as 3D co-polarization and cross-polarization signature plots, are more suited than biomass index (BMI), volume scattering index (VSI), and canopy scattering index (CSI) to map halophytic plants in arid environments. When we compare the suitability of PolSAR features using RM5MF, random forest (RaF), and other five popular attribute selection approaches, all the results uniformly show that span, MPs and entropy are the most valuable features, while PH and BMI are more valuable than CSI, VSI and the radar forest degradation index (RFDI). Additionally, the diagonal elements of the coherency matrix are more valuable than are the off-diagonal elements, and double-bounce, odd-bounce and wire elements are more valuable than helix bounce and volume bounce. The study results are obtained from Po1SAR L-band quad-polarimetric high-sensitivity stripmap data over two study regions by comparing RM5MF and CVRFR with more traditional algorithms (support vector machine (SVM), RaF, rotation forest (RoF), and MultiBoostAB). The RMSMF model achieves the highest accuracy value in the study regions. However, due to the binary splitting criteria in the M5 model tree, it is more computationally intensive than all the others. In contrast, the CVRFR model consumes much less time-approximately 10 times less than RMSMF, and 5 times less than RoF-but still achieves better (3%-8%) classification accuracy than SVM or RoF, and its results are comparable (less than 1% difference) to those by RaF.