The accurate classification of marsh vegetation is an important prerequisite for wetland management and protection. In this study, the Honghe National Nature Reserve was used as the research area. The VV and VH polarized backscattering coefficients of Sentinel-1B, the polarimetric decomposition parameters of Sentinel-1B, and Sentinel-2A multi-spectral images from June and September were selected to construct 18 multi-dimensional data sets. A highly correlated variable elimination algorithm, a recursive feature elimination variable selection algorithm (RFE-RF), and an optimized random forest algorithm (RF) were used to construct a marsh vegetation identification model. In this study, we searched for an RF model to achieve the accurate classification of marsh vegetation and find the best feature for identifying various types of vegetation. Additionally, the applicability of different optimized RF models to the task of the identification of wetland vegetation and the stability of the identification of marsh vegetation using different classification models were quantitatively analyzed. The results show the following: (1) RFE-RF variable selection and RF parameter optimization can reduce the data dimensionality, improve the accuracy and stability of the wetland vegetation classification model, and achieve a training accuracy of up to 85.39%. (2) The RF model integrating multi-spectral data, backscattering coefficients, and polarimetric decomposition parameters for June and September can obtain the highest overall accuracy (91.16%), and the model has the strongest applicability. (3) The importance of multi-spectral variables in wetland vegetation classification is higher than that of backscattering coefficients and polarimetric decomposition parameters. The visible bands and vegetation index are the most important variables, while the cross-polarized backscattering coefficient (Mean_VH), polarimetric decomposition eigenvalue (Mean_l1, Mean_l2), and calculated eigenvalues of the matrix (Mean_lambda) are the backscattering coefficient features and polarimetric decomposition parameters with the highest contributions. (4) The modified normalized difference water index in June (MNDWI_ Jun), blue band in September (Mean_B_Sep), location feature pixel coordinates (Y_Max_Pxl), and ratio vegetation index in September (RVI_Sep) have the highest contribution to the identification and classification of deep-water marsh vegetation, shallow-water marsh vegetation, forest, and shrubs, respectively. (5) The identification of forest is the strongest, and the classification accuracy for shrubs and deep-water marsh vegetation is greatly affected by the combination of time phase and data sources.

沼泽植被的准确分类是湿地管理和保护的重要前提。本研究以红河国家级自然保护区为研究区域。选取Sentinel-1B的VV和VH偏振后向散射系数、Sentinel-1B的偏振分解参数和Sentinel-2A 6月和9月的多光谱图像构建18个多维数据集。采用高度相关的变量消除算法、递归特征消除变量选择算法（RFE-RF）和优化的随机森林算法（RF）构建沼泽植被识别模型。在这项研究中，我们搜索了一个 RF 模型来实现对沼泽植被的准确分类，并找到识别各种植被类型的最佳特征。此外，定量分析了不同优化RF模型在湿地植被识别任务中的适用性，以及不同分类模型对沼泽植被识别的稳定性。结果表明：（1）RFE-RF变量选择和RF参数优化可以降低数据维数，提高湿地植被分类模型的精度和稳定性，训练精度高达85.39%。(2) 综合6月和9月的多光谱数据、后向散射系数和极化分解参数的RF模型可以获得最高的综合精度(91.16%)，模型的适用性最强。(3)多光谱变量在湿地植被分类中的重要性高于后向散射系数和极化分解参数。可见波段和植被指数是最重要的变量，而交叉极化后向散射系数（Mean_VH）、极化分解特征值（Mean_l1、Mean_l2）和矩阵的计算特征值（Mean_lambda）是后向散射系数特征和极化分解参数贡献最大。(4) 6月修正归一化差异水指数(MNDWI_Jun)、9月蓝带(Mean_B_Sep)、位置特征像素坐标(Y_Max_Pxl)、9月植被比值指数(RVI_Sep)对识别分类贡献最大深水沼泽植被，分别是浅水沼泽植被、森林和灌木。(5)森林识别能力最强，灌木和深水沼泽植被的分类精度受时间相位和数据源组合影响较大。