背景：植被是湿地的核心，易受人类活动和气候变化的影响，湿地植物群落分类与制图可以为湿地生态监测与评估提供科学数据支撑。方法：本研究以鄱阳湖国家级湿地自然保护区为研究区，基于2019年月度Sentinel-1和Sentinel-2时序数据，通过提取影像的水体指数和植被指数、红边指数、纹理特征、光谱特征、雷达极化数据5类，共计240个特征指标，使用随机森林、支持向量机和深度神经网络算法进行分类，探寻一套湿地植被分类最优的特征组合和分类方案。结果：（1）光学数据在湿地分类与制图提取中明显优于雷达数据，雷达数据可以在光学数据不足时，作为光学数据的补充.(2)对时序Sentinel-2的各特征变量进行重要性筛选，有助于提高分类精度，优选时间段主要分布在1、5、8、9、10和12月份；（2）当对5组特征变量单独分类时，分类精度排序为：红边指数组>水体-植被指数组>光谱特征组>雷达极化数据组>纹理特征组；（3）对比组合变量和单独特征变量，组合变量不一定有助于提高分类效果，分类精度排序为：红边指数分类组>水体-植被指数分类组>组合分类组，其中，红边指数组随机森林分类总体精度达0.81，Kappa系数为0.76；（4）对比三种分类方法，分类精度排序为：深度神经网络>随机森林>支持向量机，其中，深度学习方法并没有太大幅度的提高分类精度，相对随机森林算法仅仅提高了2%。故深度神经网络和随机森林算法都可以作为优选算法。结论：本研究给出的分类方案是，使用Sentinel-2和Sentinel-1多时序数据对湿地植被进行精细化分类， 时段选择建议1、5、8、9、10和12月份的卫星数据更优，特征变量可选红边指数组或者水体-植被指数组产品，分类方法可根据需求选择深度神经网络或随机森林对湿地植物群落进行分类，可得出较优的分类结果。这个分类方案可以有效的提升鄱阳湖湿地植被制图精度，并为决策部门提供科学的技术方案。

Objective：Plant community plays an important role in wetland elemets and is vulnerable to human activities and climate change. Wetland plant communities classification and mapping provide scientific important data support for wetland ecological monitoring and evaluation. This paper aims to develop a classification scheme suitable for the wetland plant communities in Poyang Lake wetland. Method：Taking Poyang Lake National Nature Reserve as the research area, based on the monthly Sentinel-1 and Sentinel-2 time series data in 2019, this study extracts five types of image feature parameters, including water and vegetation indexes group, red edge indexes group, texture features group, spectral features group and polarization radar backscatters group, with a total of 240 feature indexes, and uses random forest, support vector machine and deep neural networks (DNN) algorithms for classification. To explore a set of optimal features combination and a suitable classification scheme for wetland vegetation mapping in Poyang Lake. Result：The results show that: (1) Compared with radar data, the extraction accuracy of optical data is significantly better than that of radar data in wetland plant communities classification and mapping. . Radar data can be used as a supplement to optical data when optical data is insufficient. (2) Screening the importance of each image features of Sentinel-2 helps to improve the classification accuracy. The preferred time periods are mainly distributed in January, May, August, September, October and December; (3) Five groups of unitary image features are selected to classify separately, the classification accuracy is as follows: red edge indexes group > water and vegetation indexes group > spectral features group > radar polarization data group> texture features group; (4) Comparing the combined image features groups with the unitary image features groups, the combined image features group are not necessarily helpful to improve the classification accuracy. The classification accuracy is as follows: red edge indexes group > water vegetation indexes group > combined image features group. Among them, the overall accuracy of the classification scheme using red edge indexes group and random forest method is 0.81, Kappa coefficient is 0.76; (4) Comparing the three classification algorithms, the classification accuracy is ranked as follows: Deep Neural Networks (DNN) > Random Forest (RF) > support vector machine(SVM). The overall accuracy of deep learning method does not greatly improve, which is only 2% higher than the random forest algorithm. Thus, Both of the deep learning method (DNN) and machine learning method (RF) can be used as the optimization algorithms. Conclusion：In conclusion, A classification scheme for wetland plant communities in Poyang lake wetland was proposed in this study using multi time-series Sentinel-2 and Sentinel-1 data. The optimal acquisition time periods of satellite data are in January, April, August, September, October and December. The optimal image features group can be red edge indexes group or water and vegetation indexes group for feature selection. The classification algorithm can select deep learning or random forest algorithm to classify wetland plant communities according to the requirements. This classification scheme can effectively improve the accuracy of wetland vegetation mapping in Poyang Lake, and provide scientific and technical solutions for decision-making departments.