Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is used to get time series Sentinel-2 NDVI, from which the vegetation phenology variables are derived by the threshold method. Subsequently, both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarization backscatters (σ0 VV, σ0 VH) are obtained using the time series Sentinel-1 images. Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification. Therefore, we segment Sentinel-2 multispectral images to delineate meaningful objects in this study. Then, in order to reduce data redundancy and computation time, we analyze the optimal feature combination using the Sentinel-2 multispectral images, Sentinel-2 NDVI time series, phenological variables and other vegetation index derived from Sentinel-2 multispectral images, as well as time series Sentinel-1 backscatters at the object level. Finally, the stacked generalization algorithm is utilized to extract the wetland information based on the optimal feature combination in the Dongting Lake wetland. The overall accuracy and Kappa coefficient of the object-based stacked generalization method are 92.46% and 0.92, which are 3.88% and 0.04 higher than that using the pixel-based method. Moreover, the object-based stacked generalization algorithm is superior to single classifiers in classifying vegetation of high heterogeneity areas.

在过去的几十年中，由于自然和人为因素，湿地生态系统经历了巨大的挑战。湿地地图对于保护和管理陆地生态系统至关重要。这项研究旨在基于多时相Sentinel-1和Sentinel-2数据，使用基于对象的堆叠泛化（Stacking）方法获得准确的湿地地图。首先，利用鲁棒自适应空间时空融合模型（RASTFM）获得时间序列Sentinel-2 NDVI，通过阈值方法从中导出植被物候变量。随后，使用时间序列Sentinel-1图像获得垂直发射垂直接收（VV）和垂直发射水平接收（VH）偏振反向散射（σ0VV，σ0VH）。SAR数据固有的斑点噪声，导致过度分割或分割不足，会影响图像分割并降低湿地分类的准确性。因此，在本研究中，我们对Sentinel-2多光谱图像进行了分割，以描绘出有意义的物体。然后，为了减少数据冗余和减少计算时间，我们使用Sentinel-2多光谱图像，Sentinel-2 NDVI时间序列，物候变量和其他来自Sentinel-2多光谱图像的植被指数来分析最佳特征组合，以及时间序列Sentinel-1在对象级别后向散射。最后，基于洞庭湖湿地的最优特征组合，利用堆叠泛化算法提取湿地信息。基于对象的堆栈泛化方法的总体准确性和Kappa系数分别为92.46％和0.92，与使用像素法相比，分别提高了3.88％和0.04。此外，在对高异质性地区的植被进行分类时，基于对象的堆叠泛化算法优于单一分类器。