盐沼是最具生态价值且最为脆弱的生态系统之一，及时、精确地监测盐沼植被分布对于海岸带生态管理和保护尤为重要。随着多源遥感数据不断积累，时间序列方法日益成为海岸带资源监测的重要手段。然而，由于云雨天气频发，海岸带影像可用性较差，如何有效构建时间序列仍存在较大挑战。本研究耦合多源Landsat影像，以长三角典型滨海湿地为研究区，构建像元级时间序列的XGBoost分类模型，探讨盐沼植被精细识别的可行性与稳定性。研究结果表明：（1）通过相互定标耦合多源影像成效显著，不但提高了影像可用性，还减小了不同传感器之间的光谱反射率差异。（2）基于像元级时间序列方法的盐沼植被分类效果较好，研究区内盐沼植被平均总体分类精度可达81.50%，平均Kappa系数为0.758，对于长三角区域分布广泛的海三棱藨草和互花米草尤为优良。（3）相较于单一时相分类方法，像元级时间序列分类方法的年际绝对均值误差保持小于3.88%，稳定性较好，有望应用在盐沼植被动态变化监测中，为我国海岸带资源高效管理提供遥感技术支持。

Salt marshes are the most valuable and vulnerable ecosystem in the world, accurate and timely monitoring the distribution of salt marsh vegetation is thus important. With the accumulation of multi-source remote sensing imagery, the time-series method has increasingly become an important means for monitoring coastal areas. However, it is still challenging to construct time series effectively since the number of available observations is relatively low due to frequent cloudy weather in the coastal areas. In this study, we coupled multi-sourced Landsat images and constructed a pixel-level time-series with XGBoost. Based on which, the feasibility and stability for classifying salt marsh vegetation were tested using the three typical sites in Yangtze River Delta. The results showed that, (1) Inter-calibration for multi-sourced images was necessary for not only improving the available of images, but also reducing the spectral differences between sensors. (2) The performance of salt marsh vegetation classification based on the pixel-level time-series was favorable, reflected by 81.50% as the mean overall accuracy and 0.755 as the Kappa coefficient. The classification results were excellent especially for the widely distributed S. salsa and S. alterniflora in Yangtze River Delta. (3) Compared with the single-phrase classifications, the pixel-level time-series based classifications were stable, evidenced by an inter-annual absolute mean error lower than 3.27%. Therefore, our proposed method is expected for dynamic monitoring on salt marsh vegetation, which facilitates to manage coastal resources and implement ecological conservation effectively of China"s coasts.