Salt marshes are the world’s most valuable and vulnerable ecosystem. Thus, accurate and timely monitoring of the distribution of salt marsh vegetation is essential. With the accumulation of multi-source remote sensing imagery, the time-series method has increasingly become important for monitoring coastal areas. However, effectively constructing time series is still challenging as the number of available observations is relatively low owing to the 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 this, the feasibility and stability of classifying salt marsh vegetation were tested using the three typical sites in the Yangtze River Delta. Results showed that (1) Inter-calibration for multi-sourced images was necessary for not only improving the availability of images but also reducing the spectral differences among 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, particularly for the widely distributed Suaeda salsa and Spartina alterniflora in the 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 of salt marsh vegetation, which facilitates managing coastal resources and implementing ecological conservation effectively of China’s coasts.

remote sensing classification;salt marsh vegetation;Landsat;pixel-level time-series;XGBoost;Yangtze River Delta;Red-crowned Crane Nature Reserve;Jiuduansha Wetland;Southern Coastal Wetland in Hangzhou Bay