The Yellow River Delta wetland is the largest estuarine delta wetland in China. Identifying and classifying the Yellow River Delta wetlands by remote sensing is of great importance to promoting the monitoring and sustainable development of the Yellow River Delta wetland. In this paper, an ensemble algorithm based on an adaptive weight fusion strategy was proposed to identify wetland types in the Yellow River Delta based on Sentinel-2 data, DEM data, and the constructed vegetation and water index. The experimental results show that the classification performance of the ensemble algorithm using the weight fusion strategy outperforms that of a single machine learning classifier, with an average improvement of 9.88% in overall accuracy (OA) and 11.04% in Kappa accuracy. In addition, we select features that can reflect wetlands such as raw band information, spectral index, slope, and elevation, and found that raw band information and spectral index are the most important classification variables by ranking the importance of features.
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