Abstract: Due to environmental and ecosystem complexity, it is difficult to accurately mapping swamp in mountainous regions, especially in the inaccessible areas. Remote sensing provides a potential powerful tool for extracting the extent distribution of swamp, with great significance for understanding global change. In this study, we selected a scene extent of Sentinel imagery covering the Emur River Basin located in the Greater Khingan Mountains as the study area. The radar bands of Sentinel-1 with the red-edge bands of Sentinel-2, and other multispectral bands were combined to establish the feature bundles for delineating swamp. In order to improve the accuracy of swamp delineation, deep learning and support vector machine (SVM) were tested and compared. The results showed that: (1) For the deep learning method, the overall accuracy was improved by 2.3% to 84.3% when adding the red feature to the multispectral feature alone. Furthermore, when radar features were added, the accuracy was improved by another 1.8%. In other words, the combination of multispectral, rededge, and radar features got an overall accuracy of 86.5% and a Kappa coefficient of 0.84. The highest producer accuracy of each category was 99.9% for woodland, 85.4% for marsh, 69.9% for swamp, and 89.3% for waterbody. The maximum iteration accuracy of the optimal deep learning model was 95.7%. (2) The support vector machine method was used for the control test. The overall accuracies of only multispectral features, followed by the addition of red-edge features and radar features were 74.4%, 75.4% and 77.3%, respectively. The overall accuracy of each scheme was lower than that of deep learning method. In this study, deep learning method could extract the swamp extent information accurately from the Sentinel-1/2 images, with the highest accuracy of 86.5%, which could provide a method reference for future research on the accurate extraction of swamp at broader scale.

Key words: swamp, sentinel, red-edge band, deep learning, support vector machine.