 This study investigates the potential of the deep forest, DF, classifier for wetland classification using three well-known tree-based classifiers, namely extreme gradient boosting, XGB, random forest, RF, and extra tree, ET. The results demonstrated that the DF-XGB classifier outperforms both DF-RF and DF-ET in terms of classification accuracy, albeit with a longer training time. The study also confirmed the superiority of all three DF-based classifiers compared to the CRF and ET classifiers, indicating that the DF algorithm has great capability to be applied over large areas to support regional and national wetland mapping and monitoring. This article was authored by Ali Jamali, Masood Madyanpre, Brian Briscoe, and others.