 Efforts are increasingly being made to classify the world's wetland resources, an important ecosystem and habitat that is diminishing in abundance. Non-parametric machine learning algorithms such as Decision Tree, DT, Rule-Based, RB, and Random Forest, RF, have been used to classify these wetlands. High-resolution satellite imagery can provide more specificity to the classified end product and ancillary data layers such as the Normalized Difference Vegetation Index, and Hydrogeomorphic layers such as Distance to a Stream can be coupled to improve overall accuracy OA in wetland studies. In this paper, we compared three non-parametric machine learning algorithms, DT, RB, and RF using a large field-based dataset N equals 228 from the Selenga River Delta of Lake Baikal, Russia. We also explored the use of ancillary data layers selected to improve OA, with a goal of providing end users with a recommended classifier to use and the most parsimonious suite of input parameters for classifying wetland-dominated landscapes, though all classifiers appeared suitable. This article was authored by Tedros Amberjane, Charles Elaine, Chihoshin Wu, and others.