Abstract Radar remote sensing provides useful information to differentiate river ice conditions and ice cover types in large rivers. However, false classifications are common, especially at the end of winter, due to water on ice as well as wet snow. These situations can present challenges to end users, such as water resources managers and flood forecasters. In this study, we design a logic-driven assessment to refine existing classifications to distinguish between areas of water or wet snow on ice and open water. It is uncommon for river segments to experience ice cover, followed by open water, then ice cover again, within three consecutive radar images. Our decision tree analysis therefore assumes that river segments that are classified as water, but classified as ice in the radar images before and after, represent water or wet snow on ice. We examine the potential of this approach on two rivers in the Yukon Territory, Canada. The Äshèyi Chù (Aishihik River) is a narrow, regulated river with a relatively steep slope (0.3%) and commonly experiences flood issues at freeze-up. The Chu kon’ dëk (Yukon River at Dawson) is a much larger, low gradient (0.04%) river with a history of ice jam related flooding. Pixels are tested based on this concept, and a clustering approach is applied to reduce noise. The success of the algorithm is assessed using drone imagery and Sentinel-2 optical imagery. We show that using logic can offer ways to refine river ice classification, that is meaningful to the end user.