This paper introduces IceEB, i.e., an innovative ensemble-based method that is designed to automate mapping of river ice types using radar imagery. Its goal is the merger of outcomes from three classifiers (IceMAP-R, RIACT, and IceBC) through ensemble-estimation, resulting in a highly performant and fully automated river ice-type map, which is applicable under all meteorological conditions. The first step of our research is the development of a meta-classifier and a confidence estimation index, then we validate our method using ground-truth datasets and finally compare the performance between IceEB and the original classifiers. The anticipated outcome was a map exhibiting superior results compared to individual classifiers. Validation and comparison of IceEB employed six RADARSAT-2 HH-HV C-band images that were selected from historical datasets of Quebec and Alberta rivers (Canada). IceEB integrates RADARSAT-2 satellite imagery, a digital elevation model, and a river mask, undergoing preprocessing tasks before activating the three initial classifiers. The meta-classifier then performs ensemble-based classification, yielding a legend comprised of water, sheet ice and rubble ice. This approach facilitates broad participation in validation data collection, differentiation between ice covers and ice jams, and minimization of assumptions regarding ice formation. We conclude that IceEB successfully combines existing radar remote sensing ice- classification models to create accurate river ice-type maps. IceEB’s ensemble-based approach outperforms individual classifiers, achieving overall accuracy >91 % for each class. Shortcomings of the original classifiers are effectively offset through parallel use, resulting in marked improvements in automation and generalizability across diverse Canadian meteorological conditions.
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