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. This study compares the accuracy of visually estimated ice concentrations by eight analysts at the Canadian Ice Service with three standards: (i) ice concentrations calculated from automated image segmentation, (ii) ice concentrations calculated from automated image segmentation that were validated by the analysts, and (iii) the modal ice concentration estimate by the group. A total of 76 predefined areas in 67 RADARSAT-2 images are used in this study. Analysts overestimate ice concentrations when compared to all three standards, most notably for low ice concentrations (1/10–3/10). The spread of ice concentration estimates is highest for middle concentrations (5/10, 6/10) and smallest for the 9/10 ice concentration. The overestimation in low concentrations and high variability in middle concentrations introduce uncertainty into the ice concentration distribution in ice charts. The uncertainty may have downstream implications for numerical modelling and sea ice climatology. Inter-analyst agreement is also measured to determine which classifieru0027s ice concentration estimates (analyst or automated image segmentation) disagreed the most. It was found that one of the eight analysts disagreed the most, followed second by the automated segmentation algorithm. This suggests high agreement in ice concentration estimates between analysts at the Canadian Ice Service. The high agreement, but consistent overestimation, results in an overall accuracy of ice concentration estimates in polygons to be 39 %, 95 % CI [34 %, 43 %], for an exact match in the ice concentration estimate with calculated ice concentration from segmentation and, 84 %, 95 % CI [80 %, 87 %], for the  ±1  ice concentration category. Only images with high contrast between ice and open water and well-defined floes are used: true accuracy is expected to be lower than what is found in this study.

the cryosphere

Accuracy and inter-analyst agreement of visually estimated sea ice concentrations in Canadian Ice Service ice charts using single-polarization RADARSAT-2