Toward a Multidecadal SAR Analysis of Sea Ice Types in the Atlantic Sector of the Arctic Ocean

We present a methodology to derive sea ice type classification maps in the Atlantic sector of the Arctic Ocean for the autumn through spring seasons, from 1991 to present. We use datasets from four C-band synthetic aperture radar (SAR) sensors: Sentinel-1, RADARSAT-2, Envisat ASAR, and ERS-1/2. We demonstrate a classification workflow to specifically accommodate different characteristics of these datasets to produce satisfactory and consistent classification results. We use a Gaussian sea ice type classifier correcting for per-class incidence angle effects on SAR images. Input features include backscatter intensities corrected for thermal noise mainly using Kalman filtering, and gray-level co-occurrence matrix-based image textures. Class labels include open water, young ice, level and deformed first-year ice, and multiyear ice. A robust Markov random field-based contextual smoothing process is applied to produce final classification maps. Assessments of classification accuracies and consistency of class fractions between datasets for the training scenes, as well as for a one-year classification time series in a test area and one-month classification map mosaics over the Atlantic Arctic, suggest that our workflow is capable of producing temporally consistent large-scale ice type maps at sub-km spatial resolution. This study provides the theoretical foundation for a three-plus decade C-band SAR-based sea ice type classification product. Such a product will contribute to the assessment of seasonal and inter-annual sea ice variations and climate trends, which is critical for understanding physical and biogeochemical processes acting across the Arctic's ocean–ice–atmosphere interface.