| Abstract: | In this paper, a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data, and a backpropagation (BP) neural network is used to classify the Arctic sea ice type. During the implementation of the Bayesian sea ice detection algorithm, linear sea ice model parameters and the backscatter variance suitable for HY-2A/SCAT were proposed. The sea ice extent obtained by the Bayesian sea ice detection algorithm was projected on a 12.5 km grid sea ice map and validated by the Advanced Microwave Scanning Radiometer 2 (AMSR2) 15％ sea ice concentration data. The sea ice extent obtained by the Bayesian sea ice detection al-gorithm was found to be in good agreement with that of the AMSR2 during the ice growth season. Meanwhile, the Bayesian sea ice detection algorithm gave a wider ice edge than the AMSR2 during the ice melting season. For the sea ice type classification, the BP neural network was used to classify the Arctic sea ice type (multi-year and first-year ice) from January to May and October to De-cember in 2014. Comparison results between the HY-2A/SCAT sea ice type and Equal-Area Scalable Earth Grid (EASE-Grid) sea ice age data showed that the HY-2A/SCAT multi-year ice extent variation had the same trend as the EASE-Grid data. Classification er-rors, defined as the ratio of the mismatched sea ice type points between HY-2A/SCAT and EASE-Grid to the total sea ice points, were less than 12％, and the average classification error was 8.6％ for the study period, which indicated that the BP neural network classification was a feasible algorithm for HY-2A/SCAT sea ice type classification. |
