Objectives The rapid changes in the arctic sea ice have profoundly impacted energy exchange processes between the ice, atmosphere, and ocean, further accelerating global climate change through various mechanisms such as albedo feedback, heat exchange, and changes in ocean circulation. High-resolution sea ice concentration (SIC) products generated by multi-source remote sensing are of great significance and application value for monitoring global climate change and ship navigation. To address the need for high temporal and spatial resolution SIC products, passive microwave data with high temporal resolution is used to estimate SIC based on the least squares method, which serves as the initialization input for the proposed fusion model that combines passive and active data.
Methods A conditional random field model is applied to active microwave synthetic aperture radar data to derive sea ice and water classification labels, which are used as class priors for the fusion model. The fusion model based on maximum a posteriori estimation integrates both active and passive microwave data to achieve high-precision SIC retrieval. Compared to the existing arctic radiation and turbulence interaction study (ARTIST) sea ice (ASI) algorithm for passive microwave concentration products, the fusion SIC product provides finer details of leads and marginal sea ice zones. Compared with moderate-resolution imaging spectroradiometer SIC at 1 km, the fusion product can obtain time-continuous SIC products, and the texture features are clearer.
Results The results of quantitative analysis showed that the mean deviation of the fusion product relative to the ASI sea ice density was (3.6±1.12)% with a standard deviation of (3.5±0.64)%. The results of the time series analysis showed a negative correlation between the rate of sea ice growth and sea ice minimum. In addition, time series analysis reveals a negative correlation between the sea ice growth rate and the minimum sea ice extent. Except for 2020, the cumulative 40-day sea ice growth rates during 2016—2022 were relatively consistent.
Conclusions The primary regions of sea ice increase were the Laptev and East Siberian Seas, where the rapid temperature drop contributed significantly to the observed ice dynamic.
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