High spatiotemporal reconstruction of Arctic sea ice concentration based on multi-source remote sensing data

Abstract

Sea ice plays a significant role in Arctic research and operations. However, the lack of high spatiotemporal resolution observations on sea ice makes it challenging to accurately depict short-term sea ice changes. This limitation hampers the development of small-scale sea ice change studies and increases uncertainties in Arctic research and operational safety. With advancements in deep learning techniques and the abundance of multi-source remote sensing data such as optical, radar, and passive microwave, reconstructing high spatiotemporal resolution sea ice concentration in the Arctic becomes feasible. Based on multi-source remote sensing data and the integration of sea ice dynamics and thermodynamics, this study proposes a novel deep learning model for high spatiotemporal resolution sea ice concentration reconstruction. Based on this model, we achieved sub-kilometer scale and hourly-level reconstructions of Arctic sea ice concentration from 2021 to 2022, with a mean absolute error of less than 5%, thereby providing data support for Arctic research and operations.