A comparative study of data input selection for deep learning-based automated sea ice mapping

Abstract

The precise monitoring of sea ice parameters, including sea ice concentration and stage of development, is imperative for tactical navigation. Recent studies have showcased the enhanced mapping accuracy achieved by incorporating multi-source auxiliary data, such as passive microwave data, with Synthetic Aperture Radar (SAR) images. However, there remains a lack of research assessing the impact of individual features on model performance. This paper addresses this knowledge gap through ablation studies and alternate comparisons of data inputs. Building on the success in the AutoIce Challenge, we leverage the AI4Arctic Sea Ice Challenge Dataset to train multitask sea ice mapping models employing a U-Net architecture. Results from cross-validation and testing sets with all season data reveal the significant enhancement in estimation accuracy for all parameters when utilizing most of the AMSR2 channels. Additionally, the incorporation of time and location information as ancillary channels further amplifies the classification accuracy of all major ice types. Furthermore, among the various available ERA5 weather parameters, the inclusion of wind speed data proves effective in mitigating misclassifications in ice regions, particularly under melting scenarios. The paper culminates with a feature importance ranking table encompassing all available features, providing valuable guidance for the selection of pertinent data inputs. This comprehensive comparative study not only contributes to advancing sea ice mapping methodologies but also offers valuable insights into the nuanced impact of individual features on model performance.

Keywords

• Sea ice Mapping
• Synthetic aperture radar (SAR)
• AMSR2
• Multi-source Data
• Feature importance
• AI4Arctic