Self-Attention Convolutional Long Short-Term Memory for Short-Term Arctic Sea Ice Motion Prediction Using Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 23; p. 5437
• Main Authors: Zhong, Dengyan, Liu, Na, Yang, Lei, Lin, Lina, Chen, Hongxia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2023
• Subjects: Accuracy; Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E); Arctic sea ice; Arctic zone; Artificial intelligence; Climate change; Computational linguistics; Datasets; Deep learning; Fields (mathematics); Forecasting techniques; Global warming; Ice cover; Ice environments; Language processing; Long short-term memory; Machine learning; Natural language interfaces; Neural networks; Numerical prediction; optical flow; Optical flow (image analysis); Prediction models; Radiometers; Scanning; Sea ice; Self-Attention ConvLSTM; Self-Attention Memory; Sensors; Ships; Solar radiation; Wind; Arctic region
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15235437

Over the past four decades, Arctic sea ice coverage has steadily declined. This loss of sea ice has amplified solar radiation and heat absorption from the ocean, exacerbating both polar ice loss and global warming. It has also accelerated changes in sea ice movement, posing safety risks for ship navigation. In recent years, numerical prediction models have dominated the field of sea ice movement prediction. However, these models often rely on extensive data sources, which can be limited in specific time periods or regions, reducing their applicability. This study introduces a novel approach for predicting Arctic sea ice motion within a 10-day window. We employ a Self-Attention ConvLSTM deep learning network based on single-source data, specifically optical flow derived from the Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz data, covering the entire Arctic region. Upon verification, our method shows a reduction of 0.80 to 1.18 km in average mean absolute error over a 10-day period when compared to ConvLSTM, demonstrating its improved ability to capture the spatiotemporal correlation of sea ice motion vector fields and provide accurate predictions.