Arctic Sea Ice property retrieval from synthetic aperture radar with deep learning methods ...

• Main Author: Kortum, Karl
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Universität Bremen 2024
• Subjects: Sea Ice; Machine Learning; Deep Learning; Synthetic Aperture Radar; Physics-informed Neural Networks; Altimetry; 530; Arctic; Sea ice
• Online Access: https://dx.doi.org/10.26092/elib/2885; https://media.suub.uni-bremen.de/handle/elib/7803

Current climate models are not capturing the feedback mechanisms driving the accelerated warming of the Arctic. A central challenge is the sparsity of observations. Satellite-borne synthetic aperture radar (SAR) instruments have the capability of monitoring Earth's sea ice masses at high resolution, unhampered by cloud coverage or the Arctic night. The measurements are made at scales of 10's of metres whilst still covering the Arctic in a matter of days. However, interpreting the radar signal to retrieve relevant sea ice information is difficult because of the complex interactions of the ice with the electromagnetic radar signal. Conventional neural network algorithms leverage contextual image data to make accurate predictions of surface ice properties comparable to those made by human experts. They are, however, dependent on large amounts of high-quality ground truth that is rare in these regions. Thus, the full potential of the SAR data is yet to be unlocked. With the advent of the MOSAiC mission, large ...