Sea Ice Concentration Estimation Techniques Using Machine Learning: An End-To-End Workflow for Estimating Concentration Maps from SAR Images

TL;DR

This paper presents an end-to-end machine learning workflow for estimating sea ice concentration maps from SAR images, incorporating a novel uncertainty-aware objective function and validated with in-situ data.

Contribution

It introduces a new objective function for training concentration models that accounts for measurement uncertainty, enhancing reliability.

Findings

Effective concentration estimation from SAR data demonstrated.

Uncertainty modeling improves model robustness.

Open-source tools and data are provided for reproducibility.

Abstract

Sea ice concentration is an important metric used to characterize polar sea ice behavior. Understanding this behavior and accurately representing it is of critical importance for climate science research, and also has important uses in the context of maritime navigation. An end-to-end workflow for generating learned concentration estimation models from synthetic aperture radar data, trained on existing passive microwave data, is presented here. A novel objective function was introduced to account for uncertainty in the passive microwave measurements, which can be extended to account for arbitrary sources of error in the training data, and a recent set of in-situ observations was used to evaluate the reliability of the chosen passive microwave concentration estimation model. Google Colaboratory was used as the development platform, and all notebooks, training data, and trained models are available on GitHub. This chapter is an overview of the most interesting aspects of this investigation, and a detailed report is also available on GitHub.