# Remote measurement of sea ice dynamics with regularized optimal transport

## TL;DR

This paper introduces a novel method using regularized optimal transport to measure sea ice deformation from satellite imagery, addressing challenges of temporal sparsity and feature persistence in Arctic observations.

## Contribution

It presents a new application of optimal transport for high-resolution sea ice dynamics measurement, with efficient computational methods and validation on synthetic and real satellite data.

## Key findings

- Accurately estimates ice deformation from sparse satellite images.
- Effective at original image resolution for synthetic and MODIS data.
- Addresses limitations of traditional feature tracking methods.

## Abstract

As Arctic conditions rapidly change, human activity in the Arctic will continue to increase and so will the need for high-resolution observations of sea ice. While satellite imagery can provide high spatial resolution, it is temporally sparse and significant ice deformation can occur between observations. This makes it difficult to apply feature tracking or image correlation techniques that require persistent features to exist between images. With this in mind, we propose a technique based on optimal transport, which is commonly used to measure differences between probability distributions. When little ice enters or leaves the image scene, we show that regularized optimal transport can be used to quantitatively estimate ice deformation. We discuss the motivation for our approach and describe efficient computational implementations. Results are provided on a combination of synthetic and MODIS imagery to demonstrate the ability of our approach to estimate dynamics properties at the original image resolution.