Novel methods to study sea ice deformation, linear kinematic features and coherent dynamic clusters from imaging remote sensing data

Abstract

Satellite synthetic aperture radar (SAR) data are commonly utilized for calculating sea ice displacements and, consequently, sea ice deformation strain rates. However, strain rate calculations often suffer from a poor signal-to-noise ratio, especially for products with a spatial resolution higher than 1 km. In this study, a new filtering method to strain rate calculations derived from Sentinel-1 SAR image pairs with a spatial resolution of 800 m was applied. Subsequently, a power law to evaluate the deformation rates at decreasing spatial resolutions was employed to assess the quality of the filtered data. Upon positive evaluation of the filtered data, two innovative methods for sea ice deformation assessment were introduced. The first method, named “damage parcel” tracking, involved the combined analysis of displacements and deformation strain rates to monitor divergence and convergence within the sea ice cover. Additionally, a new term to describe the behavior of the winter pack was proposed: “coherent dynamic clusters” (CDCs). CDCs are cohesive clusters of ice plates within the pack ice that move coherently along linear kinematic features (LKFs). The second novel method developed in this study focused on exploring the geometrical properties of these CDCs. Both methods were applied to the January–February collection of Sentinel-1 SAR imagery available during the N-ICE2015 campaign. The damage parcels were continuously tracked over a period of 3 weeks, including a major storm, revealing a slow healing process of existing LKFs. Furthermore, the CDC analysis demonstrated the presence of elongated CDCs with a density ranging from 5 to 20 per 100 km by 100 km, and the shortest distance between LKFs was found to be 5–10 km.