Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data

• Published in: Remote Sensing
• Main Authors: Denise Dettmering, Alan Wynne, Felix L. Müller, Marcello Passaro, Florian Seitz
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2018
• Subjects: leads; satellite altimetry; CryoSat-2; classification; peakiness; polar ocean; Science; Q; Sea ice
• Online Access: https://doi.org/10.3390/rs10081190; https://doaj.org/article/9f01c8ce85934b65b9d249e15a37b2b5

In polar regions, sea-ice hinders the precise observation of Sea Surface Heights (SSH) by satellite altimetry. In order to derive reliable heights for the openings within the ice, two steps have to be fulfilled: (1) the correct identification of water (e.g., in leads or polynias), a process known as lead classification; and (2) dedicated retracking algorithms to extract the ranges from the radar echoes. This study focuses on the first point and aims at identifying the best available lead classification method for Cryosat-2 SAR data. Four different altimeter lead classification methods are compared and assessed with respect to very high resolution airborne imagery. These methods are the maximum power classifier; multi-parameter classification method primarily based on pulse peakiness; multi-observation analysis of stack peakiness; and an unsupervised classification method. The unsupervised classification method with 25 clusters consistently performs best with an overall accuracy of 97%. Furthermore, this method does not require any knowledge of specific ice characteristics within the study area and is therefore the recommended lead detection algorithm for Cryosat-2 SAR in polar oceans.