Next Article in Journal
Land Subsidence in Coastal Environments: Knowledge Advance in the Venice Coastland by TerraSAR-X PSI
Previous Article in Journal
An Improved BDS Satellite-Induced Code Bias Correction Model Considering the Consistency of Multipath Combinations
Previous Article in Special Issue
The Drifting Phase of SARAL: Securing Stable Ocean Mesoscale Sampling with an Unmaintained Decaying Altitude
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle

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

Deutsches Geodätisches Forschungsinstitut, Technische Universität München, Arcisstraße 21, 80333 Munich, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(8), 1190; https://doi.org/10.3390/rs10081190
Received: 6 July 2018 / Revised: 23 July 2018 / Accepted: 26 July 2018 / Published: 28 July 2018
(This article belongs to the Special Issue Satellite Altimetry for Earth Sciences)
  |  
PDF [743 KB, uploaded 30 July 2018]
  |  

Abstract

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. View Full-Text
Keywords: leads; satellite altimetry; CryoSat-2; classification; peakiness; polar ocean leads; satellite altimetry; CryoSat-2; classification; peakiness; polar ocean
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Dettmering, D.; Wynne, A.; Müller, F.L.; Passaro, M.; Seitz, F. Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data. Remote Sens. 2018, 10, 1190.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top