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Remote Sens. 2016, 8(9), 698; doi:10.3390/rs8090698

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
2
Space Imaging R&D Lab, LIG Nex1 Co., Ltd., Yongin 16911, Korea
3
Division of Polar Ocean Environment, Korea Polar Research Institute, Incheon 21990, Korea
4
Department of Environmental Resources Engineering, State University of New York, College of Environmental Science and Forestry, Syracuse, NY 13210, USA
1
University College London, London, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Walt Meier, Mark Tschudi, Xiaofeng Li and Prasad S. Thenkabail
Received: 30 April 2016 / Revised: 14 August 2016 / Accepted: 19 August 2016 / Published: 24 August 2016
(This article belongs to the Special Issue Sea Ice Remote Sensing and Analysis)
View Full-Text   |   Download PDF [9596 KB, uploaded 24 August 2016]   |  

Abstract

Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011–2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM)-bird data collected over two days during the CryoSat Validation experiment (CryoVex) field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE) = 0.29 m) compared to thickness estimation based on existing lead detection methods (RMSE = 0.86–0.93 m). Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011–2013 and rebounded in 2014. View Full-Text
Keywords: CryoSat-2; lead detection; sea ice thickness; machine learning CryoSat-2; lead detection; sea ice thickness; machine learning
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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).

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Lee, S.; Im, J.; Kim, J.; Kim, M.; Shin, M.; Kim, H.-C.; Quackenbush, L.J. Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection. Remote Sens. 2016, 8, 698.

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