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.
Highlights
Sea ice impacts the Earth’s radiation balance because thermal feedback between the Sun and theEarth is highly sensitive to sea ice reflectivity
The estimated sea ice thickness was validated with Airborne Electromagnetic (AEM)-bird data
Accurate lead detection is crucial in estimating Local Sea Surface Height (LSSH), which is essential to retrieve the freeboard and thickness [16,23]
Summary
Sea ice impacts the Earth’s radiation balance because thermal feedback between the Sun and the. Assumed that the lowest 2% values of the surface elevation profiles from ICESat would correspond to leads In addition to these relatively simple methods, Farrell et al [23] proposed a threshold-based method to distinguish leads from ice floes using various parameters extracted from ICESat level 1b data, such as gain, reflectivity, radiance and waveform characteristics. Ricker et al [19] used various waveform parameters, such as PP, SSD, stack kurtosis and sea ice concentration, to distinguish leads from ice floes These lead detection methods have been developed in several studies, the determination of ice thickness from CryoSat-2 still suffers from a lack of precise lead discrimination [24]. This study proposes decision trees and random forest machine learning approaches to identify leads and ice floes from CryoSat-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) in order to estimate sea ice thickness
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