Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data

Abstract

Sea ice thickness is one of the essential characteristics of sea ice. Sea ice lead detection is the key to sea ice thickness estimation from radar altimetry data. This research studies ten different surface type classification methods, including supervised learning, unsupervised learning, and threshold methods, being applied to the HY-2B radar altimeter data collected in October 2019 in the Arctic Ocean. The Sentinel-1 Synthetic Aperture Radar (SAR) images were used for training and validation of the classifiers. Compared with other classifiers, the supervised Bagging ensemble learning classifier showed excellent and robust performance with overall accuracy up to 95.69%. In order to assess the performance of the Bagging classifier in practical applications, lead fractions from January 2019 to March 2021 based on the HY-2B radar altimeter data were mapped using the trained Bagging classifier and compared to the CryoSat-2 L2I data product. The results of the lead fraction showed the monthly variability of ice lead, and the ice lead had a reasonable spatial distribution and was consistent with CryoSat-2 L2I data products. According to these results, the Bagging classifier can provide an essential reference for future studies of Arctic sea ice thickness and sea level estimation from HY-2B radar altimeter data.

Sea Ice Detection by an Unsupervised Method Using Ku- and Ka-Band Radar Data at Low Incidence Angles: First Results

“…The performance of the K-means approach widely used for unsupervised classification is evaluated. In previous papers, K-means was applied for sea ice detection using radar altimeter data [16] and SAR measurements [17]. In the present work, this method is applied for the first time to the NRCS data in the Ku-and Ka-bands at low incidence angles different from zero.…”

A Shape-Aware Network for Arctic Lead Detection from Sentinel-1 SAR Images

“…As a result, leads play a significant role in opening up Arctic shipping routes and supporting scientific research missions [3]. Furthermore, sea ice lead detection plays a crucial role in estimating sea ice thickness using radar altimeter data [4]. Obtaining the accurate distribution of leads in polar regions is of great importance.…”

“…Current methods that could be employed for lead detection from SAR images can be categorized into three: threshold-based methods [13], machine learning (ML) methods such as the k-nearest neighbors [12], K-means [4], and neural network [3], and deep learning (DL) methods [14,15]. The aforementioned threshold-based and ML methods all need manual involvement, such as determining thresholds and selecting features.…”