Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data

Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods

Bij de Vaate, I. (TU Delft Physical and Space Geodesy)
Martin, Ericka (Student TU Delft)
Slobbe, D.C. (TU Delft Physical and Space Geodesy)
Naeije, M.C. (TU Delft Astrodynamics & Space Missions)
Verlaan, M. (TU Delft Mathematical Physics; Deltares)

2022

In the Arctic Ocean, obtaining water levels from satellite altimetry is hampered by the presence of sea ice. Hence, water level retrieval requires accurate detection of fractures in the sea ice (leads). This paper describes a thorough assessment of various surface type classification methods, including a thresholding method, nine supervised-, and two unsupervised machine learning methods, applied to Sentinel-3 Synthetic Aperture Radar Altimeter data. For the first time, the simultaneously sensed images from the Ocean and Land Color Instrument, onboard Sentinel-3, were used for training and validation of the classifiers. This product allows to identify leads that are at least 300 meters wide. Applied to data from winter months, the supervised Adaptive Boosting, Artificial Neural Network, Naïve-Bayes, and Linear Discriminant classifiers showed robust results with overall accuracies of up to 92%. The unsupervised Kmedoids classifier produced excellent results with accuracies up to 92.74% and is an attractive classifier when ground truth data is limited. All classifiers perform poorly on summer data, rendering surface classifications that are solely based on altimetry data from summer months unsuitable. Finally, the Adaptive Boosting, Artificial Neural Network, and Bootstrap Aggregation classifiers obtain the highest accuracies when the altimetry observations include measurements from the open ocean.

Arctic Ocean
classification
lead detection
machine learning
Sentinel-3
synthetic aperture radar

http://resolver.tudelft.nl/uuid:0f1ff6db-a877-4466-9d1e-0f2ca3fbf0aa

https://doi.org/10.1080/01490419.2022.2089412

0149-0419

Marine Geodesy: an international journal of ocean surveys, mapping and sensing, 45 (5), 462-495

Institutional Repository

journal article

© 2022 I. Bij de Vaate, Ericka Martin, D.C. Slobbe, M.C. Naeije, M. Verlaan