Evaluation of Machine Learning Algorithms for the Classification of Lake Ice and Open Water from Sentinel-3 SAR Altimetry Waveforms

• Main Author: Mugunthan, Jaya Sree
• Format: Master Thesis
• Language: English
• Published: University of Waterloo 2023
• Subjects: lake ice; classification; waveform; machine learning; sentinel-3; ice cover; inland water; sar altimetry; Sea ice
• Online Access: http://hdl.handle.net/10012/19071

Lakes cover a significant fraction of the landscape in many northern countries. They play a key role in regulating weather and climate and also have a significant impact on northern communities since the presence (or absence), extent and thickness of lake ice affect transportation (ice roads), food availability, recreational activities, and tourism in wintertime. The drastic decline in in-situ observations of lake ice phenology (i.e., freeze-up and break-up dates and ice cover duration) and lake ice thickness globally over the last three decades make remote sensing technology a viable means for monitoring lake ice conditions. Although satellite radar altimetry has been used in various cryospheric and hydrological studies, little work has been conducted on lake ice compared to, for example, sea ice and the estimation of lake water levels. This study was carried out using Sentinel-3A/B SAR altimetry data acquired over three ice seasons (2018-2019, 2019-2020 and 2020-2021) at 11 large lakes across the Northern Hemisphere. We explored the information provided by radar waveforms to discriminate between open water, first (young) ice, growing ice and melting ice using machine learning models. To characterize the waveforms, seven waveform parameters were derived: Leading Edge Width (LEW), Offset Center of Gravity (OCOG) Width, Pulse Peakiness (PP), backscatter coefficient (Sigma0), late tail to peak power (LTTP), early tail to peak power (ETTP) and the maximum value of the echo power. Four machine learning algorithms including Random Forest (RF), Gradient Boosting Trees (GBT), K Nearest Neighbour (KNN) and Support Vector Machine (SVM) classifiers were tested to assess their capability in classifying the lake surfaces across all years. Manual class labelling based on Sentinel-3 Synthetic Aperture Radar Altimeter (SRAL) waveforms and complementary satellite data (Sentinel-1 imaging SAR data, Sentinel-2 Multispectral Instrument (MSI) Level 1C data, and MODIS Aqua/Terra data) was performed to create training and test ...