https://hdl.handle.net/10012/22811

Fatt, Jennifer (2026) Lake Ice Thickness Retrieval from SWOT and Legacy Spaceborne SAR Altimetry, UWSpace - Theses & Dissertations, https://hdl.handle.net/10012/22811

Lakes play a critical role as climate change proxies and cover a significant portion of the northern latitude landscape. Lake ice phenology offers valuable insight into changing climate patterns, yet in-situ observations of lake ice have declined substantially in recent decades. This observational gap underscores the growing importance of remote sensing as a tool for understanding and monitoring lake ice. Northern and remote communities particularly rely on lake ice quality, quantity, and thickness for transportation on ice roads, subsistence activities, and recreational use. There has been limited research exploring the use of satellite altimetry for the retrieval of lake ice thickness (LIT); however, its efficacy and utility have been highlighted in recent studies. The Ku-band SWOT (Surface Water and Ocean Topography) nadir altimeter (NAlt) provides an opportunity to retrieve ice properties and directly measure ice thickness. This study assesses the retrieval of LIT from SAR altimeters aboard the legacy Sentinel-3 and Sentinel-6 sensors over the winter seasons 2019 to 2025 on Lhn Mn (Kluane Lake), Yukon, and compares it with the estimated LIT derived from the SWOT altimeter analysis. LIT can be determined using Ku-band altimetry through the analysis of double-peaked waveforms characteristic of lake ice formed by the interaction of the radar signal with the ice interfaces. The utilization of SWOT altimetry has the potential to advance understanding of lake ice processes and to provide valuable datasets for d hydrological models, as well as overall resource management. This thesis discusses the potential applications of SWOT altimetry in lake ice thickness retrieval, emphasizing its capacity to fill critical data gaps. This study implements a dual-peak waveform methodology to estimate LIT using SWOT NAlt and reports an overall RMSE of <0.15m for the SWOT scientific period 2024-2025.

https://hdl.handle.net/10012/22811