This site requires Cookies enabled in your browser for login.
Updating ...
WaterNet Home
WaterNet
for
pour le
Canada
Menu
WaterNet
Home
GWFO
Home
Catalogue
Master Index
Data
Centre
X
Find Data By Variable Find Data By Site, Facility, or Deployable Show Near-realtime Telemetry (7 day)
Collections
X
Defaults
Select All
Websites
X
Global Water Futures Observatories (GWFO) Global Water Futures (GWF) Global Institute for Water Security (GIWS) International Network of Alpine Research Catchment Hydrology
Legacy Research Programs
X
Changing Cold Regions Network (CCRN) Drought Research Initiative (DRI) International Network of Alpine Research Catchment Hydrology (Legacy Site) Improving Processes & Parameterization for Prediction in Cold Regions Hydrology (IP3) The Mackenzie Global Energy and Water Cycle Experiment (GEWEX) Study (MAGS)
Legacy sites
Map
Utilities
X
Account Settings Create a New Record Record List Alias List Editor
Edit Data Centre
Data Types
. . .
X
Clear
Select All
Advanced Search
Go to Top⇡
Related items loading ...
Fetching Chart ...
Publication Additional Information Download
Publication Type
Journal Article
Authorship
Duguay, C., & Wang, J.
Title
Advancement in bedfast lake ice mapping from Sentinel-1 SAR data
Year
2019
Publication Outlet
In IGARSS 2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 6922-6925). IEEE
DOI
https://doi.org/10.1109/IGARSS.2019.8900650
Citation
Duguay, C., & Wang, J. (2019). Advancement in bedfast lake ice mapping from Sentinel-1 SAR data. In IGARSS 2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 6922-6925). IEEE. https://doi.org/10.1109/IGARSS.2019.8900650
Abstract
Algorithms for the generation of a bedfast/floating lake ice product from Sentinel-1A/B synthetic aperture radar (SAR) data were implemented, cross-compared, and validated for various permafrost regions (Alaska, Canada and Russia). The algorithms consisted of: 1) thresholding; 2) Iteration Region Growing with Semantics (IRGS); and 3) K-means. The thresholding algorithm (92.4%) was found to perform slightly better on average than the IRGS algorithm (90.1%), and to outperform K-means (85.3%). The thresholding algorithm was therefore selected for implementation of a processing chain to generate a novel bedfast/floating lake ice product. Using a time series of Sentinel-1 SAR data, the new map product shows the day of year (DOY) when the ice becomes bedfast or remains afloat for individual lake sections.
Program Affiliations
GWF: Global Water Futures
Publication Stage
Published
Download Links
https://doi.org/10.1109/IGARSS.2019.8900650
© 2026 - WaterNet Version 2026-07-24
Global Water Futures Observatories
Powered by
G W F Net
T-2022-12-05-p1cCdBDWu3kmDwgMu2LE0Tg Publication 1.0