Characterizing the Great Lakes Coastal Wetlands with InSAR Observations from X-, C-, and L-Band Sensors. Issue 6 (1st November 2020)

• Record Type: Journal Article
• Authors: Chen, Zhaohua; Banks, Sarah; Behnamian, Amir; White, Lori; Montpetit, Benoit; Pasher, Jon; Duffe, Jason
• Is Part Of: Canadian journal of remote sensing. Volume 46:Issue 6(2020)
• Page Start: 765
• Page End: 783
• Publication Date: 2020-11-01
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2020.1867974
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: We investigated the potential of using Synthetic Aperture Radar (SAR) imagery from three different frequencies: X-, C-, and L-band, to characterize coastal wetlands in the Great Lakes. Three sets of SAR data acquired over the Bay of Quinte, Ontario, Canada between 2016 and 2018 from Radarsat-2, 2016 from TerraSAR-X, and 2018 from ALOS-2 satellites were processed using small baseline subset (SBAS) Interferometric SAR (InSAR) techniques to provide maps of surface changes in marshes and swamps. Results showed that SAR backscatter and coherence were sensitive to sensor characteristics (frequency, polarization, incidence angle, acquisition interval), changes in water level, and phenology. InSAR time series observations were evaluated using measurements from water level loggers based on correlation and root mean square error (RMSE) from a linear regression model. Correlation between InSAR measurements and water level changes in the field varied from −1 to 1 depending on the site, type of wetland vegetation, and incidence angle. Although results from some sensor modes provided good correlation (0.77–1) at a few locations, the low fringe rate and large RMSE between 4 and 64 cm indicated that InSAR observations of water level changes in the dynamic wetland environment were generally underestimated.