Potential of RADARSAT-2 to Improve Ice Thickness Calculations in Remote, Poorly Accessible Areas: A Case Study on the Slave River, Canada

River ice plays an important role in high latitude regions, and ice thickness is an important consideration to local residents as well as hydrologists. Two approaches, measurement (both remotely sensed and direct) and numerical calculations, are common methods used for ice thickness quantification....

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Bibliographic Details
Main Authors: Fan Zhang, Zhaoqin Li, Karl-Erich Lindenschmidt
Format: Article
Language:English
Published: Taylor & Francis Group 2019-03-01
Series:Canadian Journal of Remote Sensing
Online Access:http://dx.doi.org/10.1080/07038992.2019.1567304
Description
Summary:River ice plays an important role in high latitude regions, and ice thickness is an important consideration to local residents as well as hydrologists. Two approaches, measurement (both remotely sensed and direct) and numerical calculations, are common methods used for ice thickness quantification. Measurement of ice thickness using SAR remote sensing approaches is still very challenging due to the low signals in thermal ice and limited penetration capability of C-band on consolidated ice covers. The numerical calculation can provide reasonable ice thickness estimation at a specific location but not the spatial variation. To carry out ice thickness estimations, numerous equations were proposed for numerical calculation. The Ashton equation, incorporating properties of ice and snow cover, was selected to determine the ice thicknesses along the Slave River in Canada. A framework (IceThick-RS) is proposed in this study, which incorporates polarimetric parameters of RADARSAT-2 images (quad-pol mode and C-band) into the ice thickness model. This framework was applied to estimate ice thicknesses along the Slave River. Direct measurements, gathered during the winter of 2014–2015, were used to calibrate the model. The results show that the IceThick-RS framework can be used to carry out river ice thickness estimation, especially in poorly accessible regions.
ISSN:1712-7971