Review of River Ice Observation and Data Analysis Technologies

This paper provides a comprehensive review of the available literature on the observation and characterization of river ice using remote sensing technologies. Through an analysis of 200 publications spanning from 1919 to June 2024, we reviewed different observation technologies deployed on in situ, aerial and satellite platforms for their utility in monitoring and characterizing river ice covers. River ice information, captured by 51 terms extracted from the literature, holds significant value in enhancing infrastructure resilience in the face of climate change. Satellite technologies, in particular the multispectral optical and multi-polarimetric synthetic aperture radar (SAR), provide a number of advantages, such as ice features discrimination, better ice characterization, and reliable delineation of open water and ice, with both current and upcoming sensors. The review includes data analysis methods employed for the monitoring and characterization of river ice, including ice information retrieval methods and corresponding accuracies. The need for further research on artificial intelligence and, in particular, deep learning (DL) techniques has been recognized as valuable for enhancing the accuracy of automated systems. The growing availability of freely available and commercial satellites, UAVs, and in situ data with improved characteristics suggests significant operational potential for river ice observation in the near future. Our study also identifies gaps in the current capabilities for river ice observation and provides suggestions for improved data analysis and interpretation.

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Institutions

Publication Details

Journal
Hydrology
Published
2024-08-20
DOI
https://doi.org/10.3390/hydrology11080126
Citations
13
Primary Topic
Arctic and Antarctic ice dynamics
Type
article
Field-Weighted Citation Impact
4.89

Funders

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article

Review of River Ice Observation and Data Analysis Technologies

Paul Barrette, Amir Ali Khan, Thomas Puestow, Igor Zakharov et al.
13 citations
Hydrology
Arctic and Antarctic ice dynamics
4.89
article

Review of River Ice Observation and Data Analysis Technologies

Paul Barrette, Amir Ali Khan, Thomas Puestow, Igor Zakharov, Robert Briggs
article en
13 citations

Abstract

This paper provides a comprehensive review of the available literature on the observation and characterization of river ice using remote sensing technologies. Through an analysis of 200 publications spanning from 1919 to June 2024, we reviewed different observation technologies deployed on in situ, aerial and satellite platforms for their utility in monitoring and characterizing river ice covers. River ice information, captured by 51 terms extracted from the literature, holds significant value in enhancing infrastructure resilience in the face of climate change. Satellite technologies, in particular the multispectral optical and multi-polarimetric synthetic aperture radar (SAR), provide a number of advantages, such as ice features discrimination, better ice characterization, and reliable delineation of open water and ice, with both current and upcoming sensors. The review includes data analysis methods employed for the monitoring and characterization of river ice, including ice information retrieval methods and corresponding accuracies. The need for further research on artificial intelligence and, in particular, deep learning (DL) techniques has been recognized as valuable for enhancing the accuracy of automated systems. The growing availability of freely available and commercial satellites, UAVs, and in situ data with improved characteristics suggests significant operational potential for river ice observation in the near future. Our study also identifies gaps in the current capabilities for river ice observation and provides suggestions for improved data analysis and interpretation.

HydrologyVol. 11(8)
Centre For Cold Ocean Resources Engineering (CA), National Research Council Canada (CA)
Government of Canada
Openalex Percentile: Top 4%
Arctic and Antarctic ice dynamics
4.89
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