Towards a framework for oil classification during oil spills in Arctic open water using microwave remote sensing

Arctic warming is extending the open water period, increasing interest in oil exploration, extraction, and transport through the extended shipping season, consequently raising the potential for an oil spill in the Arctic. Remote sensing for oil detection and characterization is at the forefront of oil spill response efforts. Here, we show that the density of an oil is related to its complex dielectric constant (CDC), a governing factor of microwave remote sensing. The relationship between oil density (ρ) and the real part of the oil CDC ( ε ′ ) as a function of composition and temperature was thoroughly investigated and a simple linear equation to describe the two variables was attained ( ε ′ = 2.0396 ρ + 0.4961 ). Based on this oil density-CDC relationship, a framework was built for radar technologies to classify spilled oil as light, medium, heavy, or extra-heavy using forward and inverse modelling of the Normalized Radar Cross-Section to better inform responders on how to approach the oil spill and what mitigation procedures are most appropriate. • Oil density and the real part of CDC have a tangible linear relationship. • The real part of CDC changes by a factor of two compared to oil density. • Radar can classify spilled oil based on the oil density-CDC relationship. • C-band radar is sensitive to oil type classification for open water and ice scenarios. • Radar sensitivity to oil type classification depends on incidence angle and frequency.

Authors

Institutions

Publication Details

Journal
Journal of Sea Research
Published
2026-04-13
DOI
https://doi.org/10.1016/j.seares.2026.102698
Primary Topic
Oil Spill Detection and Mitigation
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Towards a framework for oil classification during oil spills in Arctic open water using microwave remote sensing

Nasima Chorfa, Jens K. Ehn, Dustin Isleifson, Gary A. Stern et al.
Journal of Sea Research
Oil Spill Detection and Mitigation
article

Towards a framework for oil classification during oil spills in Arctic open water using microwave remote sensing

Nasima Chorfa, Jens K. Ehn, Dustin Isleifson, Gary A. Stern, Alexander S. Komarov, Durell S. Desmond, Mahdi Zabihi Mayvan, Nhu Duong, Feiyue Wang, Aura Diaz
article en

Abstract

Arctic warming is extending the open water period, increasing interest in oil exploration, extraction, and transport through the extended shipping season, consequently raising the potential for an oil spill in the Arctic. Remote sensing for oil detection and characterization is at the forefront of oil spill response efforts. Here, we show that the density of an oil is related to its complex dielectric constant (CDC), a governing factor of microwave remote sensing. The relationship between oil density (ρ) and the real part of the oil CDC ( ε ′ ) as a function of composition and temperature was thoroughly investigated and a simple linear equation to describe the two variables was attained ( ε ′ = 2.0396 ρ + 0.4961 ). Based on this oil density-CDC relationship, a framework was built for radar technologies to classify spilled oil as light, medium, heavy, or extra-heavy using forward and inverse modelling of the Normalized Radar Cross-Section to better inform responders on how to approach the oil spill and what mitigation procedures are most appropriate. • Oil density and the real part of CDC have a tangible linear relationship. • The real part of CDC changes by a factor of two compared to oil density. • Radar can classify spilled oil based on the oil density-CDC relationship. • C-band radar is sensitive to oil type classification for open water and ice scenarios. • Radar sensitivity to oil type classification depends on incidence angle and frequency.

Journal of Sea ResearchVol. 211
Environment and Climate Change Canada (CA), University of Manitoba (CA)
Genome Canada, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 25%
Oil Spill Detection and Mitigation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.