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Article
Detecting and Quantifying Oil Slick Thickness by Thermal Remote Sensing: A Ground-Based Experiment
Remote Sensing of Environment
  • Yingcheng Lu, Nanjing University
  • Wenfeng Zhan, Nanjing University
  • Chuanmin Hu, University of South Florida
Document Type
Article
Publication Date
8-1-2016
Keywords
  • Oil spill,
  • Oil slick,
  • Thickness,
  • Thermal remote sensing,
  • Brightness temperature,
  • Diurnal temperature cycle model
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.rse.2016.04.007
Disciplines
Abstract

Thermal remote sensing has been used to detect oil slicks, yet estimation of slick thickness has largely remained unfeasible, mainly because the optimal detection time during a day, the minimum detectable thickness (MDT), and the relationship between the thermal response and thickness all remain largely unknown. Here, a ground-based experiment is used to address some of these uncertainties. The experiment measured the brightness temperatures (BTs) of oil slicks (with different known thicknesses) and oil-free water as a function of time of the day for both clear and turbid waters. The BT differences (BTDs) between oil slicks and oil-free water were further simulated using a diurnal temperature cycle (DTC) model. The results demonstrate that: (1) for an oil slick that is in thermal equilibrium with the water, the optimal time for thermal detection is around local noon (positive BTDs) with midnight (negative BTs) being the next best time; the periods shortly before sunrise and after sunset are not suitable for the thermal detection of oil slicks; (2) a better linear relationship between slick thickness and BTD is found during daytime than night-time and the type of background water also plays a role in this; and (3) assuming a detection limit of 0.3 °C for a thermal sensor, the MDT at noon is approximately 40 μm for both clear and turbid waters, while for other times of the day the MDT is higher (e.g., 75 and 150 μm for clear and turbid waters, respectively, at midnight). Detection limits for several existing satellite sensors and for other observation scenarios are also discussed.

Citation / Publisher Attribution

Remote Sensing of Environment, v. 181, p. 207-217

Citation Information
Yingcheng Lu, Wenfeng Zhan and Chuanmin Hu. "Detecting and Quantifying Oil Slick Thickness by Thermal Remote Sensing: A Ground-Based Experiment" Remote Sensing of Environment Vol. 181 (2016) p. 207 - 217
Available at: http://works.bepress.com/chuanmin_hu/107/