Title

Detecting and Quantifying Oil Slick Thickness by Thermal Remote Sensing: A Ground-Based Experiment

Document Type

Article

Publication Date

8-2016

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

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.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

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

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