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Using SAR and optical data to detect and track surface oil

By Valborg Byfield

National Oceanography Centre, Southampton

MERIS image of surface oil from the Deepwater Horizon oil spill.
MERIS image from the Gulf of Mexico 2010.
Click for larger image and explanation

Synthetic aperture radar (SAR) is now commonly used for operational oil spill monitoring. During major spills SAR data from different satellites give an overview of the areas affected and provide information on the direction in which surface oil is drifting. SAR is also used to monitor illegal discharges from ship traffic or offshore operations. In many areas this has helped to reduce oil pollution.

In regions that are relatively cloud free data from optical sensors such as MERIS and MODIS are increasingly used. Combining SAR and optical data makes detection of small spills more reliable and can provide additional information for use in oil spill response during larger spills.

IMPORTANT NOTE: This lesson requires Bilko 3.4 from February 2013 or later, as earlier versions of the software can not open and display all data as described in the lesson.


The most reliable information about oil spills is obtained by combining data from a range of different sensors. SAR and optical data are both used for detecting and mapping surface oil; SAR is the most established because of its ability to 'see' through clouds. By combining the two sensor types it may also be possible to obtain information about relative thickness of the oil, but unless the spill is very large, this requires better spatial resolution than the 250-300m pixels available from MODIS and MERIS. Optical data may also give information about dispersed oil, but this is difficult to distinguish from suspended sediment and coloured dissolved organic matter (CDOM), and requires detailed baseline information from the region. Hence this capability is not yet exploited in operational monitoring of oil spills, although it has been used in post-spill impact assessments.

Detecting the oil and knowing where it is (or was at the time of satellite overpass) is only half the story. Equally important is the ability to predict how a spill will evolve, and where the oil is headed. This allows responsible authorities to direct clean-up activities and protection of sensitive areas and industries. Satellite data are therefore commonly used in conjunction with numerical models capable of predicting how the spill will evolve, and where it will be transported by wind and currents. Satellite data for input into oil spill models include wind and waves from altimetry, scatterometry and SAR, as well is information about current speed and direction derived from altimetry, SST, optical sensors and SAR data.

Lesson Overview

This lesson shows how ASAR Wide Swath (resolution 75m) and MERIS Full Resolution (300m+) top of atmosphere (TOA) data may be used to detect and characterise surface oil. The images used in the lesson are taken from the Gulf of Mexico oil spill in spring 2011. However, similar images exist from other spills; we have provided some of these as alternatives to those used in the lesson.

Aim and objectives

At the end of the lesson you should be able to

Lesson content

The lesson is divided into five main sections:

  1. Detecting surface oil using SAR data.
  2. Detecting surface oil in optical images and obtaining information on oil thickness.
  3. Combining SAR and optical data to obtain more information.
  4. Combining SAR and optical data to obtain more information.
  5. Modelling oil spill transport and evolution (optional)

Data and tools for this lesson

Files needed to complete the lesson activities

All data and tools needed to complete the lesson activities are listed below. We also provide additional data that may be used to complement or extend the lesson (see alternative and related data sets).

Level 1B Envisat data

The images have been compressed using bzip2 compression to save space. Bilko will open these on the fly, so there is no need to uncompress the images first.

Synthetic aperture radar (SAR) data:
Optical L1B (top of atmosphere) data from EnviSat MERIS:

Bilko tools

  • DBcalculation.frm: Bilko formula to calculate and approximate radar cross-section image in dB (the logarithm of the signal return).
  • asa_wsm_crosstrack_correction_20100429.frm: Bilko formula to provide an approximate correction for changes in radar backscattering occurring as a result of differences in the incidence angle of the radar signal relative to the sea surface.
  • mer_toa_radiance_calc.frm: Bilko formula uses the scaling factors from the MERIS metadata to calculate radiance from the digital values in each MERIS band.
  • mer_20100429_contrast_calc.frm: Bilko formula to calculate local contrast based on TOA radiance from "clean water" regions of interest (ROI) near the location of the oil platform. The result is only valid for areas with the same illumination conditions and background water properties as those found in the two ROIs.
  • anomaly.pal and anomaly2.pal: Bilko palette documents designed to work with anomaly and contrast images. When applied with a suitable linear stretch, where the minimum is negative and the maximum positive, and both Min and Max have the same absolute value, negative anomalies will be blue and positive anomalies red. Areas of no contrast / close to the normal/climatology value will be white. The two palettes are slightly different. Which one to use is a question of personal preference.
  • Oil spill demos for Google Earth (optional)

    RSMAS_oil_spill_modelling_demos: This folder contains a number of kmz files needed for the optional Section 5 of the lesson. They have been produced by the University of Miami's Carthe oil spill team from model output for a period in summer 2012, when conditions were similar to those experienced during the Deepwater Horizon spill. The work is part of an on-going effort to improve the ability of models to predict the movement of oil released from the sea floor in deep water. NB! To view the model output you will need Google Earth installed on your computer. See for information on how to download and use Google Earth.
    For more about Carthe, see, or

    Downloading the lesson

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