The textbook you need to learn how to map, identify and quantify oil spills in marine ecosystems using Earth Observation.

How to map oil spills from Space?

A big challenge to overcome using hyperspectral technology

Enrol to learn how to use Earth Observation to preserve the marine ecosystem from such threats

This textbook remote sensing online course will guide you to learn how to use hyperspectral and multispectral remote sensing images to:

  1. Map the extent of an oil spill event
  2. Identify the type of oil spilled
  3. Estimate the thickness of the oil spill in order to measure spilled oil volume


To accomplish these, two methodologies are presented presented based on satellite earth observation images. Also experiments using ground spectral measurements are used in order to build a Spectral Library and develop a model for thickness estimation.

This textbook course is ideal for students and professionals either in the EO&GIS domain or in the environmental monitoring domain.


The power of Spectral Unmixing

In oil spill mapping! While Synthetic Aperture RADAR (SAR) are used operationally to map oil spills, optical remote sensing and especially hyperspectral remote sensing can not only map, but also identify the oil spill type and estimate the spill thickness over it's entire extent!
Enroll now and advance your skills

Course curriculum

  1. 01

    • Lesson 1: Understanding the problem

    • Lesson 2: Current Approaches for oil spill mapping with Earth Observation (Part 1)

    • Lesson 3: Current Approaches for oil spill mapping with Earth Observation (Part 2)
  2. 02

    • Lesson 4: Oil spill mapping workflow

    • Lesson 5: Oil spill mapping workflow graph
  3. 03

    • Lesson 6: Datasets used to evaluate the methodology
  4. 04

    • Lesson 7: On ASTER Imagery (Part 1)

    • Lesson 8: On ASTER Imagery (Part 2)

    • Lesson 9: On MODIS Imagery (Part 1)

    • Lesson 10: On MODIS Imagery (Part 2)

    • Lesson 11: SpecTIR Imagery
  5. 05

    • Lesson 12: Drafting conclusions on the application results

    • Further Reading
  6. 06

    • Lesson 13: Integrated oil spill mapping workflow

    • Lesson 14: Integrated oil spill mapping workflow graph
  7. 07

    • Lesson 15: Why to examine the relative radiometric normalization concept?

    • Lesson 16: Introducing the Relative Radiometric Concept (Part 1)

    • Lesson 17: Introducing the Relative Radiometric Concept (Part 2)

    • Lesson 18: Spectral Indices that are used to compare spectrums

    • Lesson 19: The Algorithm: Normalized Proximity and Similarity Methodology (NPSM) [Part 1]

    • Lesson 20: The Algorithm: Normalized Proximity and Similarity Methodology (NPSM) [Part 2]

    • Lesson 21: Data & Experiments to evaluate NPSM

    • Lesson 22: Evaluation Results of NPSM

    • Lesson 23: Evaluation Results of NPSM: Multispectral Images

    • Lesson 24: Evaluation Results of NPSM: Hyperspectral Images

    • Lesson 25: Concluding Remarks for the Relative Radiometric Normalization Process

    • References and Further Reading
  8. 08

    • Lesson 26: Application of the Integrated oil spill mapping workflow

    • Lesson 27: Results of the Integrated oil spill mapping workflow (Multispectral)

    • Lesson 28: Resutls of the Integrated oil spill mapping workflow (Hyperspectral)
  9. 09

    • Lesson 29: Drafting concluding remarks
  10. 10

    • Lesson 30: Introduction to the concept

    • Lesson 31: Methodological Approaches [Part 1]

    • Lesson 32: Methodological Approaches [Part 2]

    • Lesson 33: What data we need to approach the problem?

    • Lesson 34: Unmixing methods to estimate the oil spill thickness

    • Lesson 35: Oil Spill thickness estimation [Part 1]

    • Lesson 36: Oil Spill thickness estimation [Part 2]

    • Lesson 37: Concluding Remarks for Oil spill thickness estimation

    • References and Further Reading

What will you learn?

  • Hyperspectral oil spill mapping

  • Relative Radiometric Normalization

  • Oil spill type identification with hyperspectral data

  • Spectral Unmixing

  • Oil spill thickness estimation

  • Spectral Libraries building and updating

Any Prerequisites?

  • Practically none.

Student Profile?

  • Under/post graduate students

  • Professionals and Companies

  • Master students and PhD candidates

  • Researchers and Academics

Some more information

  • Certificates of Completion

    After you successfully finish the course, you can claim your Certificate of Completion with NO extra cost! You can add it to your CV, LinkedIn profile etc

  • Available at any time! Study at your best time

    We know hard it is to acquire new skills. All our courses are self paced.

  • Online and always accessible

    Even when you finish the course and you get your certificate, you will still have access to course contents! Every time an Instructor makes an update you will be notified and be able to watch it for FREE

About your Instructor

Dimitris Sykas is a Remote Sensing and Surveying Engineer with a Ph.D. in hyperspectral remote sensing from the National Technical University of Athens (2016). He has spent fifteen years turning satellite data into operational products: at the NTUA Remote Sensing Laboratory, at Planetek Hellas as Senior EO Engineer, at Neuropublic building machine-learning models for smart farming and CAP monitoring, as a postdoctoral researcher on deep learning for agricultural Earth observation at the National Observatory of Athens, and as CTO of the cloudeo group. In 2024 he co-founded GOVADI, an Athens-based company building sovereign, on-premises AI systems for the Greek public sector and large enterprises, where he leads the technical direction of its products: Greek speech-to-text, retrieval-augmented generation with local models and embedding stores, document OCR and PII anonymisation. He has taken part in more than 25 funded EU and ESA projects, has authored over 23 peer-reviewed publications in remote sensing, and holds an international patent in hyperspectral data compression. He founded GEO University to bring hands-on GIS and Earth observation training online.

Dimitris Sykas

Remote Sensing Engineer, Ph.D. · Co-founder and Director, GOVADI · Founder, GEO University

Check these out

  • Multispectral Earth Observation Applications using ESA Sentinel Application Platform

    Learn how to process Sentinel-2 images and build Earth Observation Applications

    Free

    Enroll Now

  • Glacier Mapping using Earth Observation Satellites

    Learn how to map glaciers and snow cover with Sentinel-2 and SNAP

    Free

    Enroll Now

  • Google Earth Engine Training For Earth Observation (EO) Applications

    Become an Expert on GEE. Extend your GIS and EO processing to a planetary level!

    Free

    Enroll Now