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

Learn Multispectral Earth Observation Applications using ESA's SNAP software and Sentinel-2 imagery

Become an expert on Sentinel-2 image processing!

During this course you will learn how to process ESA's COPERNICUS Sentinel-2 data using SNAP software. The objective of the course is to make the student capable of creating optical-based Earth Observation Applications. To be more precise: 

  1. Vegetation Mapping 
  2. Burn Area Mapping 
  3. Chlorophyll Mapping 
  4. Supervised Land Cover Classification 


are the main applications examined. The Graph Builder module is used extensively during the course in order to create workflows. Example XML workflow files are provided within the course to better help students practice at their own computers! Example Sentinel-2 images are also included in the course, so you can test the contents of course presented directly to your computer! By the end of the course can claim your blockchain based digital certificate to better prove your skills! Best part? It come with price of the course, no hidden costs. Just enroll to the course, successfully finish all lessons in the course, and claim you certificate.

This remote sensing online course is what you need to learn how to process Sentinel 2 satellite images!

Learn to create multispectral applications with SNAP and capture the opportunities

According to the "Study to examine the socio-economic impact of Copernicus in the EU", more than 10.000 related jobs will be needed! Become an expert on SNAP now and tap to the fast growing Earth Observation Downstream market!
Enroll now and advance your skills

Course curriculum

  1. 01

    • Lesson 1: Getting the Sentinel-2 images to learn multispectral Earth Observation Applications

    • Lesson 2: Dataset preparation

    • Lesson 3: Sentinel-2 Dataset used during the course

    • Quiz 1
  2. 02

    • Lesson 4: Atmospherically correct Sentinel-2 using Sen2Cor

    • Lesson 5: Comparing non-atmospherically corrected (L1C) with atmospherically corrected (L2A) Sentinel-2 images

    • Lesson 6: Resampling Sentinel-2 atmospherically corrected images

    • Quiz 2
  3. 03

    • Lesson 7: Sampling Spectral Signatures

    • Lesson 8: Leaf Area Index (LAI) calculation

    • Lesson 9: Leaf Area Index (LAI) Evolution time

    • Lesson 10: Improved Masking before Mapping

    • Lesson 11: Normalized Burn Ratio Index

    • Lesson 12: Burn Area Mapping Workflow

    • Lesson 13: Burn Area Mapping workflow XML file to use

    • Quiz 3
  4. 04

    • Lesson 14: Chlorophyll Mapping
  5. 05

    • Lesson 15: Supervised Classification Part 1

    • Lesson 16: Supervised Classification Part 2

    • Lesson 17: Supervised Classification Part 3
  6. 06

    • Lesson 18: Introduction to the purpose of the current chapter

    • Lesson 19: Getting the data for the skill test

    • Lesson 20: Preparing the datasets

    • Lesson 21: Open the Sentinel-2 multispectral image

    • Lesson 22: Crop the reference classification dataset

    • Lesson 23: Open both Sentinel-2 and reference classification map in SNAP

    • Lesson 24: Remember the resampling operator before collocating the data

    • Lesson 25: Collocate or stack multispectral image with reference map in a single file

    • Lesson 26: Resample and subset step using the Graph Builder

What will you learn?

  • Sentinel-2 preprocessing to start building any Earth Observation application

  • Vegetation mapping with Sentinel-2

  • Chlorophyll mapping with Sentinel-2 coastal images

  • Atmospherically correct Sentinel-2 images

  • Burn area mapping using bi-temporal Sentinel-2 images

  • Supervised (Land cover) classification of Sentinel-2 images

Any prerequisites?

  • Basic knowledge of Earth Observation

  • Be familiar with ESA Sentinels Application Platform (SNAP) software

  • A simple laptop or desktop computer to follow the course's steps

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

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