Interested in learning how to manipulate Earth Observation data? The Applied Remote Sensing Group at Cranfield University will be hosting a training short course in Earth Observation (EO) data science from Monday 15th to Friday 19th May 2023. The course is funded by the Natural Environment Research Council (NERC) and is offered at no cost to the selected participants (including on-site accommodation, subsistence, course materials and instruction).

Earth Observation (EO) plays a vital role in a wide range of environmental research areas such as monitoring biodiversity, wildlife populations, deforestation, sea level rise, greenhouse gas emissions, glacier retreat, and changes in land use. With the growing number of EO satellite operators and advancements in computing algorithms, the importance of EO data is expected to increase in both research and in achieving sustainability, net zero, and climate targets.

EO data and automated analysis techniques are becoming increasingly important, providing researchers and practitioners with valuable datasets to tackle environmental challenges using a whole systems approach. Participants in this course will gain hands-on experience with data extraction, manipulation, and visualisation techniques, which they can apply to various use cases.

At a glance

  • Dates
    • 15 - 19 May 2023
  • DurationFive days
  • LocationCranfield campus
  • Cost

    Free for selected participants.

    Costs for accommodation, subsistence, course materials and instruction are covered by the training grant.

Course structure

The course format will include in-person lectures, hands-on PC labs, group work and participant presentations.

What you will learn

After taking the course, participants will be in a position to identify and harness EO data to advance their research.

On successful completion of this course, you will be able to:

  • Describe types and principles of satellite EO and how electromagnetic radiation as related to satellite EO interacts with the earth’s surface and the atmosphere,
  • Identify key planetary EO cloud back-ends (e.g., Google Earth Engine, Microsoft Planetary Computer, AWS) and discover their associated data catalogues including datasets from the Google Earth Engine Community,
  • Use Python APIs to access Google Earth Engine and Microsoft Planetary Computer EO cloud data,
  • Create and customise scripts using Python API to access and manipulate collections of EO data in Google Earth Engine,
  • Extract and visualise time series of spectral indices over a given area such as vegetation, water, soil and snow indices,
  • Build EO data workflows integrating data from EO cloud back-ends.

Core content

  • Day 1: Introduce satellite EO types and principles including Optical, Thermal and Radar Remote Sensing,
  • Day 2: Explore key EO cloud back-ends and associated data catalogues focussing on Google Earth Engine and Microsoft planetary computer. This will include hands-on sessions on data discovery using Python APIs and JupyterLab,
  • Day 3: Manipulate EO data collections: hands-on sessions focussing on progressively developing a workflow on how to get image collections from a given satellite sensor, apply cloud algorithms and other processing functions, and aggregate data spatially and temporally,
  • Day 4: Develop workflows to extract time series data of various spectral indices over a given area/grid cell. We will cover various indices such as vegetation, water, soil and snow indices,
  • Day 5: Develop custom workflows to integrate datasets from key EO cloud platforms.

Who should attend

The course is intended for NERC- and UKRI-funded PhD students and early career researchers. Priority will be given to PhD students and postdoctoral research associates across environmental science who have had limited exposure to Earth Observation training. To ensure impartiality, applications will be anonymised and selection will be made based on training needs

To optimise your experience in the course, we suggest that you have some prior programming experience in Python. For those without a background in Python, introductory materials to Python will be provided before the start of the course.

We are committed to promoting diversity and inclusivity and encourage applications from individuals from under-represented or disadvantaged backgrounds, including ethnic minorities, students with disabilities, caring responsibilities, and first-generation degree-seekers.

A limited number of places is available (20 places). Registration closes on Friday 10 March 2023 and your place will be confirmed in early April. For enquiries please email the course lead at:

Accommodation options and prices

Accommodation is provided at the Cranfield Management Development Centre - Costs for accommodation, subsistence, course materials and instruction are covered by the training grant. 

Location and travel

Cranfield University is situated in Bedfordshire close to the border with Buckinghamshire. The University is located almost midway between the towns of Bedford and Milton Keynes and is conveniently situated between junctions 13 and 14 of the M1.

London Luton, Stansted and Heathrow airports are 30, 90 and 90 minutes respectively by car, offering superb connections to and from just about anywhere in the world. 

For further location and travel details

Location address

Cranfield University
College Road
MK43 0AL

Read our Professional development (CPD) booking conditions.