Single-Course English 5 ECTS

Earth observations for monitoring changes (EO4Change)

Overall Course Objectives

The Earth is changing, and these changes can clearly be seen from space. Here, we will introduce some of the different satellite-based Earth observation (EO) datasets available, emphasizing real-life use examples. After the introduction, the students (in groups) will work more in-depth with a specific dataset to observe the phenomena they find most interesting (e.g., deforestation, floods, drought, ice thickness). Here, we need to identify relevant satellite missions and spatio-temporal data requirements before diving into the method development, implementation and analysis. Examples of such projects could be, e.g., the use of NASA’s ICESat-2 green-laser mission to determine Amazon deforestation, monitor drought across continents with the EU Sentinel missions, or use Sentinel-2 to see the biological activity in the world’s oceans. These are just some examples of the data we aim to have the students be able to grasp and analyze. Through this course, we aim for the students to be able to navigate the growing stream of EO data freely available from different agencies and make use of them for society.

Learning Objectives

  • describe the different EO platforms relevant to terrestrial/climate monitoring and possible technical challenges
  • explain the most important error sources
  • apply the most suitable method to process a specific EO-dataset
  • choose the most suitable EO-dataset for studying a given phenomenon
  • analyze the EO dataset in context of monitoring a given phenomenon
  • judge if the chosen phenomena can successfully be observed by the EO dataset
  • defend and argue for the choice of method and data
  • develop a draft plan for the analyzed EO dataset as an operational service within e.g. Copernicus.

Course Content

• General insight into EO
• Case driven introduction to specific applications of EO data
• Introduction to data management in Python, e.g. EO-learn
• Literature search in connection with algorithm selection.

Recommended prerequisites

10020/10022/10024/10033/30350/30752, A basic knowledge of programming is expected (Matlab, R, Python or similar).

Teaching Method

Lectures and data processing


See course in the course database.





3 weeks




DTU Lyngby Campus

Course code 30574
Course type Candidate
Semester start Week 23
Semester end Week 26
Days Mon-fri 8:00-17:00

7.500,00 DKK