Course description
In recent decades satellite remote sensing became more and more important as those technologies provide the basic data set for analyzing vitally important problems like air pollution, the Ozone hole and Global Warming. This course aims to lay down the basic knowledge of atmospheric measurement instrumentation and data processing techniques, and to give the student an insight in the application of advanced instrumentation and remote sensing systems for environmental assessments. The course will start with a historic overview of remote sensing instruments followed by a description of the underlying physics (electromagnetic radiation, electromagnetic spectrum used for remote sensing, interaction of radiation with matter, etc). The next section will introduce different remote sensing instruments. The measurement principles covered in this course range from camera systems, multi- and hyperspectral imaging, spectrometers, radar (radio detection and ranging) and lidar (light detection and ranging) systems and will describe the different orbits satellites use for monitoring the Earth. Students will learn about satellite measuring principles and what kind of data they produce. In this course the students will also learn about how to process satellite data using digital image processing. Part of this course will be in the computer lab (or working on home PCs or laptops if the circumstances require remote teaching) and the students will be introduced to Python programming techniques in order to be able to solve some basic image processing tasks (like visualizing satellite imaging data or color separation of multi spectral satellite images, etc.)

Learning outcomes of the course
By completing this course, the students will have acquired relevant knowledge in satellite remote sensing and gained the skills to be able to:
• recognize basic laws and principles that form the science of remote sensing
• identify underlying physical principles of remote sensing instrumentation
• choose the right instrument for measuring environmental parameters and identify design and implementation issues of spectral imaging instruments
• use computational methods for analyzing digital imaging data.
• apply their acquired knowledge of digital image processing in a group environment to solve programming problems in a team effort.

Teaching and evaluation methods
An important aspect of monitoring the students’ progress will be quizzes and group discussions at the end of each lecture reflecting on the topics covered. About 2-3 tutorial questions will be answered individually via Moodle (or some other software) so that the students can directly get the correct answers. One or two more questions will be discussed in groups with each group presenting their findings to class afterwards. Another aspect of this class are small programming projects that are again to be solved in a group effort. After the students learned basic image processing tools in Python, each group can choose a different remote sensing topic (e.g. visualizing a volcanic eruption, detecting clouds in satellite images, analyzing the extent of the ozone hole, etc.) using imaging data that will be provided. Each group will present their project to the class. As individual assignments, each student has to choose a satellite instrument from a list that will be provided as well (e.g. Meteosat, OMI, TROPOMI, GOME, SCHIAMACHY, Landsat, CALIPSO, MODIS, etc.) and research its application and data products. Each student will have to write a report about their findings which will be graded based on completeness and understanding of the underlying measurement concepts as explained in class.
The mid-term exam and the final exam will form another part of the evaluation.

The contribution of the different evaluation methods is as follows:

Class participation (tutorial questions and group discussions towards the end of each lecture, ≈1% each lecture for 24 lectures) 24%
Mid-term exam 20%
Group presentations of satellite remote sensing data processing projects 20%
Report about a chosen satellite instrument (each student chooses a different instrument) 16%
Final exam 20% 

Bibliography
Recommended reading:
Our Changing Planet - The View from Space by Michael D. King, Claire L. Parkinson, Kim C. Partington, Robin G. Williams, 2007, Cambridge University Press 978-0-521- 82870-3 (ISBN)
The lecture slides will be provided on Moodle.

Last updated: May 11, 2023