After successful completion of the course, students are able to assess how  Earth observation data can be used to improve the understanding and the modelling of climate and environmental processes, including:

• Knowledge of Earth and climate systems (drivers, water and energy fluxes, feedbacks etc.)
• Water cycle: processes, Earth observation systems, variability, computing and interpreting trends
• Carbon cycle: processes, vegetation properties observable from space, Measurements systems, variability, trends
• Principles of climate modelling
• Principles of the use Earth observation data for model improvement: evaluation and calibration of models
• Software tools: e.g. Python, Jupyter Notebooks

• Knowledge of Earth and climate systems (drivers, water and energy fluxes, feedbacks etc.)
• Water cycle: processes, Earth observation systems, variability, computing and interpreting trends
• Carbon cycle: processes, vegetation properties observable from space, Measurements systems, variability, trends
• Principles of climate modelling
• Principles of the use Earth observation data for model improvement: evaluation and calibration of models
• Software tools: e.g. Python, Jupyter Notebooks

Lectures, group discussions, assignments, programming in Python, data analysis and visualisation

Three assignments will be handed in by the students using Python programming and data analysis.

Introduction to Earth observation, Einführung in die Fernerkundung, Programming in Python