Earth System Data Science and Remote Sensing

Global environmental challenges are on the rise. These challenges include climate change and the increasing frequency of extreme events as well as rapid land transformations and the resulting biodiversity crisis. Global data streams, ongoing digitisation and novel methodological approaches offer unique opportunities to make a contribution in the search for solutions to these problems. One novel approach is data science, consisting of the combination of domain knowledge, coding skills, and quantitative methods. Another one is remote sensing, which provides Earth observation data (e.g. optical, Lidar, radar) at multiple observation scales suitable for monitoring a wide range of processes. Since Earth system sciences are a data-rich domain, such approaches become particularly powerful. Leipzig University is very strong in all relevant aspects. Domain knowledge is available in the areas of geoecology, biodiversity research, meteorology, eco-hydrology, and related fields. Remote sensing is covered through the Remote Sensing Centre for Earth System Research, and data science is very high on the agenda of various institutions. Leipzig University therefore offers perfect preconditions for bringing these aspects together in one dedicated study programme: the MSc “Earth System Data Science and Remote Sensing”. Our aim is to train the next generation of Earth system scientists that possess a unique set of skills balancing domain knowledge in Earth and environmental sciences, coding skills and quantitative methods with an emphasis on state of the art remote sensing technology. We combine an in-depth methodological education with the opportunity to specialise in a distinct domain of the Earth system. Our students will thus become the method experts in an individually chosen field of Earth and environmental sciences. Overview of topics covered in the curriculum (total of 120 ECTS in two years): 20 ECTS remote sensing (multi- and hyperspectral remote sensing, radar, Lidar and reflectance spectroscopy, field techniques for ground truthing, data products) 20 ECTS data science (machine learning and artificial intelligence, spatio-temporal data analysis, geostatistics) 20 ECTS specialisation in a field of application (e.g. physical geography and geoecology, biodiversity research, meteorology and climate science, geology and others) 10 ECTS soft skills (scientific writing, research data management) 10 ECTS research internship (national or international) 10 ECTS basic introductions to remote sensing, data science and the Earth system 30 ECTS Master's thesis The course contents are taught in single, organisationally independent units (modules). Modules contain clearly defined areas of knowledge that have a factual or thematic relationship. Modules might contain various teaching units, e.g. lecture (L), seminar (S) or hands-on training (P) and are concluded by a final exam. Modules are rated by their teaching load in credit points (ECTS); one credit point corresponds to an average workload of 30 hours. Most modules in this MSc programme have a workload of 5 ECTS. Basic introductions are a mandatory elective module (WP, two out of three introductions must be selected). Method modules and soft skills are mandatory (P) and modules targeting fields of application are elective (W).