Research of the Environmental Remote Sensing Group

We develop and apply satellite data sets and derived products to observe, analyse, model and predict changes in vegetation, fires and the related impacts on the carbon and water cycle in ecosystems. We use and develop data-driven methods such as time series analysis and machine learning as well as process-based environmental models and model-data integration techniques.

The research in the EnvRS group is clustered in four inter-linked research topics. The four research topics cover different scales (local to global) and make use of different approaches of data analysis in remote sensing (e.g. field measurements, physical modelling, radiative  transfer modelling, model inversion, time series analysis, machine learning, deep learning):

• Microwave remote sensing of water-forest interactions
• Integrative remote sensing for fire science and management
• Remote sensing for carbon cycle model-data integration
• High-resolution landscape mapping with deep learning