PhD Position - SAR Remote Sensing - Forest Biomass and Structure Estimation
Karlsruhe Institute of Technology (KIT)
In close partnership with society, KIT develops solutions for urgent challenges – from climate change, energy transition and sustainable use of natural resources to artificial intelligence, sovereignty and an aging population. As The University in the Helmholtz Association, KIT unites scientific excellence from insight to application-driven research under one roof – and is thus in a unique position to drive this transformation. As a University of Excellence, KIT offers its more than 10,000 employees and 22,800 students outstanding opportunities to shape a sustainable and resilient future.
Homepage: https://www.kit.edu/english/
Earth and Space Science Informatics (ESSI)
Energy, Resources and the Environment (ERE)
This Joint-PhD position at KIT (Germany) and University of Melbourne (Australia) is part of the International Research Training Group C4LaND. It focuses on developing transfer learning approaches that link forest above-ground biomass and structure estimates from next-generation, biomass-oriented SAR missions to long-term SAR archives, thereby enabling spatially explicit reconstruction of forest dynamics at least back to the beginning of the Sentinel-1 era.
Lines of research include:
- Developing cross-sensor transfer learning frameworks based on multi-level SAR observables for above-ground biomass estimation by exploiting polarimetric SAR features across sensor generations and enriching them with higher-order structural information from Polarimetric InSAR and Tomographic SAR where available.
- Exploration of multi-modal and multi-model support using optical and hyperspectral data to improve robustness and generalizability of transfer learning between SAR sensors.
- Sensor-aware uncertainty characterization and error transfer, with emphasis on decomposing the error budget and estimating loss of precision associated with products derived from established missions compared to new biomass missions.
- Validation across long-term forest observatories in multiple regions, including Europe (TERENO), Australia (CSIRO Permanent Rainforest Plots), and potentially Brazil, ensuring transferability across forest types, climatic zones, and land-use contexts relevant to C4LAND.
Requirements
- An above-average M.Sc. degree (or equivalent) in remote sensing, environmental sciences, computer science, forestry or a related field.
- Strong background and interest in Earth observation, remote sensing of forests and SAR data processing
- Experience with machine learning, domain adaptation, or transfer learning methods is highly desirable.
- Excellent communication skills and enthusiasm for working in an international and multidisciplinary research environment.
- Fluency in English, written and spoken
- Willingness to undertake a one-year research placement at the University of Melbourne, Australia and comply with formalities at both institutions.
For further information see also the C4LaNd project page: https://c4land.earth/index.html