PhD position (f/m/d) Cross-Sensor Transfer Learning for Long-Term Forest Biomass and Structure [...]

Overview

Do you have a strong interest in remote sensing, forests, and Earth observation? Are you excited about combining next-generation satellite missions with machine learning to reconstruct long-term forest dynamics? If so, we invite applications for a PhD position at the Institute of Photogrammetry and Remote Sensing (KIT-IPF), as part of the International Research Training Group C4LaND.

Organisationseinheit

Institut für Photogrammetrie und Fernerkundung (IPF)

Ihre Aufgaben

Forests play a central role in the land-use nexus by providing carbon storage, biodiversity, and renewable resources, while being increasingly affected by land-use change and climate extremes. Robust, spatially explicit and temporally consistent information on forest biomass and structure is essential for assessing long-term land-use trade-offs and informing integrated modelling and governance frameworks. Recently launched Synthetic Aperture Radar (SAR) satellite missions have been specifically designed to retrieve three-dimensional forest structure and above-ground biomass with high sensitivity, but their observational records are short. In contrast, established SAR missions such as Sentinel-1 offer dense and consistent time series extending back more than a decade, albeit with limited biomass sensitivity.

This PhD position is part of the International Research Training Group C4LaND and 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. The project will be hosted at KIT (Karlsruhe Institute of Technology), Institute for Photogrammetry and Remote Sensing (IPF), under the supervision of Prof. Stefan Hinz. Your Melbourne co-advisor will be Dr. Jagannath Aryal.

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.

Eintrittstermin

October 2026

Ihre Qualifikation

• 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.