ir. Çağlar Küçük

Çağlar KüçükResearch team: Hydrology and Climate

As a result of my passion in nature and geography, I started to build a natural sciences oriented scientific career on my engineering background. During my masters period, I worked as a research assistant on a project about monitoring paddy-rice fields with remote sensing and applied some machine learning algorithms to SAR data. Afterwards, I worked as a trainee researcher in Joint Research Centre of the European Commission on monitoring tree stress in a region with an exotic pest outbreak, using optical remote sensing and statistical analysis. Finally, I started working as a PhD candidate on Max Planck Institute for Biogeochemistry (MPI-BGC) on a project called “Ecohydrology from Space”, trying to disentangle the vegetation-water interactions in water limited systems. I also became a part of Ghent University thanks to Diego Miralles, promoting of my studies.

Although I’m still interested in the things I’ve done previously, my most recent interests can be summarized as remote sensing with geostationary satellites, time series analysis, spatio-temporal analysis of ecohydrological concepts and surface energy budget. Even though my main office is in Jena - Germany, at MPI-BGC; I’m doing my best to be in collaboration with the LHWM, by visiting and teleconferencing. Further information can be found on my MPI-BCG webpage.

Research topics: remote sensing, ecohydrology, time series analysis


Address: - Room C2.004




  • 2017 – Present: PhD candidate, Max Planck Institute for Biogeochemistry and Ghent University
  • 2017 – 2017: Trainee researcher for monitoring tree stress via remote sensing, Joint Research Centre of the European Commission
  • 2014 – 2016: MSc. in Geographic Information Technologies, Istanbul Technical University
  • 2007 – 2013: Bsc. in Civil Engineering, Middle East Technical University


  • Ecohydrology from Space – Vegetation-Water interactions in water limited systems via remote sensing


  1. Küçük Ç., Taşkın G., Erten E. Paddy-Rice Phenology Classification Based on Machine-Learning Methods Using Multi-temporal Co-Polar X-Band SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), 2509-2519, 2016.