I graduated from the Ludwig-Maximilians University Munich with a Masters degree in Physical Geography/Environmental Systems with a focus on soil-vegetation-atmosphere interactions, hydrological modelling and remote sensing. In parallel to my studies I worked in private held remote sensing companies applying remote sensing techniques to practical applications. In this context my Bachelor thesis was on the implementation of per-pixel atmospheric correction of multispectral images and my Master thesis on Flood Mapping for the Insurance Industry within a ESA funded project. Both my theses were done at VISTA remote sensing in Geosciences in Munich.

Funded by the German Aerospace Center I thereafter worked for two years at ESA/ESRIN within the support to Science Element mostly carrying out research on the characterization of the water cycle using the SMOS satellite. The main topic of research was linking both soil moisture and salinity retrievals to atmospheric fluxes but also supporting a research project on the salinity retrieval through Support Vector Regression.

Currently I am working on the improvement of drought monitoring and forecasting through the assimilation of SMOS, SMAP and Sentinel 1 data into hydrological models within the BELSPO funded HYDRAS+ project.

Research topics: Remote Sensing of hydrological variables, hydrological modelling

CONTACT

Address: Coupure links 653 - Room A2.008

               9000 Ghent, Belgium

Phone: +32 9 264 61 37

E-mail: Dominik.Rains@UGent.be

TRAJECTORY

• 2019 – Present: Post-doctoral Researcher – Ghent University, Laboratory of Hydrology and Water Management
• 2015 – 2019: Doctoral Researcher – Ghent University, Laboratory of Hydrology and Water Management
• 2013 – 2015: Researcher at ESA/ESRIN - German Trainee Programme funded by the German Aerospace Center (DLR)
• 2008 – 2013: M.Sc. in Physical Geography / Environmental Systems - Ludwig-Maximilians University Munich

RECENT AND ONGOING PROJECTS

• HYDRAS+ (BELSPO) ­– Improving drought monitoring through assimilating multi-source remote sensing observations in hydrologic models (2014 - 2017)
• ET-SENSE