dr. Dominik Rains

Bio

My expertise lies in extracting geophysical variables from Level 1 ‘raw’ satellite data, e.g. SMOS, SMAP or Sentinel-1. I work on data assimilation, combining the advantages of the spatio-temporally sparse observations with those of estimates from land surface models, linking the model estimates to the observations either through traditional radiative transfer modelling or machine-learning.

Besides working in academia, I have experience in working at and collaborating with private Earth Observation companies delivering operational products to clients (VISTA / VanderSat). Before joining Ghent University I worked at the European Space Agency (ESA/ESRIN) for two years as a trainee funded by the German Aerospace Center (DLR).

I have carried out research visits at FZ Jülich, Monash University and University College London and I am a collaborator at the Earth Observation Science group of the University of Leicester.

My current project ET-SENSE is on assimilating observations from the Sentinel satellites into the GLEAM land surface model. It is funded by the Belgian Federal Science Policy Office (BELSPO).

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+
• ET-SENSE

Selected publications

• Bispo, P.D.C., Rodríguez-Veiga, P., Zimbres, B., do Couto de Miranda, S., Henrique Giusti Cezare, C., Fleming, S., Baldacchino, F., Louis, V., Rains, D., Garcia, M. and Del Bon Espírito-Santo, F. Woody aboveground biomass mapping of the Brazilian Savanna with a multi-sensor and machine learning approach. Remote Sensing, 12(17), 2685, 2020.
• Hostache, R., Rains, D., Mallick, K., Chini, M., Pelich, R., Lievens, H., Fenicia, F., Corato, G., Verhoest N.E. and Matgen, P. Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: the Murray–Darling basin in Australia as a test case. Hydrology and Earth System Sciences, 24(10), 4793–4812, 2020.
• Bispo, P.D.C., Pardini, M., Papathanassiou, K.P., Kugler, F., Balzter, H., Rains, D., Dos Santos, J.R., Rizaev, I.G., Tansey, K., dos Santos, M.N. and Araujo, L.S. Mapping forest successional stages in the Brazilian Amazon using forest heights derived from TanDEM-X SAR interferometry. Remote Sensing of Environment, 232, 111194, 2019.
• Rains, D., De Lannoy, G.J., Lievens, H., Walker, J.P. and Verhoest, N.E. SMOS and SMAP Brightness Temperature Assimilation Over the Murrumbidgee Basin. IEEE Geoscience and Remote Sensing Letters, 15(11), 1652–1656, 2018.
• Rains, D., Han, X., Lievens, H., Montzka, C. and Verhoest, N.E. SMOS brightness temperature assimilation into the Community Land Model. Hydrology and Earth System Sciences, 21(11), 5929–5951, 2018.
• Rains, D., Sabia, R., Fernández-Prieto, D., Marconcini, M. and Katagis, T. Extended analysis of SMOS salinity retrieval by using Support Vector Regression (SVR). IEEE Geoscience and Remote Sensing Symposium, 2265–2268, 2014.