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).

Research topics: Retrieval of geophysical variables, data assimilation, high-performance computing, machine-learning

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.