Remote Sensing in Hydrology
Remote sensing data combined with modeling techniques are increasingly used in hydrological studies. These data provide observation of multiple hydrological variables both in time and space.
The remote sensing in hydrology group uses satellite data, data driven approaches, and machine learning algorithms to monitor hydrological variables as well as to assess the water resources from regional to global scales. Research is carried out in co-operation with NASA PMM Team, Stanford University, University of Texas at Austin, Missouri University of Technology in the US, and several universities in the Middle east. Research is also carried out in close collaboration with national stakeholders and research institutes, including VASYD, NSVA, SMHI, SGU, and Svenskt Vatten.
Areas
- Satellite precipitation (TRMM-Tropical Rainfall Measuring Mission and GPM-Global Precipitation Measurement)
- Land surface deformation and groundwater (InSAR-Interferometric Synthetic Aperture Radar)
- Weather radar (X-band and Mini Meteorological Radar-MRR-2)
- Remote sensing of dust
- Machine learning algorithms and data driven approaches
Contact
Assistant Prof. Hossein Hashemi
hossein [dot] hashemi [at] tvrl [dot] lth [dot] se (hossein[dot]hashemi[at]tvrl[dot]lth[dot]se)
Involved researchers
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Funding partners
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