Biblio
Filters: Author is Morten Wergeland Hansen [Clear All Filters]
A small-scale oceanic eddy off the coast of West Africa studied by multi-sensor satellite and surface drifter data. Remote Sensing of Environment 129, (2013).
INCUSAR—A Method to Retrieve Temporal Averages of 2D Ocean Surface Currents from Synthetic Aperture Radar Doppler Shift. Remote Sensing Advances for Earth System Science (2013).doi:10.1007/978-3-642-32521-2_7
Wave Breaking in Slicks: Impacts on C-Band Quad-Polarized SAR Measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, (2016).
The Nansen-Cloud: a Scientific Platform as a Service for the Nansen Group. Proceedings of 2016 conference on Big Data from Space (BiDS'16) (2016).at <http://publications.jrc.ec.europa.eu/repository/handle/JRC100655> Abstract
A new look at Envisat ASAR range Doppler shift retrieval with the aim of reprocessing ten years of level-0 data. EUSAR 2018; 12th European Conference on Synthetic Aperture Radar (2018).
Simulation of radar backscatter and Doppler shifts of wave–current interaction in the presence of strong tidal current. Remote Sensing of Environment 120, (2012). Abstract
Nansat: a Scientist-Orientated Python Package for Geospatial Data Processing. Journal of Open Research Software 4, (2016). Abstract
Signature of the Agulhas Current in high resolution satellite derived wind fields. Remote Sensing of Environment 217, (2018).
Evaluation of Radial Ocean Surface Currents Derived From Sentinel-1 IW Doppler Shift Using Coastal Radar and Lagrangian Surface Drifter Observations. Journal of Geophysical Research (JGR): Space Physics 125:e2019JC015743, (2020). Abstract
On the use of Doppler shift for sea surface wind retrieval from SAR. IEEE Transactions on Geoscience and Remote Sensing 50, (2012).
Classification of sea ice types in Sentinel-1 synthetic aperture radar images. The Cryosphere 14, (2020).
NANSAT - a scientist friendly Python toolbox for processing 2D satellite EO and model raster data. Proceedings of 2016 conference on Big Data from Space (BiDS'16) (2016).doi:10.2788/854791 Abstract
