UCL Earth Sciences


Thomas Johnson

Using novel machine learning techniques to study remote sensing data for sea ice surface characterisation.

PhD project title:

Fusion of CryoSat-2, ICESat-2, and MISR remote sensing data for sea ice surface characterisation.

Thomas Johnson
Project description:

Surface roughness and albedo are crucial parameters in many climate and oceanographic studies. The surface morphology of sea ice is vital in constraining momentum transfer between the atmosphere and ocean, provides preconditioning for summer melt pond extent, has important applications for coastal communities, while also related to ice age.  The MISR (Multi-angle Imaging SpectroRadiometer) instrument utilizes nine near-simultaneous camera view angles to sample specular anisotropy, which can be related to surface morphology. The multi-decadal temporal archive of the MISR sensor offers an exciting avenue of research based on promising synergies with several satellite missions operated by ESA. 
Together with analysis of sea ice roughness and albedo from radar altimetry (CryoSat-2, 2010-present), laser altimetry (ICESat-2, 2018-present) and airborne data (IceBridge, IceBird, 2009-present), this project will apply novel machine learning techniques to provide significant improvements in validating roughness and albedo models over the cryosphere.

Academic Background:
BSc: Geophysics, Imperial College London | MSc: Geoscience, University College London