Project Summary:
Antarctic snow layers contain information about past ice dynamics, accumulation rate, temperature and the timing of melt events. They can also hide buried features such as crevasses (Marsh et al., 2021). To observe sub-surface layering, we have historically relied on ground-based measurements (snowpits, ice cores) or radio-echo sounding from aircraft (Bingham et al., 2015), requiring extensive field campaigns and providing limited spatial and temporal coverage.
Recent improvement in satellite-borne radar resolution and the timing of orbital crossovers allows coincident measurements over ice sheets at multiple frequencies, enabling the mapping of near-surface snow properties through penetration differences (Michel et al., 2014; Rott et al., 2021) and advanced waveform analysis (Fons et al., 2021). This new satellite capability, alongside machine learning assisted processing, provides a pathway to mapping Antarctic-wide accumulation rates and physical properties that control crack growth, which remain two of the most uncertain components of mass balance of the ice sheet (Palerme et al., 2017). This PhD project ties in with a NERC-funded research project on the Brunt Ice Shelf (RIFT-TIP) and field validation work may be possible through the Collaborative Antarctic Science Scheme.
Objectives and Methods:
The project has two aims:
1/ To investigate temporal change in satellite radar backscatter and penetration at X-, C- and L- band (including at different polarisations) over a well-studied control site in Antarctica (Fig 1): Images will first be radiometrically calibrated and adjusted for ice flow. A supervised or semi-supervised change detection approach will be developed, (e.g., a convolutional neural network), using differences of multi-look backscatter images and interferometric coherence as input. Initially, the seasonal evolution of a 4+ year sequence of TerraSAR-X and Sentinel-1 synthetic aperture radar (SAR) backscatter imagery will be correlated with local meteorological observations and ground-based (400 MHz) radar (GPR) measurements. Coincident field validation work may be possible on the Brunt Ice Shelf through the Collaborative Antarctic Science Scheme.
2/ To investigate the extent and depth at which subsurface features (crevasses, firn aquifers, etc.) are visible and represented at X-, C- and L- band (TerraSAR-X, Sentinel-1, Palsar-2) relative to depths and surface heights obtained from GPR and altimetry (Envisat, Cryosat-2, ICESat-2): A classifier will be developed to match unlabelled regions of SAR images against subsurface information available from layer tracking of GPR (e.g. Ibikunle et al., 2020) to form a spatially continuous layer depth map. Data from the P-band BIOMASS sensor may be included after its launch (in 2024) with the potential to map and interpret more deeply buried features.
This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning and field training. All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org
Application support
More information for applicants can be found on our available Projects, How to Apply and dedicated FAQ webpages. You will also find additional resources such as How can a PhD help me with my career? and our ‘Demystifying the PhD application process’ webinar
This student will be registered at the University of Edinburgh but based at British Antarctic Survey in Cambridge
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