Master Thesis Proposals

Presented here are thesis proposals for students, supervised by staff at the Earth Observation group. The scope of these proposals can be adjusted and are equally suitable for students from the 5-year integrated masters (30sp) and the 2-year master’s program in Physics (60sp).

You are also welcome to pick one of the already suggested projects below. For examples of completed master projects, see Education section. 

In addition we welcome suggestions from prospective students on specific topics and will together with the student set up a master's project on a topic of your choice. Just send us an email or stop by our offices! Earlier topics created after discussions were; mapping plastic from space, various glaciology studies and technological developments.

The Earth Observation group currently propose the following project proposals:

Sea ice
  • Tracking the seasonal evolution of radar backscatter signatures from different sea ice types 
  • Multi-frequency SAR study of thinner sea ice and ridged areas
  • Thin sea ice detection with CryoSat-2
  • SAR polarimetric sea ice evolution during the CIRFA 2022 cruise
  • Can cold snow interefere with radar signal?
  • Influence of height, distance, shape and orientation of pressure ridges on the X-band ice ship radar
  • Machine Learning for Sea Ice Classification
  • Sea Ice Samples for Earth Observation Applications

Other environmental applications and technology development

  • Counting seals from space - using data from seal count cruises to the east Greenland coast
  • New approach to SAR Terrain correction
  • Naturally occurring oil seeps in Hopendjupet and how to model their drift patterns

Sea ice related projects 

Tracking the seasonal evolution of radar backscatter signatures from different sea ice types 

Synthetic aperture radar (SAR) is the main tool for operational sea ice monitoring. Due to its all-day and all-weather imaging capability, SAR instruments can reliably provide images throughout the entire year. The radar signal that is backscattered from the sea ice and snow surface is controlled by both surface and radar parameters. The most important radar parameters are frequency, polarization, and incident angle (IA), the dominant surface parameters include small-scale roughness (on the scale of mm to dm), large-scale roughness (deformation on the scale of dm to m), and the electric properties of the ice and snow. 

For a given sea ice type, such as for example level first-year ice (FYI) or deformed multi-year ice (MYI), variations in IA and changes in the overlying snowpack during melt onset can cause large variations in the radar backscatter. In the past, these variations have mostly been investigated for stationary landfast ice. In this master project, you will study the effect of IA and seasonal changes on drifting sea ice. 

During the CIRFA-22 cruise in April and May 2022, we deployed 17 drift buoys (drifters) on the sea ice close to Greenland. The drifters sampled GPS position every 30 minutes, resulting in the drift trajectories shown in the left figure. These trajectories enable us to identify and track individual sea ice floes in a time series of overlapping Sentinel-1 SAR images and track their backscatter signature over the course of several weeks and months. The figure on the right shows an example result of HH and HV intensity time series from a MYI and a FYI floe, indicating the manually identified timing of melt onset. In this master thesis, you will use the drifter data and overlapping Sentinel-1 imagery to expand the amount of tracked ice floes, and you will identify additional ice types in the imagery and include them in the study. You will then calculate the statistical separability of the different ice types and use this to assess the results of an automated ice type classification algorithm. Finally, you will extract backscatter signature time series over the landfast ice at the Greenland coast and investigate the differences and similarities in the evolution of drift ice and landfast ice backscatter. Depending on your initial findings, progress, and personal interest, there is also the option to work on retraining the classifier to enhance its performance during the melt season. 

During this project, you will learn about sea ice types, drift, and classification, and you will gain experience in SAR image processing and interpretation. The work is directly related to several ongoing projects in the Earth Observation group, and you will have a chance to closely work with our PhD students or post-docs. 

         

Left: Trajectories of drifters deployed on the sea ice during the CIRFA-22 cruise in April and May 2022 (map by C. Taelman). Photograph of a drifter after its deployment in top left corner. Right: Time series of IA-corrected backscatter from example MYI and FYI floes (from Taelman et al., 2023).

 Contact: Johannes (johannes.p.lohse@uit.no), Catherine (catherine.c.taelman@uit.no) 

Literature: 

Taelman, C., Lohse, J., Doulgeris, A.P., Johansson, M. (2023). From winter to melt season: C-band radar backscatter evolution of fast-drifting sea ice floes. Poster presentation at IGS 2023. 10.13140/RG.2.2.11564.44164 

Lohse, J., Doulgeris, A.P., Dierking, W. (2020). Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle. Annals of Glaciology, 61(83), 260-270. 
https://doi.org/10.1017/aog.2020.45 

Geldsetzer T., Howell, S. (2023). Incidence angle dependencies for C-band backscatter from sea ice during both the winter and melt season. IEEE Transactions on Geoscience and Remote Sensing. 10.1109/TGRS.2023.3315056 

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Multi-frequency SAR study of thinner sea ice and ridged areas

The Arctic sea ice landscape is rapidly transforming and this includes a thinning of the sea ice and a reduction of the sea ice extent. The dark and often cloudy environment means that satellite borne radar sensors, with multi-polarization capabilities, play a key role in the sea ice monitoring. The marginal ice zone in particular is subjected to cloud cover and fog and is also an area of specific interest for guiding ship traffic and for the maritime industry. Ice regimes differ in composition and areal fractions of ice types, ice concentration, and degree of deformation.

 

The image is showing thin sea ice that has undergone finger rafting. Image taken during the CIRFA cruise in April-May 2022

C-band SAR are used operationally to map presence of sea ice as well as classify different sea ice types. However, L-band SAR has been shown to be sensitive to deformed sea ice areas and in addition does the good signal-to-noise ratio at L-band enable good separation of young ice types from surrounding thicker sea ice and the open water. The larger penetration depth in L-band SAR is of general benefit for characterizing and classifying sea ice, as it improves discrimination between first (FYI) and multiyear ice (MYI), and is of specific benefits for detection and characterization of leads and thin ice areas. The low noise floor for ALOS-2 enables reliable separation of thin ice areas from thick sea ice also in the presence of frost flowers.

This project will build upon overlapping fully-polarimetric L- and C-band data collected during the MOSAiC campaign in 2019-2020 and during the CIRFA cruise in 2022 to Belgica Bank. The aim is to investigate the importance of the pixel resolution for the detection of the thin ice and ridges and how this varies with the two different frequencies. This can be done using both spatially and temporally overlapping medium-resolution images such as those from the Sentinel-1 or by simulating images with a lower resolution. The outcome is expected to provide answers to the size of thin ice areas and ridges can be captured in the high-resolution fully polarimetric images and how does this translate to the daily medium-resolution Sentinel-1 images.

Contact: Malin Johansson (malin.johansson@uit.no)

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Thin sea ice detection with CryoSat-2

 

Plot shows the November 2017 sea ice thickness thinner than 0.5 m from CryoSat-2 (left) and SMOS (right).

Over the past two decades sea ice in the Arctic basin has transitioned from mainly thick, old multi-year ice to thinner, younger first-year ice types, as the northern polar climate has warmed at a rate 3-4 times faster than the global average. The thickness of the ice is a critical consideration for maritime vessels navigating ice-covered waters, for instance in the region around Svalbard, so it is a high priority of the Norwegian Ice Service to remotely detect the ice thickness from space.

We can detect the thickness of sea ice with the spaceborne SAR Altimeter CryoSat-2; however, previous research has suggested that CryoSat-2 has very low accuracy when the ice is thinner than ~1 meter in thickness.

In this project we will re-evaluate the lower detection limit of CryoSat-2 to see if we can get reliable measurements down to a few 10s cm in thickness. For this goal, we will use the latest physically based dataset of CryoSat-2 sea ice freeboard derived from a SAR waveform model applied to the altimeter observations. The student will compare to independent thin sea ice measurements from the SMOS L-band radiometer and to airborne measurements collected on the European Space Agency SMOSice campaign. Re-setting the thin ice capability of CryoSat-2 will be valuable for the Norwegian Ice Service to provide accurate sea ice thickness data to its users.

Contact: Jack Landy (jack.c.landy@uit.no)

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SAR polarimetric sea ice evolution during the CIRFA 2022 cruise

In April – May 2022 CIRFA conducted a cruise to the Belgica bank area. During this cruise in-situ data was collected to confirm sea ice types and other sea and snow surface parameters that we can observe in the satellite images. Sensors were installed on the sea ice to keep recording temperature data even after the cruise. Regular fully-polarimetric C- and L-band SAR images were collected over this area until the end of June.

Pauli images from April 11 (before the cruise), May 4 (under the cruise) and June 18 (after the cruise). The images cover in part the same area.

The data set in this project consists of primarily 11 Radarsat-2 scenes collected over fast ice in the western Fram Strait in the period April 11th to June 18th, 2022. The scenes cover several types of snow-covered sea ice areas, including levelled first year ice, deformed multi-year ice, and thin young lead ice. The first part of the time period, the area had cold winter conditions, with dry snow cover. The temperatures gradually increased through the last part of May, and towards the end of the data collection, the site area experienced warm temperatures and melting conditions. As can be seen, different ice surfaces undergo different changes, which we think are correlated to the physical structure of the surfaces.

Within this project the backscatter evolution as well as the evolution of polarimetric features from the winter season until the melt season will be investigated. The effect of the onset of melt season on different sea ice types, such as smooth and deformed, and will be analyzed and if possible, connected to scattering models.

Contact: Malin Johansson (malin.johansson@uit.no)

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Can cold snow interefere with radar signal?

It is normally assumed that the cold snow layer is transparent for the light in microwave part of the spectrum. The actively sent pulse of light should scatter predominantly on the sea ice surface or shortly after penetrating it. Recent research shows that that does not hold for salty and wet snow (Nandan et al, 2017). This knowledge is essential for correct retrieval of sea ice thickness and snow from radar altimeters mounted on satellites (e.g. CryoSat-2, Sentinel-6 and future ESA missions). At MOSAiC several thousand snow depth measurements were collected each week along several-kilometer-long transects. At the same location sea ice thickness was recorded. The X-band ice radar mounted on the research ship (Oikkonen et al, 2017) was recording continuously over large area surrounding those measurements. The first look at the imagery suggests that large artificial snow accumulations can change X-band radar intensities. While the Arctic snow cover is typically thin (10-30 cm), large amounts of snow can accumulate also in naturally-occurring features of pack ice -in the pressure ridges (Liston et al, 2018). When the ridges are first formed the snow cover there is same thin as everywhere else and even blocks of bare ice are exposed. Over the time, ridges as rough features in windy environment accumulate deep snow drifts, in places over 1m deep. For this master thesis topic, the candidate will explore the development of ship radar back-scatter intensities and compare them to the snow depth observed in-situ on the ice. The candidate should be interested in sea ice geophysical processes and statistical analysis of the data. The analysis could be potentially extended to satellite radar data and radar signal modeling.

Literature:

Contact: Polona (polona.itkin@uit.no), Anthony(anthony.p.doulgeris@uit.no)

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Influence of height, distance, shape and orientation of pressure ridges on the X-band ice ship radar

The images from navigational sea ice ship radar could be used to develop sea ice classification (e.g. see pioneering work with ship radar by Kaspersen (2017), and sea ice classification examples in Lohse et al, 2020). The images with radar intensities can be comparedto airborne data: Airborne Laser Scanner (ALS) from helicopter on MOSAiC expedition (for example data from previous campaign see: Itkin et al, 2018). Some data is available as ‘raw’ radar back-scatter, but majority of the data is already converted to signal intensity. How can we best use such limited data? Additional information that can be used is in-situ data from ice cores and snow pits inside the radar footprint, ice observations from the ship, and back trajectory analysis of SAR images and passive microwave products. The candidate should be interested in image analysis, sea ice geophysical processes and statistical analysis of the data. The topic has a potential todevelop into a PhD thesis.

Literature:

  • Cruise reports from MOSAiC expedition (in preparation, drafts available as internal material on request from polona.itkin@uit.no)
  • Cruise report from Nansen Legacy cruise SSQ2 –April/May 2021 (draft available in June 2021)

Contact: Polona (polona.itkin@uit.no) and Anthony (anthony.p.doulgeris@uit.no)

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Sea Ice Samples for Earth Observation Applications

Unlike freshwater ice, sea ice is saline and freezes unevenly. The brine concentrates in pockets and channels. If sea ice survives the summer melt its structure is further complicated by brine drainage and melt-freeze cycles of snow that covers it. These features influence the light reflection from snow and sea ice surfaces. Data from space-borne radars and laser altimeters are widely used for understanding climate processes in remote areas like the Arctic Ocean. They are also valuable climate model validation data resources. Still, the ground data is scarce and radar signals are not completely understood.

Studying in situ samples of sea ice is a key piece of the jigsaw for better understanding remote satellite measurements of the ice cover.

This project will make use of in situ snow and sea ice data collected from several recent research expeditions to the Arctic (for example on the recent cruise of UiT to the Belgica Bank in the East Greenland Sea and the large Norwegian collaboration the Nansen Legacy Project). There may be opportunities to learn about snow and sea ice sampling during short training days on fjord ice near Tromsø, regularly run in the spring.

The candidate will primarily work with detailed photographs of sea ice core microstructure, prepared by CIRFA scientist Janina Osanen (see below). Sea ice crystal size, orientation, brine channels and bubble content are crucial input to radiative transfer models used to simulate the satellite radar signal travelling through sea ice. Images of sea ice thin sections can be analyzed to automatically detect sea ice features, such as distinct layers, crystals, and inclusions. The core of the master thesis project will be the development, tuning, and testing of classification and segmentation algorithms for computer-assisted detection of these sea ice features. Prototype codes based on scikit-learn models are available for segmenting images, and training will be provided for developing these further.

Images of thin sea ice sections, photographed through a polarized filter, showing ice crystals and microstructure. Initial binary segmentations applied to the images to identify layers and features. Photo on the right shows how sea ice sample is extracted by ice coring.

The candidate can also work with in situ observations to understand the density, salinity, and microstructure of snow and sea ice collected from different locations and in time periods in the Arctic. Training will be provided for these analytical methods. Output from the sea ice microstructure analysis and from in situ ice and snow observations can be applied in a simple radiative transfer model to simulate the satellite radar signal. The model can then be used to understand the sensitivity of the radar penetration into, and backscattering from, snow-covered sea ice at different radar frequencies. Alternatively, the sea ice density measured from ice core samples can be combined with other data collected in the field (sea ice thickness and snow depth, snow density) and related to the satellite products from radar and laser altimeters on earth-observing satellites. There is the possibility for more than one MSc thesis project to be accomodated within this topic.

For additional information about this master thesis opportunity contact polona.itkin@uit.no, johannes.p.lohse@uit.no and jack.c.landy@uit.no.

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Machine Learning for Sea Ice Classification

Machine learning methods have been frequently used for remote sensing applications. Lately, a new approach has been successfully introduced for estimating geophysical sea ice information from dual-polarimetric SAR data, information which in general require quad-polarimetric observations. The dual-polarimetric SAR data is from the freely available wide swath data from the Sentinel-1 satellites, which are often preferred for sea ice monitoring. This project will further study machine learning models for sea ice monitoring by using the established methodology together with machine learning classifiers to address sea ice classification. The study will in particular address the problem of distinguishing between sea ice and open water under various weather conditions, and potentially develop a tool/methodology for robust large-scale localization of the ice edge.

You will learn to process SAR data, develop and apply machine learning methods and understand sea ice remote sensing.

Contact: Katalin Blix (katalin.blix@uit.no)

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Other environmental applications and technology development

 

Counting seals from space - using data from seal count cruises to the east Greenland coast


Every 5 years a cruise lead by Institute of Marine Research counts the number of adult harp and hooded seals and their newborn seal cubs on the sea ice east of Greenland. The seal count takes place in late March when the cubs are just born. Optical images acquired from airplanes are used for the count. The images have a high enough resolution to determine the seal type. The campaign normally takes place for one day and is flow in a lawnmower pattern to ensure that each seal is only counted once. However, during the 2018 campaign the flight campaign was split over 2 days, and this means that some seals might have been counted twice. Using pattern recognition techniques this project aims to identify sea ice floes from one day to the next to ensure that seals located on the same floe are not counted twice. The images are orthorectified and georeferenced, but the sea ice drift between the two days is difficult to account for.

 

Optical satellite image (Maxar) to the left where the seals can be seen on top of the ice floes and photograph on the right taking from the helicopter showing a close up of the seals. 


For the initial stage of the seal counting an AI based algorithm called Sealhunt is used. The algorithm identifies images that doesn’t contain any seals at all (main purpose) and identifies seals where possible. The main initial purpose is to remove images lacking seals and this has so far had priority in the algorithm and a higher number of false positives have been accepted. The development would be to work on reducing the number of false positives in the algorithm. High resolution optical images from the airplane (and possible satellite images) from both 2018 and 2022 will be used for this purpose. Information about the 2022 cruise can be found here: https://imr.brage.unit.no/imr-xmlui/handle/11250/3012654

The project/s will be supervised by Malin Johansson/Anthony Doulgeris at UiT and Martin Biuw from IMR.

Contact: Malin Johansson (malin.johansson@uit.no), Anthony (anthony.p.doulgeris@uit.no)

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New approach to SAR Terrain correction

Synthetic aperture radar (SAR) back-scattered signals are dependent on both the surface properties (making them useful for remote sensing) and the viewing geometry (which complicates the interpretation). The signal strength varies systematically with the incidence angle withan approximately exponential relation, or linear in the log-domain (in decibels, dB). This is traditionally corrected with a single rate for the entire image as a pre-processing step known as incidence angle correction. However, many research papers observe different rates of decrease for different surface classes, mostly related to surface roughness properties, and so this one rate correction is not ideal.

We have recently had great success by modelling this incidence angle effectas a class property (rather than an image property), thus allowing different rates per class. This has been demonstrated for maritime monitoring where the satellite viewing angle is changing smoothly across the imaging swath on the flat ocean surface. We believe that the per-class rates may also help land-based classification where terrain slopes vary the signals, but this has not yet been demonstrated.

The project shall explore how to introduce per class land-based terrain correction into segmentation or classification algorithms. The first stage may search for large flat terrains, suchas large scale agriculture, to confirm its benefits in a similar case to the flat ocean case, and subsequent cases will seek to introduce the local terrain angles (derived from Digital ElevationModels, DEMs) with different degrees of rough surface topography (from hills and glaciers to mountains) for evaluation of the approach.

Contact: Anthony (anthony.p.doulgeris@uit.no)

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Naturally occurring oil seeps in Hopendjupet and how to model their drift patterns

In Barents Sea hydrocarbons (oil and natural gas) are stored in the seabed, and methane gas and oil is naturally seeping out and rising to the ocean surface. Known hot spots for such oil releases are Hopendjupet and the area outside Prins Karls Forland. These oil seepages can be observed in synthetic aperture radar (SAR) images.

 It's common that these slicks have a drift pattern that has the shape of spirals (see figure). These spirals are partially caused by inertial oscillations. The frequency of these is depends (to first order) on the latitude and to second order on the stratification. The frequency is possible to derive from, e.g., the Norwegian Norkyst-800 or Barents 2.5 model. The amplitude however will be inferred from the SAR images and within this project it’ll be investigated how well the of the amplitude of the near-intertial waves is resolved in the models.  

 

Radarsat-2 image (left) with naturally occuring seepages shown as dark spirals, and Sentinel-1 SAR image (right) with overlayed areas showing the slick detections during one week. 

The supervision will be shared between UiT and the Meteorological Institute.

Contact: Malin Johansson (malin.johansson@uit.no) or Johannes Röhrs (johannesro@met.no)

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