Title: Extension of sea ice climate time series with historical satellite data

• Student: Wiebke Margitta Kolbe
• Funding: DMI/DTU

Arctic sea ice is an important climate indicator because the effects of global climate change are amplified in the Arctic. Current sea ice climate data records (CDRs) beginning in the late 1970’s are based on satellite data. However, satellite missions from the early and mid-1970s can also be used for mapping sea ice and extending the current CDRs. While older satellite instruments have their limitations compared to modern multi-channel sensors, they still provide useful data for mapping sea ice extent and the distribution of sea ice type. This PhD project will build and run a method to process historical satellite data to extend existing sea ice climate data records of sea ice extent in the past. The project is a part of the Danish National Center for Climate Research (NCKF) at DMI, and the research will provide insight into historical sea ice development and serve as an important sea ice extent reference from the 1970s, which can be used for input to climate models and reanalysis.

Title: Mapping soil moisture at a field-scale using Remote Sensing

• Student: Miguel Negre Dou
• Funding: DTU/Landbrugsstyrelsen

Existing satellite-derived soil moisture products have a spatial resolution limited to 1 km, which is too coarse for most agricultural applications. The main objective of this Ph.D. is to define and validate algorithms that reliably map soil moisture at a field level, using spaceborne Synthetic Aperture Radar (SAR), in combination with other remote sensing techniques, such as multispectral and hyperspectral imagery, and passive microwave radiometry. This project is financed by Landbrugsstyrelsen (the Danish Agricultural Agency) within the framework of the broader KortDrænN2O initiative, whose goal is to explore new approaches to systematically quantify pernicious nitrous oxide (N2O) emissions from poorly drained mineral soils.

Title: Error characterization of multi-temporal InSAR deformation time series

• Student: Jakob Ahl
• Funding: DTU

Multi-temporal Interferometric SAR (MT-InSAR) techniques utilize tens or hundreds of space-borne radar acquisitions to model time series of slowly varying ground deformation patterns on the scale of years by using observed changes in electromagnetic phase from one acquisition to the next. Due to the long timescales, there is a high risk of decorrelation causing errors, and therefore care must be taken in the modelling and subsequent phase integration.

This project aims to find and investigate parameters capable of characterizing errors in the deformation estimates and integrating them into traditional MT-InSAR workflows.

Previous PhD projects

Title: Artic sea ice climate data records and the consistency between SST and sea ice satellite products

• Student: Pia Nielsen-Englyst
• Funding: DMI/DTU

Accurate global sea surface temperature (SST) observations are important for climate monitoring, understanding of air–sea interactions, and numerical weather prediction. SST has been retrieved from infrared (IR) satellite observations since 1981, but these are limited by clouds, and biased from aerosols. Passive microwave (PMW) observations are not prevented by non-precipitating clouds and the bias by aerosols is small. The aim of the PhD is to improve the algorithms to retrieve SST from PMW satellite observations, including an assessment of the impact of using different channel selections in the retrieval algorithms, and finally to assess how SST observations can be integrated with sea ice parameters in a multi-sensor gap free SST and sea ice product for the Arctic.

Title: Synthetic Aperture Radar TOPS-mode Interferometry for Ice Velocity Retrieval

• Student: Jonas Kvist Andersen
• Funding: DTU

The project aims to improve polar ice velocity retrievals from space-borne Synthetic Aperture Radar, specifically the EU Copernicus Sentinel-1 satellites. Currently, only amplitude-based velocity measurements are being routinely generated. Interferometric (i.e. phase-based) velocity retrievals are of significantly higher resolution and accuracy. However, due to complications introduced by the Sentinel-1 TOPS acquisition mode, interferometry is not straightforward to apply in scenes with motion in the along-track direction. This project seeks to solve the challenges of TOPS interferometry on ice sheets. Additionally, methods of combining amplitude- and phase-based measurements in a highly automated fashion will be investigated.

Title: MIMO radar for drone detection

• Student: Lasse Lehmann
• Funding: Terma industrial PhD

Airborne drones pose tremendous challenge to common surveillance systems such as radars. Multiple-input multiple-output (MIMO) radar has been suggested as a suitable technology to enable wide-area surveillance, capturing small targets in high-resolution with the ability to track several targets at any given time. This is performed without mechanically rotating the antenna, instead relying on digital signal processing to scan the covered search-volume. In my project I investigate algorithms and signal processing to enable optimal MIMO radar operation, resolving targets at fine angular resolution and exploiting micro-Doppler signatures of drones to affirm detection and perform classification.

Title: MIMO radar Systems and Algorithms

• Student: Ricard Llado Grove
• Funding: Thomas B. Thriges Fond

Multiple-Input-Multiple-Output (MIMO) radar builds upon the principle of superposition, where several transmitters simultaneously radiate independent waveforms. Separating the waveforms in each receiver and forming a virtual array from the combinations of transmit and receive channels, provides a higher diversity than that of the conventional phased array radar. On the other hand, imperfections of multichannel systems will degrade the performance of such systems drastically. Hence, this project will mainly focus on the impact of imperfections for MIMO radar systems, and not least the calibration of the effects. Furthermore, the design of suitable waveforms in terms of application and scalability will be investigated along direction-of-arrival algorithms.

Title: Detection and identification of objects in advanced radar images

• Student: Paul Jacques Connetable
• Funding: DTU

The aim of the project is to study techniques for the detection and identification of man-made objects, such as vehicles or buildings, using polarimetric SAR images. The core of the project consists in finding parameters and data behaviors which highlight the sought objects as opposed to natural backgrounds, as well as ways this information can help identifying them. The research also includes investigation and development of the statistical tools which can be used for the detection and classification processes, particularly statistical hypothesis testing, image analysis, machine learning, and artificial neural networks, and the comparison of their performance.