Arctic Field Summer Schools: training the next generation of Arctic researchers

The Arctic Field Summer Schools project is funded by the Research Council of Norway (NFR) and Norwegian Centre for International Cooperation in Education (SIU), under the grant agreement number 261786/H30. The project supports research and education collaboration among UiT - The Arctic University of Norway, University of Alaska Fairbanks (UAF), USA and University of Calgary (UC), Canada. Through a series of three summer schools, the project will engage graduate students in exploring science questions related to current Arctic challenges, and bring together leading Arctic researchers from the partner institutions. The project aims to create a forum and learning environment for students and young researchers to obtain an understanding of the complex Arctic environment, and how it can be monitored with various remote sensing sensors and platforms as well as in-situ observations

An Advanced Non-Gaussian Feature Space Method for POL-SAR Image Segmentation

This work extends upon our simple feature-based multichannel SAR segmentation method to incorporate highly desirable statistical properties into a computationally simple approach. The desirable properties include Markov random field contextual smoothing and goodness-of-fit testing to automatically obtain the significant number of classes. To achieve this we need to find an explicit class model to fit these non-Gaussian, non-symmetric or skewed feature space clusters. We take the skewed scale mixture of Gaussian scheme to model our classes and approximate it by a number of constrained Gaussians, thereby retaining much of the speed and simplicity of the original feature space method. The algorithm will be demonstrated on a real data and compared to an automatic Gaussian model

Aspects of model-based decompositions in radar polarimetry

Accepted manuscript version of the following article: Doulgeris, A.P. & Eltoft, T. (2015). Aspects of model-based decompositions in radar polarimetry. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1109/IGARSS.2015.7325746. Published version available at https://doi.org/10.1109/IGARSS.2015.7325746.In this paper, we further analyse the problem that polarimetric target decomposition methods in general have more physical parameters than equations, making the decomposition under-determined and hence have no unique solution. The common approach to get around this problem is to make certain assumptions, thus fixing one or more parameters, allowing the other free parameters to be solved from the set of expressions. We recently showed how to obtain additional information from fourth-order statistics to find a unique solution to model-based polarimetric decompositions ([1]). We previously showed a fourth-order unique solution that was valid only for Gaussian data, and indicated that non-Gaussian data led to an over-estimation in many of the parameters. This work describes our new method to obtain a generic textured data solution through an optimisation approach and presents preliminary results for a sea ice specific model

Towards operational sea ice type retrieval using L-band Synthetic aperture radar

Source at https://doi.org/10.1109/IGARSS.2019.8900458.Operational ice services around the world have recognized the economic and environmental benefits that come from the increased capabilities and uses of space-borne Synthetic Aperture Radar (SAR) observation system. The two major objectives in SAR based remote sensing of sea ice is on the one hand to have a large areal coverage, and on the other hand to obtain a radar response that carries as much information as possible. Although until now, L-Band SAR sensors are rarely used in an operational context, it offers greater capabilities for sea ice type retrieval and is more robust during the melt season compared to higher frequency bands. With the help of JAXA’s ALOS-2 PALSAR-2 sensor, we are able to explore the potential of polarimetric L-band acquisitions for sea ice analysis and classification in an operational environment. In this study we investigated the incidence angle related variation on the L-band backscatter and recommended optimal scenarios for Artificial Neural Network based sea ice type retrieval schemes

Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images

Synthetic aperture radar systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Owing to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nature of the targets, thus rendering single-rate image correction approaches only partially successful. The decay, also known as the incidence angle effect, impacts the segmentation of wide-swath images performed on absolute intensity values. We propose to integrate the target-specific intensity decay rates into a nonstationary statistical model, for use in a fully automatic and unsupervised segmentation algorithm. We demonstrate this concept by assuming Gaussian distributed log-intensities and linear decay rates, a fitting approximation for the smooth systematic decay observed for extended flat targets. The segmentation is performed on Sentinel-1, Radarsat-2, and UAVSAR wide-swath scenes containing open water, sea ice, and oil slicks. As a result, we obtain segments connected throughout the entire incidence angle range, thus overcoming the limitations of modeling that does not account for different per-target decays. The model simplicity also allows for short execution times and presents the segmentation approach as a potential operational algorithm. In addition, we estimate the log-linear decay rates and examine their potential for a physical interpretation of the segments

Automatic Detection of Low-Backscatter Targets in the Arctic Using Wide Swath Sentinel-1 Imagery

Low backscatter signatures in synthetic aperture radar (SAR) imagery are characteristic to surfaces that are highly smooth and specular reflective of microwave radiation. In the Arctic, these typically represent newly formed sea ice, oil spills, and localized weather phenomena such as low wind or rain cells. The operational monitoring of low backscatter targets can benefit from a stronger integration of freely available SAR imagery from Sentinel-1. We, therefore, propose a detection method applicable to Sentinel-1 extra wide-swath (EW) SAR scenes. Using intensity values coupled with incidence angle and noise-equivalent sigma zero (NESZ) information, the image segmentation method is able to detect the low backscatter targets as one segment across subswaths. We use the Barents Sea as a test site due to the abundant presence of low backscatter targets with different origins, and of long-term operational monitoring services that help cross-validate our observations. Utilizing a large set of scenes acquired in the Barents Sea during the freezing season (November–April), we demonstrate the potential of performing large-scale operational monitoring of local phenomena with low backscatter signatures

Modeling Microwave Scattering From Rough Sea Ice Surfaces

In this paper, COMSOL Multiphysics® was used to simulate the microwave scattering from the rough sea ice surface. A nonperiodic model and a periodic model were built. The nonperiodic model considers the rough surface of finite length and introduces a tapered incident wave. In this model, the strategy of total and scattered-field decomposition (TSFD) was used to formulate the finite-element method (FEM). The computational area was split into a scattered-field region and a total-field region so that the incident wave can be impressed closer to the rough sea ice surface. The periodic model considers the periodic rough surface by introducing Floquet periodic boundary conditions. The incident wave is excited by the port boundary condition so this model is based on the total-field formulation. The two models were tested to simulate the radar cross section (RCS) of scattering from sea ice surfaces at C band (frequency 5.4GHz). The results were compared with the Small Perturbation Method (SPM) and good agreements were achieved

On Importance of Off-Diagonal Elements in the Polarimetric Covariance Matrix: A Sea Ice Application Perspective

Poster presentation at the ESA Polinsar Biomass 2023 conference, 19.06.23 - 23.06.23 in Espaces Vanel, Toulouse. https://polinsar-biomass2023.esa.int/

Numerical Analysis of Microwave Scattering from Layered Sea Ice Based on the Finite Element Method

Source at https://doi.org/10.3390/rs10091332.A two-dimensional scattering model based on the Finite Element Method (FEM) is built for simulating the microwave scattering of sea ice, which is a layered medium. The scattering problem solved by the FEM is formulated following a total- and scattered-field decomposition strategy. The model set-up is first validated with good agreements by comparing the results of the FEM with those of the small perturbation method and the method of moment. Subsequently, the model is applied to two cases of layered sea ice to study the effect of subsurface scattering. The first case is newly formed sea ice which has scattering from both air–ice and ice–water interfaces. It is found that the backscattering has a strong oscillation with the variation of sea ice thickness. The found oscillation effects can increase the difficulty of retrieving the thickness of newly formed sea ice from the backscattering data. The second case is first-year sea ice with C-shaped salinity profiles. The scattering model accounts for the variations in the salinity profile by approximating the profile as consisting of a number of homogeneous layers. It is found that the salinity profile variations have very little influence on the backscattering for both C- and L-bands. The results show that the sea ice can be considered to be homogeneous with a constant salinity value in modelling the backscattering and it is difficult to sense the salinity profile of sea ice from the backscattering data, because the backscattering is insensitive to the salinity profile

Numerical Simulation of Microwave Scattering from Sea Ice Based on The Finite Element Method

Embargo of 24 months from date of publishing on accepted manuscript version.Link to publisher's version:Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International Copyright notice: “© © 20xx IEEE policy"In this paper, a 2-dimensional scattering model for sea ice based on the Finite Element Method (FEM) is presented. The scattering problem is formulated following a physics-separate strategy. The wave in the air domain is expressed by the scattered field formulation, while the wave in the sea ice domain is expressed by the total field formulation. The two separate physics and formulations are coupled through the boundary conditions at the air-sea ice interface. The proposed FEM is tested for simulating the radar cross section (RCS) of homogeneous sea ice at C and L bands. By comparing the results of the FEM with the Small Perturbation Method (SPM), good agreements are achieve