Inverse electromagnetic scattering models for sea ice

Journal ArticleInverse scattering algorithms for reconstructing the physical properties of sea ice from scattered electromagnetic field data are presented. The development of these algorithms has advanced the theory of remote sensing, particularly in the microwave region, and has the potential to form the basis for a new generation of techniques for recovering sea ice properties, such as ice thickness, a parameter of geophysical and climatological importance. Moreover, the analysis underlying the algorithms has led to significant advances in the mathematical theory of inverse problems

Laboratory Studies of the Electromagnetic Properties of Saline Ice: A Multi-disciplinary Research Plan Submitted to the Office of Naval Research

This plan describes laboratory and theoretical research to be carried out under the Sea Ice Electromagnetics Accelerated Research Initiative of the Office of Naval Research. The plan is built around three broad objectives: 1) to understand the mechanisms and processes that link the orphological/physical and the electromagnetic properties of sea ice; 2) to further develop and verify predictive models for the interaction of visible, infrared and microwave radiation with sea ice; 3) to develop and verify selected techniques in the mathematical theory of inverse scattering that are applicable to problems arising in the interaction of EM radiation with sea ice. The plan will be executed by over 30 investigators from 15 institutions. Research includes measuring and quantifying the physical properties of sea ice, collecting radiometric signatures of different ice types and morphologies, developing and testing forward models of scattering and emission from sea ice, and developing and testing inverse models to extract geophysical data about sea ice from remotely sensed data. Experiments will begin in January of 1993 at the Cold Regions Research and Engineering Laboratory in Hanover, New Hampshire. Work will focus around studies on the Geophysical Research Facility which is a new, concrete lined pool filled with saline water. The facility can be shielded from local fluctuations in weather by using a movable roof and refrigerated blanket. Three measurement series are planned for the winter of 1993. These will focus on collecting data on the microwave and optical properties of an undeformed ice sheet grown from the melt. Measurements to resolve the contributions of volume and surface scattering to sea ice signatures will be performed on an artificially roughened ice sheet. A snow covered ice sheet will be created to study the effects of brine wicking and scattering from snow grains on electromagnetic signatures. Data from these measurements will be used to evaluate the performance of existing forward models. The data will also be used to begin the development of inverse models.The Office of Naval Researc

Investigation of Sea Ice Using Multiple Synthetic Aperture Radar Acquisitions

The papers of this thesis are not available in Munin. Paper I: Yitayew, T. G., Ferro-Famil, L., Eltoft, T. & Tebaldini, S. (2017). Tomographic imaging of fjord ice using a very high resolution ground-based SAR system. Available in IEEE Transactions on Geoscience and Remote Sensing, 55 (2):698-714. Paper II: Yitayew, T. G., Ferro-Famil, L., Eltoft, T. & Tebaldini, S. (2017). Lake and fjord ice imaging using a multifrequency ground-based tomographic SAR system. Available in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(10):4457-4468. Paper III: Yitayew, T. G., Divine, D. V., Dierking, W., Eltoft, T., Ferro-Famil, L., Rosel, A. & Negrel, J. Validation of Sea ice Topographic Heights Derived from TanDEMX Interferometric SAR Data with Results from Laser Profiler and Photogrammetry. (Manuscript).The thesis investigates imaging in the vertical direction of different types of ice in the arctic using synthetic aperture radar (SAR) tomography and SAR interferometry. In the first part, the magnitude and the positions of the dominant scattering contributions within snow covered fjord and lake ice layers are effectively identified by using a very high resolution ground-based tomographic SAR system. Datasets collected at multiple frequencies and polarizations over two test sites in Tromsø area, northern Norway, are used for characterizing the three-dimensional response of snow and ice. The presented experimental results helped to improve our understanding of the interaction between radar waves and snow and ice layers. The reconstructed radar responses are also used for estimating the refractive indices and the vertical positions of the different sub-layers of snow and ice. The second part of the thesis deals with the retrieval of the surface topography of multi-year sea ice using SAR interferometry. Satellite acquisitions from TanDEM-X over the Svalbard area are used for analysis. The retrieved surface height is validated by using overlapping helicopter-based stereo camera and laser profiler measurements, and a very good agreement has been found. The work contributes to an improved understanding regarding the potential of SAR tomography for imaging the vertical scattering distribution of snow and ice layers, and for studying the influence of both sensor parameters such as its frequency and polarization and scene properties such as layer stratification, air bubbles and small-scale roughness of the interfaces on snow and ice backscattered signal. Moreover, the presented results reveal the potential of SAR interferometry for retrieving the surface topography of sea ice

The sea-ice detection capability of synthetic aperture radar

Climate change, increasing activities in areas like offshore oil and gas exploration, marine transport, eco-tourism, in additional to the usual activities of northerners resident are leading to reductions in sea ice. Therefore, there is an urgent need for improvement in the sea ice detection in polar areas. Starting from the mechanism of electromagnetic scattering, based on an empirical dielectric constant model, we apply EM multi-reflection and transmission formulas for coefficients between the air-ice interface and sea water-ice interface to develop a model for estimating the capability of detection of sea ice and ice thickness based on a pulse radar system, synthetic aperture radar (SAR). Although the dielectric constant of sea ice is less than that of sea water, this model can provide a rational methodology as the normalized radar cross section (NRCS) of sea ice is larger than that of sea water due to multiple reflections. The numerical simulations of this model showed that the convergence rate is rapid. With 3 or 4 reflections and transmissions (depending on temperature, salinity, and dielectric constants of sea ice and water), truncation errors can be satisfied using theoretical considerations and practical applications. The model is applied to estimate the capability of SAR to discriminate ice from water. The numerical results suggested that the model ability to measure ice thickness decreases with increasing radar incident angles and increases with increasing radar pulse width. Reflection and transmission coefficients decrease monotonically with ice thickness and are saturated for ice thicknesses above a certain critical value which depends on SAR incidence angle, frequency and dielectric constants of sea ice. The capability to detect ice thickness for given different bands of pulse radar widths can be estimated with this model

Advancements in Measuring and Modeling the Mechanical and Hydrological Properties of Snow and Firn: Multi-sensor Analysis, Integration, and Algorithm Development

Estimating snow mechanical properties – such as elastic modulus, stiffness, and strength – is important for understanding how effectively a vehicle can travel over snow-covered terrain. Vehicle instrumentation data and observations of the snowpack are valuable for improving the estimates of winter vehicle performance. Combining in-situ and remotely-sensed snow observations, driver input, and vehicle performance sensors requires several techniques of data integration. I explored correlations between measurements spanning from millimeter to meter scales, beginning with the SnowMicroPenetrometer (SMP) and instruments applied to snow that were designed for measuring the load bearing capacity and the compressive and shear strengths of roads and soils. The spatial distribution of snow’s mechanical properties is still largely unknown. From this initial work, I determined that snow density remains a useful proxy for snowpack strength. To measure snow density, I applied multi-sensor electromagnetic methods. Using spatially distributed snowpack, terrain, and vegetation information developed in the subsequent chapters, I developed an over-snow vehicle performance model. To measure the vehicle performance, I joined driver and vehicle data in the coined Normalized Difference Mobility Index (NDMI). Then, I applied regression methods to distribute NDMI from spatial snow, terrain, and vegetation properties. Mobility prediction is useful for the strategic advancement of warfighting in cold regions. The security of water resources is climatologically inequitable and water stress causes international conflict. Water resources derived from snow are essential for modern societies in climates where snow is the predominant source of precipitation, such as the western United States. Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. In this work, I combined high-spatial resolution light detection and ranging (LiDAR) measured snow depths with ground-penetrating radar (GPR) measurements of two-way travel-time (TWT) to solve for snow density. Then using LiDAR derived terrain and vegetation features as predictors in a multiple linear regression, the density observations are distributed across the SnowEx 2020 study area at Grand Mesa, Colorado. The modeled density resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation. The integration of radar and LiDAR sensors shows promise as a technique for estimating SWE across entire river basins and evaluating observational- or physics-based snow-density models. Accurate estimation of SWE is a means of water security. In our changing climate, snow and ice mass are being permanently lost from the cryosphere. Mass balance is an indicator of the (in)stability of glaciers and ice sheets. Surface mass balance (SMB) may be estimated by multiplying the thickness of any annual snowpack layer by its density. Though, unlike applications in seasonal snowpack, the ages of annual firn layers are unknown. To estimate SMB, I modeled the firn depth, density, and age using empirical and numerical approaches. The annual SMB history shows cyclical patterns representing the combination of atmospheric, oceanic, and anthropogenic climate forcing, which may serve as evaluation or assimilation data in climate model retrievals of SMB. The advancements made using the SMP, multi-channel GPR arrays, and airborne LiDAR and radar within this dissertation have made it possible to spatially estimate the snow depth, density, and water equivalent in seasonal snow, glaciers, and ice sheets. Open access, process automation, repeatability, and accuracy were key design parameters of the analyses and algorithms developed within this work. The many different campaigns, objectives, and outcomes composing this research documented the successes and limitations of multi-sensor estimation techniques for a broad range of cryosphere applications

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Review Article: Global Monitoring of Snow Water Equivalent Using High-Frequency Radar Remote Sensing

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth\u27s surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth\u27s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world\u27s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth\u27s cold regions\u27 ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Thickness retrieval and emissivity modeling of thin sea ice at L-band for SMOS satellite observations

In this study we have developed an empirical retrieval for thickness of young and first-year ice during the freeze up period for the L-band passive microwave radiometer Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) on the Soil Moisture and Ocean Salinity (SMOS) satellite. The retrieval is based on intensity and polarization difference using the incidence angle range of 40° to 50° and is validated using data from airborne EM-Bird, Moderate-resolution Imaging Spectroradiometer (MODIS) thermal imagery, and self consistency checks for ice thicknesses up to 50 cm with an error of 30 % on average. In addition, we modeled the microwave emission for Arctic first-year ice using the sea ice version of the Microwave Emission Model of Layered Snowpacks (MEMLS). The sea ice conditions used as input for MEMLS were generated using a thermodynamic energy balance model (based on the Crocus model) driven by reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF). From unexpected features in the modeled microwave emission and disagreements with the empirically trained SMOS retrieval several shortcomings of the energy balance model and MEMLS were identified and corrected. The corrections include a treatment of mismatch of layer definition between the energy balance model and MEMLS, an adaptation of the reflection coefficient for lossy media in MEMLS, and several smaller corrections. For comparison, two simple models ignoring volume scattering, one incoherent and one coherent, were set up and were found to be able to reproduce the results of the more complex MEMLS model on average. With the simple models, the effects of thin coherent layers, the snow cover, the interface roughness and three different dielectric mixture models for sea ice were explored. It was found that the choice of the mixture model is essential for the relation of sea ice thickness to brightness temperatures in L-band, suggesting sea ice thickness sensitivities from few centimeters to several meters for salinity conditions of the global oceans. The interface properties, especially at the sea ice bottom, were found to be a major uncertainty source when modeling the microwave emission of thin sea ice. In addition, the variability in snow depth, the interface roughness, and the ice surface salinity and temperature were found to have a similar influence on the resulting brightness temperatures, with a strong effect on horizontally (up to 30 K) and weak effect on vertically polarized radiation (up to 10 K) for temperatures below 260 K. A model for simulating coherent microwave emission for thickness distributions of ice and snow was prepared to overcome weaknesses from the single thickness coherent and incoherent models. Comparison to the incoherent model showed that for realistic snow depth distributions obtained from Operation IceBridge (OIB) coherence effects can change the brightness temperatures on the scale of a SMOS footprint up to 10 K in horizontal polarization. These findings suggest that the retrieval for the thickness of thin sea ice with satellite based L-band sensors yield higher uncertainties than expected from earlier studies

Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version