Radar Systems for Glaciology

This chapter deals with radar systems, measurements and instrumentation employed to study the internal core and bedrock of ice sheets in glaciology. The Earth's ice sheets are in Greenland and Antarctica. They cover about 10% of the land surface of the planet. The total accumulated ice comprises 90% of the global fresh water reserve. These ice sheets, associated with the ocean environment, provide a major heat sink which significantly modulates climate. Glaciology studies aim to understand the various process involved in the flow (dynamics), thermodynamics, and long-term behaviour of ice sheets. Studies of large ice masses are conducted in adverse environmental conditions (extreme cold, long periods of darkness). The development of remote sensing techniques have played an important role in obtaining useful results. The most widely used techniques are radar systems, employed since the 1950s in response to a need to provide a rapid and accurate method of measuring ice thickness. Year by year, polar research has become increasingly important because of global warming. Moreover, the discovery of numerous subglacial lake areas (water entrapped beneath the ice sheets) has attracted scientific interest in the possible existence of water circulation between lakes or beneath the ice (Kapitsa et al., 2006; Wingham et al., 2006; Bell et al., 2007). Recent studies in radar signal shape and amplitude could provide evidence of water circulation below the ice (Carter 2007, Oswald and Gogineni 2008). In this chapter the radar systems employed in glaciology, radio echo sounding (RES), are briefly described with some interesting results. RES are active remote sensing systems that utilize electromagnetic waves that penetrate the ice. They are used to obtain information about the electromagnetic properties of different interfaces (for example rock-ice, ice-water, seawater-ice) that reflect the incoming signal back to the radar. RES systems are characterized by a high energy (peak power from 10 W to 10 KW) variable transmitted pulse width (about from 0.5 ns to several microseconds) in order to investigate bedrock characteristics even in the thickest zones of the ice sheets (4755 m is the deepest ice thickness measured in Antarctica using a RES system). Changing the pulse length or the transmitted signal frequencies it is possible to investigate particular ice sheet details with different resolution. Long pulses allows transmission of higher power than short pulses, penetrating the thickest parts of the ice sheets but, as a consequence, resolution decreases. For example, the GPR system, commonly used in geophysics for rock, soil, ice, fresh water, pavement and structure characterization, employs a very short transmitted pulse (0.5 ns to 10 ns) that allow detailing of the shallow parts of an ice sheet (100-200 m in depth) (Reynolds 1997). Consequently, in recent years, GPR systems are also employed by explorers to find hidden crevasses on glaciers for safety. RES surveys have been widely employed in Antarctic ice sheet exploration and they are still an indispensable tool for mapping bedrock morphologies and properties of the last unexplored continent on Earth. The advantage of using these remote sensing techniques is that they allow large areas to be covered, in good detail and in short times using platforms like aeroplanes and surface vehicles

Earth Resources: A continuing bibliography with indexes, issue 1

This bibliography lists 616 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1974 and March 1974. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory, natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Validation of Airborne FMCW Radar Measurements of Snow Thickness Over Sea Ice in Antarctica

Antarctic sea ice and its snow cover are integral components of the global climate system, yet many aspects of their vertical dimensions are poorly understood, making their representation in global climate models poor. Remote sensing is the key to monitoring the dynamic nature of sea ice and its snow cover. Reliable and accurate snow thickness data are currently a highly sought after data product. Remotely sensed snow thickness measurements can provide an indication of precipitation levels, predicted to increase with effects of climate change in the polar regions. Airborne techniques provide a means for regional-scale estimation of snow depth and distribution. Accurate regional-scale snow thickness data will also facilitate an increase in the accuracy of sea ice thickness retrieval from satellite altimeter freeboard estimates. The airborne data sets are easier to validate with in situ measurements and are better suited to validating satellite algorithms when compared with in situ techniques. This is primarily due to two factors: better chance of getting coincident in situ and airborne data sets and the tractability of comparison between an in situ data set and the airborne data set averaged over the footprint of the antennas. A 28-GHz frequency modulated continuous wave (FMCW) radar loaned by the Center for Remote Sensing of Ice Sheets to the Australian Antarctic Division is used to measure snow thickness over sea ice in East Antarctica. Provided with the radar design parameters, the expected performance parameters of the radar are summarized. The necessary conditions for unambiguous identification of the airsnow and snowice layers for the radar are presented. Roughnesses of the snow and ice surfaces are found to be dominant determinants in the effectiveness of layer identification for this radar. Finally, this paper presents the first in situ validated snow thickness estimates over sea ice in Antarctica derived from an FMCW radar on a helicopterborne platform

Development of high-precision snow mapping tools for Arctic environments

Le manteau neigeux varie grandement dans le temps et l’espace, il faut donc de nombreux points d’observation pour le décrire précisément et ponctuellement, ce qui permet de valider et d’améliorer la modélisation de la neige et les applications en télédétection. L’analyse traditionnelle par des coupes de neige dévoile des détails pointus sur l’état de la neige à un endroit et un moment précis, mais est une méthode chronophage à laquelle la distribution dans le temps et l’espace font défaut. À l’opposé sur la fourchette de la précision, on retrouve les solutions orbitales qui couvrent la surface de la Terre à intervalles réguliers, mais à plus faible résolution. Dans l’optique de recueillir efficacement des données spatiales sur la neige durant les campagnes de terrain, nous avons développé sur mesure un système d’aéronef télépiloté (RPAS) qui fournit des cartes d’épaisseur de neige pour quelques centaines de mètres carrés, selon la méthode Structure from motion (SfM). Notre RPAS peut voler dans des températures extrêmement froides, au contraire des autres systèmes sur le marché. Il atteint une résolution horizontale de 6 cm et un écart-type d’épaisseur de neige de 39 % sans végétation (48,5 % avec végétation). Comme la méthode SfM ne permet pas de distinguer les différentes couches de neige, j’ai développé un algorithme pour un radar à onde continue à modulation de fréquence (FM-CW) qui permet de distinguer les deux couches principales de neige que l’on retrouve régulièrement en Arctique : le givre de profondeur et la plaque à vent. Les distinguer est crucial puisque les caractéristiques différentes des couches de neige font varier la quantité d’eau disponible pour l’écosystème lors de la fonte. Selon les conditions sur place, le radar arrive à estimer l’épaisseur de neige selon un écart-type entre 13 et 39 %. vii Finalement, j’ai équipé le radar d’un système de géolocalisation à haute précision. Ainsi équipé, le radar a une marge d’erreur de géolocalisation d’en moyenne <5 cm. À partir de la mesure radar, on peut déduire la distance entre le haut et le bas du manteau neigeux. En plus de l’épaisseur de neige, on obtient également des points de données qui permettent d’interpoler un modèle d’élévation de la surface solide sous-jacente. J’ai utilisé la méthode de structure triangulaire (TIN) pour toutes les interpolations. Le système offre beaucoup de flexibilité puisqu’il peut être installé sur un RPAS ou une motoneige. Ces outils épaulent la modélisation du couvert neigeux en fournissant des données sur un secteur, plutôt que sur un seul point. Les données peuvent servir à entraîner et à valider les modèles. Ainsi améliorés, ils peuvent, par exemple, permettre de prédire la taille, le niveau de santé et les déplacements de populations d’ongulés, dont la survie dépend de la qualité de la neige. (Langlois et coll., 2017.) Au même titre que la validation de modèles de neige, les outils présentés permettent de comparer et de valider d’autres données de télédétection (par ex. satellites) et d’élargir notre champ de compréhension. Finalement, les cartes ainsi créées peuvent aider les écologistes à évaluer l’état d’un écosystème en leur donnant accès à une plus grande quantité d’information sur le manteau neigeux qu’avec les coupes de neige traditionnelles.Abstract: Snow is highly variable in time and space and thus many observation points are needed to describe the present state of the snowpack accurately. This description of the state of the snowpack is necessary to validate and improve snow modeling efforts and remote sensing applications. The traditional snowpit analysis delivers a highly detailed picture of the present state of the snow in a particular location but lacks the distribution in space and time as it is a time-consuming method. On the opposite end of the spatial scale are orbital solutions covering the surface of the Earth in regular intervals, but at the cost of a much lower resolution. To improve the ability to collect spatial snow data efficiently during a field campaign, we developed a custom-made, remotely piloted aircraft system (RPAS) to deliver snow depth maps over a few hundred square meters by using Structure-from-Motion (SfM). The RPAS is capable of flying in extremely low temperatures where no commercial solutions are available. The system achieves a horizontal resolution of 6 cm with snow depth RMSE of 39% without vegetation (48.5% with vegetation) As the SfM method does not distinguish between different snow layers, I developed an algorithm for a frequency modulated continuous wave (FMCW) radar that distinguishes between the two main snow layers that are found regularly in the Arctic: “Depth Hoar” and “Wind Slab”. The distinction is important as these characteristics allow to determine the amount of water stored in the snow that will be available for the ecosystem during the melt season. Depending on site conditions, the radar estimates the snow depth with an RMSE between 13% and 39%. v Finally, I equipped the radar with a high precision geolocation system. With this setup, the geolocation uncertainty of the radar on average < 5 cm. From the radar measurement, the distance to the top and the bottom of the snowpack can be extracted. In addition to snow depth, it also delivers data points to interpolate an elevation model of the underlying solid surface. I used the Triangular Irregular Network (TIN) method for any interpolation. The system can be mounted on RPAS and snowmobiles and thus delivers a lot of flexibility. These tools will assist snow modeling as they provide data from an area instead of a single point. The data can be used to force or validate the models. Improved models will help to predict the size, health, and movements of ungulate populations, as their survival depends on it (Langlois et al., 2017). Similar to the validation of snow models, the presented tools allow a comparison and validation of other remote sensing data (e.g. satellite) and improve the understanding limitations. Finally, the resulting maps can be used by ecologist to better asses the state of the ecosystem as they have a more complete picture of the snow cover on a larger scale that it could be achieved with traditional snowpits

Re-evaluating Scattering Mechanisms in Snow-Covered Freshwater Lake Ice Containing Bubbles Using Polarimetric Ground-based and Spaceborne Radar Data

Lakes are a prominent feature of the sub-Arctic and Arctic regions of North America, covering up to 40% of the landscape. Seasonal ice cover on northern lakes afford habitat for several flora and fauna species, and provide drinking water and overwintering fishing areas for local communities. The presence of lake ice influences lake-atmosphere exchanges by modifying the radiative properties of the lake surface and moderating the transfer of heat to the atmosphere. The thermodynamic aspects of lakes exhibit a pronounced effect on weather and regional climate, but are also sensitive to variability in climate forcings such as air temperature and snow fall, acting as proxy indicators of climate variability and change. To refine the understanding of lake-climate interactions, improved methods of monitoring lake ice properties are needed. Manual lake ice monitoring stations have dropped significantly since the 1990s and existing stations are restricted to populated and coastal regions. Recently, studies have indicated the use of radar remote sensing as a viable option for the monitoring of small lakes in remote regions due to its high spatial resolution and imaging capability independent of solar radiation or cloud cover. Active microwave radar in the frequency range of 5 – 10 GHz have successfully retrieved lake ice information pertaining to the physical status of the ice cover and areas that are frozen to bed, but have not been demonstrated as effective for the derivation of on-ice snow depth. In the 10 – 20 GHz range, radar has been shown to be sensitive to terrestrial snow cover, but has not been investigated over lakes. Utilizing a combination of spaceborne and ground-based radar systems spanning a range of 5 – 17 GHz, simulations from the Canadian Lake Ice Model (CLIMo), and ice thickness information from a shallow water ice profiler (SWIP), this research aimed to further our understanding of lake ice scattering sources and mechanisms for small freshwater lakes in the sub-Arctic. Increased comprehension of scattering mechanisms in ice advances the potential for the derivation of lake ice properties, including on-ice snow depth, lake ice thickness and identification of surface ice types. Field observations of snow-covered lake ice were undertaken during the winter seasons of 2009-2010 and 2010-2011 on Malcolm Ramsay Lake, near Churchill Manitoba. In-situ snow and ice observations were coincident with ground-based scatterometer (UW-Scat) and spaceborne synthetic aperture radar (SAR) acquisitions. UW-Scat was comprised of two fully polarimetric frequency modulated continuous wave (FMCW) radars with centre frequencies of 9.6 and 17.2 GHz (X- and Ku-bands, respectively). SAR observations included fine-beam fully polarimetric RADARSAT-2 acquisitions, obtained coincident to UW-Scat observations during 2009-2010. Three experiments were conducted to characterize and evaluate the backscatter signatures from snow-covered freshwater ice coincident to in-situ snow and ice observations. To better understand the winter backscatter (σ°) evolution of snow covered ice, three unique ice cover scenarios were observed and simulated using a bubbled ice σ° model. The range resolution of UW-SCAT provided separation of microwave interaction at the snow/ice interface (P1), and within the ice volume (P2). Ice cores extracted at the end of the observation period indicated that a considerable σ° increase at P2 of approximately 10 – 12 decibels (dB) HH/VV at X- and Ku-band occurred coincident to the timing of tubular bubble development in the ice. Similarly, complexity of the ice surface (high density micro-bubbles and snow ice) resulted in increased P1 σ° at X- and Ku-band at a magnitude of approximately 7 dB. P1 observations also indicated that Ku-band was sensitive to snowpack overlying lake ice, with σ° exhibiting a 5 (6) dB drop for VV (HH) when ~ 60 mm SWE is removed from the scatterometer field of view. Observations indicate that X-band was insensitive to changes in overlying snowpack within the field of view. A bubbled ice σ° model was developed using the dense medium radiative transfer theory under the Quasi-Crystalline Approximation (DMRT-QCA), which treated bubbles as spherical inclusions within the ice volume. Results obtained from the simulations demonstrated the capability of the DMRT model to simulate the overall magnitude of observed σ° using in-situ snow and ice measurements as input. This study improved understanding of microwave interaction with bubble inclusions incorporated at the ice surface or within the volume. The UW-Scat winter time series was then used to derive ice thickness under the assumption of interactions in range occurring at the ice-snow and ice-water interface. Once adjusted for the refractive index of ice and slant range, the distance between peak returns agreed with in-situ ice thickness observations. Ice thicknesses were derived from the distance of peak returns in range acquired in off-nadir incidence angle range 21 - 60°. Derived ice thicknesses were compared to in-situ measurements provided by the SWIP and CLIMo. Median ice thicknesses derived using UW-Scat X- and Ku-band observations agreed well with in-situ measurements (RMSE = 0.053 and 0.045 m), SWIP (RMSE = 0.082 and 0.088 m) and Canadian Lake Ice Model (CLIMo) simulations using 25% of terrestrial snowpack scenario (RMSE = 0.082 and 0.079), respectively. With the launch of fully polarimetric active microwave satellites and upcoming RADARSAT Constellation Mission (RCM), the utility of polarimetric measurements was observed for freshwater bubbled ice to further investigate scattering mechanisms identified by UW-Scat. The 2009-2010 time series of UW-Scat and RADARSAT-2 (C-band) fully polarimetric observations coincident to in-situ snow and ice measurements were acquired to identify the dominant scattering mechanism in bubbled freshwater lake ice. Backscatter time series at all frequencies show increases from the ice-water interface prior to the inclusion of tubular bubbles in the ice column based on in-situ observations, indicating scattering mechanisms independent of double-bounce scatter, contrary to the longstanding hypothesis of double-bounce scatter off tubular bubbles and the ice-water interface. The co-polarized phase difference of interactions at the ice-water interface from both UW-Scat and SAR observations were centred at 0°, indicating a scattering regime other than double bounce. A Yamaguchi three-component decomposition of the time series suggested the dominant scattering mechanism to be single-bounce off the ice-water interface with appreciable surface roughness or preferentially oriented facets. Overall, this work provided new insight into the scattering sources and mechanisms within snow-covered freshwater lake ice containing spherical and tubular bubbles

Acoustic sounding of snow water equivalent

An acoustic frequency-swept wave was investigated as a means for determining Snow Water Equivalent (SWE) in cold wind-swept prairie and sub-alpine environments. Building on previous research conducted by investigators who have examined the propagation of sound in snow, digital signal processing was used to determine acoustic pressure wave reflection coefficients at the interfaces between 'layers' indicative of changes in acoustic impedance. Using an iterative approach involving boundary conditions at the interfaces, the depth-integrated SWE was determined using the Berryman equation from porous media physics. Apparatuses used to send and receive sound waves were designed and deployed during the winter season at field sites situated near the city of Saskatoon, Saskatchewan, and in Yoho National Park, British Columbia. Data collected by gravimetric sampling was used as comparison for the SWE values determined by acoustic sounding. The results are encouraging and suggest that this procedure is similar in accuracy to SWE data collected using gravimetric sampling. Further research is required to determine the applicability of this technique for snow situated at other geographic locations

Remote Sensing of Earth Resources (1970 - 1973 supplement): A literature survey with indexes. Section 2: Indexes

Documents related to the identification and evaluation by means of sensors in spacecraft and aircraft of vegetation, minerals, and other natural resources, and the techniques and potentialities of surveying and keeping up-to-date inventories of such riches are cited. These documents were announced in the NASA scientific and technical information system between March 1970 and December 1973

Literature review of the remote sensing of natural resources

Abstracts of 596 documents related to remote sensors or the remote sensing of natural resources by satellite, aircraft, or ground-based stations are presented. Topics covered include general theory, geology and hydrology, agriculture and forestry, marine sciences, urban land use, and instrumentation. Recent documents not yet cited in any of the seven information sources used for the compilation are summarized. An author/key word index is provided

Earth Resources: A continuing bibliography with indexes, Issue 4

This bibliography lists 651 reports, articles, and other documents introduced into the NASA scientific and technical information system between October 1974 and December 1974. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis