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We propose a lightweight radar that autonomously measures snow depth over sea ice from an unmanned aerial vehicle (UAV). Development of this snow radar and its integration with an octocopter UAV is presented. Field trials of the UAV-mounted snow radar, conducted in Antarctica during the summer season of 2017/2018, are also described. The radar allows measurements of snow depths on sea ice between 10 and 100 cm. Additional reflections due to internal layers within the snow are evident at a few measurement points. The snow radar is evaluated for various flight parameters: stationary; flying at speeds between 1 and 3 m/s, and at heights from 5 to 15 m. Evaluation of snow-depth results indicates that a depth accuracy of ±3.2 cm is achieved with stationary measurements, and of ±9.1 cm with measurements at the various flight speeds. [ABSTRACT FROM AUTHOR]

The surface mass balance (SMB) of West Antarctica is an important glaciological input to understanding polar climate and sea-level rise but with historically poor in situ data coverage. Previous studies demonstrate the utility of frequency-modulated continuous-wave radar to image subsurface layering in ice sheets, providing an additional source of data with which to estimate SMB. Traditional methods, however, require time-intensive manual oversight. Here, we present a probabilistic, fully automated approach to estimate annual SMB and uncertainties from radar echograms using successive peak-finding and weighted neighborhood search algorithms with the Monte Carlo simulations based on annual-layer likelihood scores. We apply this method to ground-based and airborne radar in a 175-km transect of the West Antarctic interior and compare the results to traditional manual methods and independent estimates from firn cores. The method demonstrates an automated estimation of SMB across a range of accumulation rates (100–450-mm water equivalent per year) and layer gradients up to 2 m/km. Based on likelihood-weighted F-scores, automated layer picks have a success rate between 64% and 84.6% compared with manually picked layers for three validation sites dispersed across the region. Comparisons between the automated SMB estimates and independent firn cores show a bias of 24 ± 70-mm water equivalent per year (12% ± 35% water equivalent of the in situ mean accumulation rate) although individual core site biases differ. This new approach permits the fully automated extraction of annual SMB rates and should be broadly and readily applicable to previously collected and ongoing radar data sets across polar regions. [ABSTRACT FROM AUTHOR]

In order to measure the variance of wind velocity, which is contributed from turbulence, via radar observations, it is necessary to remove the unwanted contribution from strong horizontal velocity components through the finite beamwidth of the radar. This effect is referred to as beam broadening. Although the amount of beam broadening has thus far been calculated based on the approximating assumption that the pattern of the beam is rotationally symmetric and has very low sidelobes, we need to take a more theoretical approach to radar—one that does not have a simple beam pattern like the Antarctic Program of the Antarctic Syowa Station (PANSY) radar (69S, 39E). In this article, we clarify the theoretical relationship in a very simple form between the turbulence spectrum, which is directly associated with the variance of turbulence, two-way beam patterns, and the observed spectrum, using autocorrelation functions (ACFs). The theory is thoroughly universal and applicable to any type of atmospheric radar, such that we can quantitatively analyze radar observation systems. Furthermore, we propose a “debroadening” algorithm based directly on this theory and from calculations of the general maximum likelihood (ML). We performed numerical simulations that validate our theory and the algorithm. [ABSTRACT FROM AUTHOR]

During the past decades, radar sounder (RS) instruments have been effectively used to detect subglacial lakes (SLs). SLs appear as flat, smooth, and bright reflectors in RS radargrams. The visual interpretation has been the main approach to SL detection in radargrams. However, this approach is subjective and inappropriate for processing large amounts of radargrams. While the analysis of RS data for understanding the subglacial hydrology has recently received increased attention, the literature on the development of automatic methods specifically designed for SL detection is still limited. In order to fill this gap, in this paper, we propose a novel automatic technique for SL detection. The technique is made up of two steps: 1) feature extraction and 2) automatic detection. In the first step, we define and extract three families of features for discriminating between the lake and nonlake radar reflections. The features model locally the basal topography, the shape of the basal reflected waveforms, and the statistical properties of the basal signal. In the second step, we provide the features as input to a support vector machine classifier to perform the automatic SL detection. The proposed technique has been applied to radargrams acquired over two large regions in East Antarctica and Siple Coast. The obtained results, which are validated both quantitatively and qualitatively, confirm the robustness of the features and their capabilities to effectively characterize SLs. Moreover, they prove the potentiality of the method to process large amounts of radargrams and update the current SL inventory. [ABSTRACT FROM AUTHOR]

Monitoring long-term, large-scale changes in the Antarctic sea ice thickness is not currently possible due to the sampling constraints of the ship-based and airborne observations which comprise most of the available thickness data. Satellite radar altimetry has been used to measure sea ice thickness variability in the Arctic where it is assumed that the highest amplitude radar return originates from the snow/ice interface as the Arctic snow is cold and dry; however, this may not be the case in the Antarctic due to more complex snow stratigraphy caused by warmer winter temperatures and frequent snow flooding. We present the first measurements of radar penetration into snow cover on Antarctic sea ice in the Ku-band at which satellite radar altimeters operate. Data were taken using a sled-borne radar on sea ice off East Antarctica during September and October 2007. Radar data and field measurements of snow density, thickness, wetness, and layers were taken over a range of locations including snow packs with flooding, hard crusts, and icy layers. Detailed snow pit studies showed that the snow/ice interface was the dominant scattering surface only for snow without morphological features or flooding. Analysis of transect data showed that the mean depth of the dominant scattering surface of the radar was only around 50% of the mean measured snow depth, indicating that the dominant scattering surface was not always the snow/ice interface for the Antarctic sea ice surveyed. [ABSTRACT FROM AUTHOR]

Snow accumulation in remote regions, such as Green- land and Antarctica, is a key factor for estimating the Earth's ice mass balance. In situ data are sparse; hence, they are useful to derive snow accumulation from remote sensing observations, such as microwave thermal emission and radar brightness. These data are usually interpreted using electromagnetic models in which volume scattering is the dominant mechanism. The main limitation of this approach is that microwave brightness is not well related to backscatter if the ice sheet is layered. Because larger grain size and thicker annual layers both increase radar image brightness, with the first corresponding to lower accumulation rate and the second to higher accumulation rate, models of radar brightness alone cannot accurately reflect accumulation. Consideration of correlation measurements can also resolve this ambiguity. We introduce an interferometric ice scattering model that relates the interferometric synthetic aperture radar correlation and radar brightness to both ice grain size and hoar layer spacing in the dry-snow zone of Greenland. We use this model and the European Remote Sensing satellite radar observations to derive several parameters related to snow accumulation rates in a small area in the dry-snow zone. These parameters show agreement with four in situ core accumulation rate measurements in this area, whereas models using only radar brightness data do not match the observed variation in accumulation rates. [ABSTRACT FROM AUTHOR]