Sensitivity of Radar Altimeter Waveform to Changes in Sea Ice Type at Resolution of Synthetic Aperture Radar

Aldenhoff, Wiebke; Heuzé, Céline; Eriksson, Leif E. B.

doi:10.3390/rs11222602

Cited by 14 publications

(21 citation statements)

References 36 publications

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“…This assumption requires geophysical property information on the overlying snowpack, as well as the underlying sea ice, which can affect the accuracy of the radar height estimate. These geophysical parameters include snow depth, snow density, temperature, salinity, snow grain size, snow surface-sea-ice interface roughness, sea ice density, and seawater density (Landy et al, 2020;Nandan et al, 2020;Landy et al, 2019;Tonboe et al, 2010;Nandan et al, 2017a;Alexandrov et al, 2010;Ricker et al, 2014). The radar height estimate or track point is conceptualized as the scattering surface depth detected by the radar re-tracker algorithm and the floe buoyancy, and in turn it impacts the accuracy of F I and H I estimates (Ricker et al, 2014).…”

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confidence: 99%

Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice

et al. 2021

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Abstract. Owing to differing and complex snow geophysical properties, radar waves of different wavelengths undergo variable penetration through snow-covered sea ice. However, the mechanisms influencing radar altimeter backscatter from snow-covered sea ice, especially at Ka- and Ku-band frequencies, and the impact on the Ka- and Ku-band radar scattering horizon or the “track point” (i.e. the scattering layer depth detected by the radar re-tracker) are not well understood. In this study, we evaluate the Ka- and Ku-band radar scattering horizon with respect to radar penetration and ice floe buoyancy using a first-order scattering model and the Archimedes principle. The scattering model is forced with snow depth data from the European Space Agency (ESA) climate change initiative (CCI) round-robin data package, in which NASA's Operation IceBridge (OIB) data and climatology are included, and detailed snow geophysical property profiles from the Canadian Arctic. Our simulations demonstrate that the Ka- and Ku-band track point difference is a function of snow depth; however, the simulated track point difference is much smaller than what is reported in the literature from the Ku-band CryoSat-2 and Ka-band SARAL/AltiKa satellite radar altimeter observations. We argue that this discrepancy in the Ka- and Ku-band track point differences is sensitive to ice type and snow depth and its associated geophysical properties. Snow salinity is first increasing the Ka- and Ku-band track point difference when the snow is thin and then decreasing the difference when the snow is thick (>0.1 m). A relationship between the Ku-band radar scattering horizon and snow depth is found. This relationship has implications for (1) the use of snow climatology in the conversion of radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both (1) and (2), the impact of using a snow climatology versus the actual snow depth is relatively small on the radar freeboard, only raising the radar freeboard by 0.03 times the climatological snow depth plus 0.03 times the real snow depth. The radar freeboard is a function of both radar scattering and floe buoyancy. This study serves to enhance our understanding of microwave interactions towards improved accuracy of snow depth and sea ice thickness retrievals via the combination of the currently operational and ESA's forthcoming Ka- and Ku-band dual-frequency CRISTAL radar altimeter missions.

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confidence: 99%

Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice

et al. 2021

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“…Under strong winds, open water can present radar backscatter intensities comparable to those of sea ice (an example https://doi.org/10.5194/essd-2020-332 is shown in Figure 4 (b), where the sea surface wind speed varies between approximately 8 m/s and m/s according to the ERA5 reanalysis wind field at synoptic time (Hersbach et al, 2020)). Moreover, sea ice with a smooth surface can have a low backscatter intensity (Aldenhoff et al, 2019) (see Figure 4 (d) for example). These substantial variations in the SAR radar backscatter intensities of sea ice and open water make sea ice segmentation a challenging task.…”

Section: Combination Of S1 Co-and Cross-polarization Datamentioning

confidence: 99%

Arctic sea ice cover data from spaceborne SAR by deep learning

Li¹

2020

Preprint

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Abstract. Widely used sea ice concentration and sea ice cover in polar regions are derived mainly from spaceborne microwave radiometer and scatterometer data, and the typical spatial resolution of these products ranges from several to dozens of kilometers. Due to dramatic changes in polar sea ice, high-resolution sea ice cover data are drawing increasing attention for polar navigation, environmental research, and offshore operations. In this paper, we focused on developing an approach for deriving a high-resolution sea ice cover product for the Arctic using Sentinel-1 (S1) dual-polarization (horizontal-horizontal, HH, and horizontal-vertical, HV) data in extra wide swath (EW) mode. The approach for discriminating sea ice from open water by synthetic aperture radar (SAR) data is based on a modified U-Net architecture, a deep learning network. By employing an integrated stacking model to combine multiple U-Net classifiers with diverse specializations, sea ice segmentation is achieved with superior accuracy over any individual classifier. We applied the proposed approach to over 28,000 S1 EW images acquired in 2019 to obtain sea ice cover products in a high spatial resolution of 400 m. By converting the S1-derived sea ice cover to concentration and then compared with Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration data, showing an average absolute difference of 5.55 % with seasonal fluctuations. A direct comparison with Interactive Multisensor Snow and Ice Mapping System (IMS) daily sea ice cover data achieves an average accuracy of 93.98 %. These results show that the developed S1-derived sea ice cover results are comparable to the AMSR and IMS data in terms of overall accuracy but superior to these data in presenting detailed sea ice cover information, particularly in the marginal ice zone (MIZ). Data are available at: https://doi.org/10.11922/sciencedb.00273 (Wang and Li, 2020).

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“…Convolutional neural networks have been used to estimate sea ice concentration from SAR imagery [35]. However, SAR data is better suited for classification of sea ice types (e.g., with a convolutional neural network [35]) than for SIT estimation due to the ambiguity of the backscatter [36], and is not sensitive to snow depth.…”

Section: Introductionmentioning

confidence: 99%

A Textural Approach to Improving Snow Depth Estimates in the Weddell Sea

Mei

Maksym

2020

Remote Sensing

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The snow depth on Antarctic sea ice is critical to estimating the sea ice thickness distribution from laser altimetry data, such as from Operation IceBridge or ICESat-2. Snow redistributed by wind collects around areas of deformed ice and forms a wide variety of features on sea ice; the morphology of these features may provide some indication of the mean snow depth. Here, we apply a textural segmentation algorithm to classify and group similar textures to infer the distribution of snow using snow surface freeboard measurements from Operation IceBridge campaigns over the Weddell Sea. We find that texturally-similar regions have similar snow/ice ratios, even when they have different absolute snow depth measurements. This allows for the extrapolation of nadir-looking snow radar data using two-dimensional surface altimetry scans, providing a two-dimensional estimate of the snow depth with ∼22% error. We show that at the floe scale (∼180 m), snow depth can be directly estimated from the snow surface with ∼20% error using deep learning techniques, and that the learned filters are comparable to standard textural analysis techniques. This error drops to ∼14% when averaged over 1.5 km scales. These results suggest that surface morphological information can improve remotely-sensed estimates of snow depth, and hence sea ice thickness, as compared to current methods. Such methods may be useful for reducing uncertainty in Antarctic sea ice thickness estimates from ICESat-2.

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Sensitivity of Radar Altimeter Waveform to Changes in Sea Ice Type at Resolution of Synthetic Aperture Radar

Cited by 14 publications

References 36 publications

Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice

Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice

Arctic sea ice cover data from spaceborne SAR by deep learning

A Textural Approach to Improving Snow Depth Estimates in the Weddell Sea

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