Surface-based Ku-band radar altimetry investigations indicate the radar signal is typically backscattered from well above the snow-sea ice interface. However, this would induce a bias in satellite altimeter sea ice thickness retrievals not reflected by buoy validation. Our study presents a mechanism to potentially explain this paradox: probabilistic quasi-specular radar scattering from the snow-ice interface. We introduce the theory for this mechanism before identifying it in airborne Ku-band radar observations collected over landfast first year Arctic sea ice near Eureka, Canada, in spring 2016. Based on SAR data, this study area likely represents level first year sea ice across the Arctic. Radar backscatter from the snow and ice interfaces were estimated by co-aligning laser scanner and radar observations within situmeasurements. On average, 4-5 times more radar power was scattered from the snow-ice than the air-snow interface over first-year ice. However, return power varied by up to 20 dB between consecutive radar echoes, particularly from the snow-ice interface, depending on local slope and roughness. Measured laser-radar snow depths were more accurate when radar returns were specular, but there was no systematic bias between airborne and in situ snow depths. The probability and strength of quasi-specular returns depend on the measuring height above and slope distribution of sea ice, so these findings have implications for satellite altimetry snow depth and freeboard estimates. This mechanism could explain the apparent differences in Ku-band radar penetration into snow on sea ice when observed from the range of a surface-, airborne- or satellite-based sensor.

We present new high-resolution snow depth data on Arctic sea ice derived from airborne microwave radar measurements from the IceBird campaigns of the Alfred Wegener Institute (AWI) together with a new retrieval method using signal peakiness based on an intercomparison exercise of colocated data at different altitudes. We aim to demonstrate the capabilities and potential improvements of radar data, which were acquired at a lower altitude (200 ft) and slower speed (110 kn) and had a smaller radar footprint size (2-m diameter) than previous airborne snow radar data. So far, AWI Snow Radar data have been derived using a 2–18-GHz ultrawideband frequency-modulated continuous-wave (FMCW) radar in 2017–2019. Our results show that our method in combination with thorough calibration through coherent noise removal and system response deconvolution significantly improves the quality of the radar-derived snow depth data. The validation against a 2-D grid ofin situsnow depth measurements on level landfast first-year ice indicates a mean bias of only 0.86 cm between radar and ground truth. Comparison between the radar-derived snow depth estimates from different altitudes shows good consistency. We conclude that the AWI Snow Radar aboard the IceBird campaigns is able to measure the snow depth on Arctic sea ice accurately at higher spatial resolution than but consistent with the existing airborne snow radar data of NASA Operation IceBridge. Together with the simultaneous measurements of the total ice thickness and surface freeboard, the IceBird campaign data will be able to describe the whole sea-ice column on regional scales.

Within the framework of European Space Agency (ESA) activities, several campaigns were carried out in the last decade with the purpose of exploiting the capabilities of multifrequency synthetic aperture radar (SAR) data to retrieve snow information. This article presents the results obtained from the ESA SnowSAR airborne campaigns, carried out between 2011 and 2013 on boreal forest, tundra and alpine environments, selected as representative of different snow regimes. The aim of this study was to assess the capability of X- and Ku-bands SAR in retrieving the snow parameters, namely snow depth (SD) and snow water equivalent (SWE). The retrieval was based on machine learning (ML) techniques and, in particular, of artificial neural networks (ANNs). ANNs have been selected among other ML approaches since they are capable to offer a good compromise between retrieval accuracy and computational cost. Two approaches were evaluated, the first based on the experimental data (data driven) and the second based on data simulated by the dense medium radiative transfer (DMRT). The data driven algorithm was trained on half of the SnowSAR dataset and validated on the remaining half. The validation resulted in a correlation coefficient$R \simeq 0.77$between estimated and target SD, a root-mean-square error (RMSE)$\simeq 13$cm, and bias = 0.03 cm. ANN algorithms specific for each test site were also implemented, obtaining more accurate results, and the robustness of the data driven approach was evaluated over time and space. The algorithm trained with DMRT simulations and tested on the experimental dataset was able to estimate the target parameter (SWE in this case) with$R =0.74$, RMSE = 34.8 mm, and bias = 1.8 mm. The model driven approach had the twofold advantage of reducing the amount ofin situdata required for training the algorithm and of extending the algorithm exportability to other test sites.

For northern countries like Canada, seasonal snow cover is a key component of the water cycle and a commodity of high importance to public safety, economic sustainability, and ecosystem function. Despite this importance, snow water equivalent (SWE - the amount of water stored by snow) information from existing surface observing networks and satellite data does not adequately address most user needs. To address this gap, a new synthetic aperture radar (SAR) mission capable of providing information on terrestrial SWE at previously unrealized spatial resolution is currently under development. The Terrestrial Snow Mass Mission (‘TSMM’) will provide moderate resolution (500m) dual frequency (13.5/17.25 GHz) Ku-band radar measurements across all northern hemisphere snow covered areas every 7 days. Data from this mission will be used at Environment and Climate Change Canada to (1) provide a new level of information on the temporal/spatial variability in SWE in support of climate services, and (2) feed into environmental prediction and analysis systems to improve weather and hydrological forecasts.

In this paper we present processing and analysis results of an airborne Ku-band InSAR, constructed at the University of Massachusetts, and flown on a Cessna 208 Caravan over the Trail Valley Creek region in Canada's Northwest Territories during the 2018–19 snow season. In this paper, we describe the Ku-band InSAR, provide some intermediate results and discuss on how these data can be used for furthering the science in the remote sensing of snow.

Current satellite observing systems lack the capability to derive terrestrial snow water equivalent (SWE, the amount of liquid water stored in solid form by snow) at the spatial resolution, synoptic sensitivity, global coverage, and accuracy required for operational environmental monitoring, services, and prediction. The required combination of revisit time, spatial coverage, measurement resolution, and sensitivity to the mass of snow on the ground necessitates a new spaceborne observing concept. To address this observing gap, Environment and Climate Change Canada (ECCC), the Canadian Space Agency, industrial partners at Airbus, and international scientific collaborators are developing a new dual frequency (Ku-band: 13.5 and 17.2 GHz), moderate resolution (250 m), wide swath (~500 km) radar mission concept. This paper provides an overview of the measurement concept, and ongoing science activities in support of the technical mission development.

A winter time series of ground-based (X- and Ku-bands) scatterometer and spaceborne synthetic aperture radar (SAR) (C-band) fully polarimetric observations coincident with in situ snow and ice measurements are used to identify the dominant scattering mechanism in bubbled freshwater lake ice in the Hudson Bay Lowlands near Churchill, Manitoba. Scatterometer observations identify two physical sources of backscatter from the ice cover: the snow-ice and ice-water interfaces. 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. The co-polarized phase difference of interactions at the ice-water interface from both scatterometer and SAR observations is centered at 0° during the time series, also indicating a scattering regime other than double bounce. A Yamaguchi three-component decomposition of the RADARSAT-2 C-band time series is presented, which suggests the dominant scattering mechanism to be single-bounce off the ice-water interface with appreciable surface roughness or preferentially oriented facets, regardless of the presence, absence, or density of tubular bubble inclusions. This paper builds on newly established evidence of single-bounce scattering mechanism for freshwater lake ice and is the first to present a winter time series of ground-based and spaceborne fully polarimetric active microwave observations with polarimetric decompositions for bubbled freshwater lake ice.

In this paper, we develop a radar snow water equivalent (SWE) retrieval algorithm based on a parameterized forward model of bicontinuous dense media radiative transfer (Bic-DMRT). The algorithm is based on retrieving the absorption loss of the snowpack which is directly proportional to the SWE. In the algorithm, Bic-DMRT is first applied to generate a lookup table (LUT) of snowpack backscattering at X- and Ku-band. Regression training is applied to the LUT to transform the dual-frequency backscatter into functions of two parameters: the scattering albedo at X-band and SWE. The background scattering is subtracted from the SnowSAR data to give the volume scattering of snow. Classification of SnowSAR data is applied to provide a priori information. Based on the obtained volume scattering and the priori information, a cost function is established to find SWE. Performance of the retrieval algorithm was tested using three sets of airborne SnowSAR data acquired over mixed areas in Finland and open tundra landscape in Canada. It is shown that the retrieval algorithm has a root-mean-square error below 30 mm of SWE and a correlation coefficient above 0.64.

Recent advancements to the understanding of snow-microwave interaction have helped to identify the considerable potential for radar-based retrieval of terrestrial snow mass. If applied to space-borne platforms, such retrievals could provide much needed improvements to the spatial and temporal availability of snow mass estimates. To further understanding of these interactions in tundra environments, this study evaluates an extensive set of coincident in situ snow measurements and airborne dual-frequency (17.2 and 9.6 GHz) radar observations near Inuvik, Northwest Territories, Canada. Given known uncertainties related to the role of microstructure in radar-based retrievals, an enhanced snow pit protocol was introduced to objectively characterize specific surface area (SSA) with a shortwave infrared integrating sphere (IRIS) system. Snow pit and bulk snow measurements including SSA are used to parameterize the Microwave Emission Model of Layered Snowpacks Adapted to Include Backscattering (MEMLS3&a) and evaluate observed spatial diversity in the airborne radar signal.

We validate an absorption based radar retrieval algorithm of snow water equivalent (SWE) using X- and Ku-band backscatter with airborne SAR data. The bicontinuous dense media radiative transfer (Bic-DMRT) model is first applied to generate a look-up table of snow properties against backscattering at X- and Ku-bands. In the retrieval algorithm, the background scattering is subtracted from the total scattering giving the volume scattering of snow. With the look-up table, we generate regression equations between multiple and single scattering and correlations between the scattering albedo and optical thickness at the two bands. With these relationships and the volume scattering of the snowpack, the best solution for the radar observation is found using a priori constrained least-squares cost function. Next, the absorption loss of the snowpack is derived from the solution, which is directly proportional to the SWE. We have applied the algorithm to airborne SAR observations from Finland and Canada. The retrieval algorithm is shown to be effective, achieving root mean square error (RMSE) of ~19 mm for both SnowSAR data, which is smaller than the 20mm RMSE requirement of SCLP.

Cloud shadows dramatically affect the appearance of outdoor scenes. We describe two approaches that use video of cloud shadows to estimate a cloudmap, a spatio-temporal function that represents the clouds passing over the scene. Our first method makes strong assumptions about the camera geometry and estimates the cloud motion direction. Our second method uses techniques from manifold learning and does not require known geometry. Neither method requires directly viewing the clouds, but instead uses the pattern of intensity changes caused by the cloud shadows. We show renderings of cloudmaps extracted using both methods from videos of real outdoor scenes as well as quantitative results on synthetic datasets. An accurate estimate of the cloudmap has potential applications in surveillance and graphics, as well as scientific studies that depend on solar radiation.

The University of Waterloo scatterometer, which is a system developed for observation of snow and ice properties, is described. The system is composed of two frequency-modulated continuous-wave radars operating at center frequencies of 17.2 and 9.6 GHz. A field-deployable platform allows a rapid setup and observation at remote sites under harsh environmental conditions. A two-axis positioning system moves the radar beam across a user-programmable range of azimuth (±180°) and elevation angles (15°-105°). Typical azimuth scans of 60° angular width generate between 21 and 586 independent samples, depending on the wavelength and the elevation angle. The backscatter response of terrestrial snow in the Canadian Subarctic is demonstrated with two experiments conducted in Churchill, MB, Canada, between 2009 and 2011.

Retrieval of snow volume properties at high spatial resolutions has become a priority in hydrological and climatological research. Recent studies have identified the 8 to 18 GHz range as particularly sensitive to snow water equivalent. In this study we present snow target observations using a dual frequency (17.2 GHz and 9.6 GHz) scatterometer system. The observations were collected in a unique tundra environment providing a novel dataset for comparison with in situ snow survey data. Moreover, the scatterometer was sled mounted allowing observations to be collected over a substantial spatial domain.