Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth observation mission. The snow scientific community has shown that Ku-band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the snow micropenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer (SMRT) model, it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada, was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-band radar backscatter at a 100 m scale. Airborne Ku-band data were acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using RADARSAT-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil=0.010±0.002), and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, were retrieved. Using the geometrical optics surface backscatter model, the retrieved effective soil permittivity increased from C-band (εsoil=2.47) to X-band (εsoil=2.61) and to Ku-band (εsoil=2.77) for the TVC domain. Using the SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve snow water equivalent (SWE) at the 100 m scale.

Abstract. Sea ice thickness is essential for climate studies and numerical weather prediction. Radar altimetry has provided sea ice thickness measurement since the launch of ERS-1 and currently through CryoSat-2, Sentinel-3 and Altika but uncertainty in the scattering horizon used to retrieve sea ice thickness arises from interactions between the emitted signal and snow cover on the ice surface. Therefore, modelling the scattering of the electromagnetic waves with the snowpack and ice is necessary to retrieve the sea ice thickness accurately. The Snow Microwave Radiative Transfer (SMRT) model was used to simulate the low resolution altimeter waveform echo from the snow-covered sea ice, using in-situ measurements as input. Measurement from four field campaigns were used: Cambridge Bay, Eureka Sound and near Alert, Nunavut, Canada in April 2022 in the cold and later winter condition when snow and ice thickness are neat their seasonal maxima prior to melt. In-situ measurements included snow temperature, salinity, density, specific surface area, microstructure from X-ray tomography and surface roughness measurements using structure from motion photogrammetry. Evaluation of SMRT in altimeter mode was performed against CryoSat-2 waveform data in pseudo-low-resolution mode. Simulated and observed waveforms showed good agreement, although it was necessary to adjust sea ice roughness. The retrieved roughness (root-mean-square height) in Cambridge Bay was 2.1 mm and 1.6 mm in Eureka, which was close to the observed value of 1.4 mm for flat sea ice. In addition, simulations of backscatter in preparation for the European Space Agency's CRISTAL mission demonstrated the dominance of scattering from the snow surface at Ku and Ka-band. However, these findings depend on the parameterisation of the roughness. The scattering from the snow surface dominates when roughness is high, but the interface return dominates if the roughness is low ( < 2.5 mm). This is the first study to consider scattering within the snow and demonstrate the origin of CryoSat-2 signals. This work paved the way to a new physical retracker using SMRT to retrieve snow depth and sea ice thickness for radar altimeter missions.

The airborne cryospheric synthetic aperture radar (SAR) system, called CryoSAR, has been developed to advance our scientific expertise in estimating snow accumulation on land, lakes and sea ice, characterizing freshwater ice and sea ice properties, and monitoring the freeze-thaw state of soils. Currently, the dual frequency Ku (13.5 GHz) and L-band (1.3 GHz) instrument is focused on making backscatter and phase measurements of snow and ice on land and lakes to estimate total snow accumulation. This paper describes the core system characteristics, the instrument operation, and SAR data processing that is conducted to achieve science ready data. We illustrate the system’s capability to produce science-ready data using examples from a field experiment conducted in Ontario during the winters of 2022-23 and 2023-24. CryoSAR flights were made over a farm field site and a lake site in Ontario with field measurements. Calibrated Ku and L-band polarimetric data are presented and illustrate the sensitivity of the signals to surface and backscatter processes on the ground.

Accurate simulations of snow emission in surface-sensitive microwave channels are needed to separate snow from atmospheric information essential for numerical weather prediction. Measurements from a field campaign in Trail Valley Creek, Inuvik, Canada, during March 2018 were used to evaluate the Snow Microwave Radiative Transfer (SMRT) model at 89 GHz and, for the first time, frequencies between 118 and 243 GHz. In situ data from 29 snow pits, including snow specific surface area, were used to calculate exponential correlation lengths to represent the snow microstructure and to initialize snowpacks for simulation with SMRT. Measured variability in snowpack properties was used to estimate uncertainty in the simulations. SMRT was coupled with the Atmospheric Radiative Transfer Simulator to account for the directionally dependent emission and attenuation of radiation by the atmosphere. This is a major developmental step needed for top-of-atmosphere simulations of microwave brightness temperature at atmosphere-sensitive frequencies with SMRT. Nadir-simulated brightness temperatures at 89, 118, 157, 183 and 243 GHz were compared with airborne measurements and with ground-based measurements at 89 GHz. Inclusion of anisotropic atmospheric radiance in SMRT had the greatest impact on brightness temperature simulations at 183 GHz and the least impact at 89 GHz. Medians of simulations compared well with medians of observations, with a root mean squared difference of 14 K across five frequencies and two flights (n=10). However, snow pit measurements did not capture the observed variability fully as simulations and airborne observations formed statistically different distributions. Topographical differences in simulated brightness temperature between sloped, valley and plateau areas diminished with increasing frequency as the penetration depth within the snow decreased and less emission from the underlying ground contributed to the airborne observations. Observed brightness temperature differences between flights were attributed to the deposition of a thin layer of very-low-density snow. This illustrates the need to account for both temporal and spatial variabilities in surface snow microstructure at these frequencies. Sensitivity to snow properties and the ability to reflect changes in observed brightness temperature across the frequency range for different landscapes, as demonstrated by SMRT, are necessary conditions for inclusion of atmospheric measurements at surface-sensitive frequencies in numerical weather prediction.

The extent, timing and duration of seasonal freeze/thaw (FT) state exerts dominant control on boreal forest carbon, water and energy cycle processes. Recent and on-going L-Band (approximate to 1.4 GHz) spaceborne missions have the potential to provide enhanced information on FT state over large geographic regions with rapid revisit time. However, the low spatial resolution of these spaceborne observations ( 45 km) makes it difficult to isolate the primary contributions (soil, vegetation, snow) to the FT signal in boreal forest. To better quantify these controls, two L-Band radiometers were deployed (September 2016 to July 2017) at a black spruce (Picea mariana) dominated boreal forest site; one unit above and one unit on the ground surface below the canopy to disentangle the microwave contributions of overstory canopy, and the ground surface on the FT brightness temperature (TB) signal. Bi-weekly multi-angular measurements from both units were combined in order to estimate effective scattering albedo (omega) and the microwave vegetative optical depth (tau), using the tau-omega microwave vegetation radiative transfer model. Soil moisture probes were inserted in the trunk of two black spruce and one larch (Larix laricina) trunks located in the footprint of the above-canopy radiometer to measure tree trunk relative dielectric constant (RDCtree). Results showed a strong relationship between RDCtree and tree skin temperature (Thee) under freezing temperature conditions, which led to a gradual decrease of tau in winter. During the spring thawing period in April and May, tau remained relatively stable. In contrast, it increased substantially in June, most likely in relation to the growing season onset. Overall, tau was related to the seasonal RDCtree cycle (r = 0.76). Regarding omega, a value of 0.086 (+/- 0.029) was obtained, but no dependency on T-tree or RDCtree was observed. Despite the observed impact of FT on vegetation L-Band signals, results from continuous TB observations spanning from 14 September 2016 to 25 May 2017, indicated that the main contribution to the observed L-Band TB freeze-up signal in the fall originated from the ground surface. The above-canopy unit showed some sensitivity to overstory canopy FT, yet the sensitivity was lower compared to the signal induced by the ground FT. In April and May, L-Band radiometer FT retrieval agreed closely to the melt onset detection using ADC, but it was likely related to the coincident presence of liquid water in the snow. Our findings have important applications to L-Band spaceborne FT algorithm development and validation across the boreal forest. More specifically, our findings allow better quantification of the potential effect of frozen ground on various biogeophysical and biogeochemical processes in boreal forests.

Local-scale variations in snow density and layering on Arctic sea ice were characterized using a combination of traditional snow pit and SnowMicroPen (SMP) penetrometer measurements. The SMP profiles provide detailed information about the snow microstructure and stratigraphy and were collected to evaluate coincident airborne and satellite measurements aimed at improving the understanding of inter-annual variability of Arctic snow and sea ice properties. The measurements were acquired during two April field campaigns conducted near the time of maximum snow thickness, coinciding with NASA Operation IceBridge (17 April 2016) and ESA CryoVEx (26 April 2017). In total, 8 survey sites were evaluated within the Canadian Arctic Archipelago near Eureka (80.0°N 85.9°W) and 6 survey sites on the Arctic Ocean (spanning 83.4°N and 86.3°N), on both first (FYI) and multi-year (MYI) sea ice. The SMP measurements were recorded using a SnowMicroPen® 4 penetrometer.

Local-scale variations in snow density and layering on Arctic sea ice were characterized using a combination of traditional snow pit and SnowMicroPen (SMP) measurements. In total, 14 sites were evaluated within the Canadian Arctic Archipelago and Arctic Ocean on both first-year (FYI) and multi-year (MYI) sea ice. Sites contained multiple snow pits with coincident SMP profiles as well as unidirectional SMP transects. An existing SMP density model was recalibrated using manual density cutter measurements (n=186) to identify best-fit parameters for the observed conditions. Cross-validation of the revised SMP model showed errors comparable to the expected baseline for manual density measurements (RMSE = 34 kg m−3 or 10.9 %) and strong retrieval skill (R2=0.78). The density model was then applied to SMP transect measurements to characterize variations at spatial scales of up to 100 m. A supervised classification trained on snow pit stratigraphy allowed separation of the SMP density estimates by layer type. The resulting dataset contains 58 882 layer-classified estimates of snow density on sea ice representing 147 m of vertical variation and equivalent to more than 600 individual snow pits. An average bulk density of 310 kg m−3 was estimated with clear separation between FYI and MYI environments. Lower densities on MYI (277 kg m−3) corresponded with increased depth hoar composition (49.2 %), in strong contrast to composition of the thin FYI snowpack (19.8 %). Spatial auto-correlation analysis showed layered composition on FYI snowpack to persist over long distances while composition on MYI rapidly decorrelated at distances less than 16 m. Application of the SMP profiles to determine propagation bias in radar altimetry showed the potential errors of 0.5 cm when climatology is used over known snow density.

Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

Snow is a crucial element within Arctic ecosystems that is undergoing rapid change in the context of Arctic warming. High spatial resolution synthetic aperture radar (SAR) systems have shown high potential to characterize changes in snow properties, such as depth, density, water equivalent and microstructure. However, distinguishing between the contributions of snow, ground, vegetation and moisture to the SAR signal is challenging, in particular in remote environments with sparse reference data such as the Tundra. Our study focuses on a long-term monitoring site at Trail Valley Creek, Northwest Territories, Canada where ongoing snow science related to the Canadian Space Agency Terrestrial Snow Mass Mission has provided a reference snow dataset to evaluate. We completed a time series of TerraSAR-X (TSX) and TanDEM-X (TDX) VV-VH and HH-HV acquisitions between February 2018 and February 2019 in an 11 day repeat cycle. We used the Stripmap-mode of TDX that acquires continuous scene strips with a swath width of 15 km, variable strip length and a spatial resolution of up to 2.5 m. By spatial analysis of X-Band time series, snow depth from extensive repeated winter field campaigns, vegetation height from aerial LiDAR as well as soil moisture and ground temperature from in-situ observations we explain dynamics of X-Band backscatter for different tundra landscape units during winter.

Several large in-situ soil moisture-monitoring networks currently exist over seasonally frozen regions that may have use for the validation of remote sensing soil freeze/thaw (F/T) products. However, further understanding of how the existing network instrumentation responds to changes in near surface soil F/T is recommended. This case study describes the results of a small plot-scale (7 x 7 m) study from November 2013 through April 2014 instrumented with 36 impedance probes. Soil temperature and real dielectric permittivity (epsilon'(r)) were measured every 15 minutes during F/T transition periods at shallow soil depths (0-10 cm). Categorical soil temperature and real dielectric permittivity techniques were used to define the soil F/T state during these periods. Results demonstrate that both methods for detecting soil F/T have strong agreement (84.7-95.6%) during the fall freeze but weak agreement (53.3-60.9%) during the spring thaw. Bootstrapping results demonstrated both techniques showed a mean difference within +/- 1.0 degrees C and +/- 1.4 epsilon'(r) between the standard 5 cm below surface measurement depth and probes at 2, 10 and integrated 0-5.7 cm depths installed within the same study plot. Overall this study demonstrates that the Hydra Probe offers promise for near surface soil F/T detection using existing soil moisture monitoring networks particularly for the fall freeze.

Detailed angular ground-based L-band brightness temperature (TB) measurements over snow covered frozen soil in a prairie environment were used to parameterize and evaluate an electromagnetic model, the Wave Approach for LOw-frequency MIcrowave emission in Snow (WALOMIS), for seasonal snow. WALOMIS, initially developed for Antarctic applications, was extended with a soil interface model. A Gaussian noise on snow layer thickness was implemented to account for natural variability and thus improve the TB simulations compared to observations. The model performance was compared with two radiative transfer models, the Dense Media Radiative Transfer-Multi Layer incoherent model (DMRT-ML) and a version of the Microwave Emission Model for Layered Snowpacks (MEMLS) adapted specifically for use at L-band in the original one-layer configuration (LS-MEMLS-1L). Angular radiometer measurements (30°, 40°, 50°, and 60°) were acquired at six snow pits. The root-mean-square error (RMSE) between simulated and measured TB at vertical and horizontal polarizations were similar for the three models, with overall RMSE between 7.2 and 10.5 K. However, WALOMIS and DMRT-ML were able to better reproduce the observed TB at higher incidence angles (50° and 60°) and at horizontal polarization. The similar results obtained between WALOMIS and DMRT-ML suggests that the interference phenomena are weak in the case of shallow seasonal snow despite the presence of visible layers with thicknesses smaller than the wavelength, and the radiative transfer model can thus be used to compute L-band brightness temperature.

A field campaign was conducted October 30th to November 13th, 2015 with the intention of capturing diurnal soil freeze/thaw state at multiple scales using ground measurements and remote sensing measurements. On four of the five sampling days, we observed a significant difference between morning (frozen scenario) and afternoon (thawed scenario) ground-based measurements of the soil relative permittivity. These results were supported by an in situ soil moisture and temperature network (installed at the scale of a spaceborne passive microwave pixel) which indicated surface soil temperatures fell below 0 degrees C for the same four sampling dates. Ground-based radiometers appeared to be highly sensitive to F/T conditions of the very surface of the soil and indicated normalized polarization index (NPR) values that were below the defined freezing values during the morning sampling period on all sampling dates. The Scanning L-band Active Passive (SLAP) instrumentation, flown over the study region, showed very good agreement with the ground-based radiometers, with freezing states observed on all four days that the airborne observations covered the fields with ground-based radiometers. The Soil Moisture Active Passive (SMAP) satellite had morning overpasses on three of the sampling days, and indicated frozen conditions on two of those days. It was found that > 60% of the in situ network had to indicate surface temperatures below 0 degrees C before SMAP indicated freezing conditions. This was also true of the SLAP radiometer measurements. The SMAP, SLAP and ground-based radiometer measurements all indicated freezing conditions when soil temperature sensors installed at 5 cm depth were not frozen.

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 degrees 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.

Permanently frozen and seasonally frozen soils occur over a large portion of the Earth's land surface. Changes in the freeze/thaw state of the land surface reflects major changes in thermal and hydraulic properties as well as acting as a “switch” for many ecological processes. In short, soil freeze/thaw state is a fundamental land surface variable in the water and energy cycles, and it connects to the carbon cycle. Surface freeze/thaw state is observable by passive and active microwave sensors. For example, NASA's Soil Moisture Active Passive (SMAP) mission includes a freeze/thaw data product. Such satellite sensing offers routine all-season and all-weather global observations of soil freeze/thaw state with the application of suitable algorithms. We describe early finding from the SLAPex Freeze/Thaw campaign, believed to be the first airborne campaign of its type, focusing on soil freeze/thaw.

Assessment of remote sensing derived freeze/thaw products from L-band radiometry requires ground validation. There is growing interest in utilizing soil moisture networks to meet this validation requirement, although it remains unclear whether the current configuration of these networks is appropriate. To address this issue, a small-scale L-band radiometry study was conducted from November 2014 to April 2015 to capture F/T events. Soil moisture probes measuring soil temperature and real dielectric permittivity were installed within a surface-based L-band radiometer footprint vertically at the surface and horizontally at 2.5, 5 and 10cm depths. A binary freeze/thaw product was derived using radiometer brightness temperatures and compared to the binary F/T classification using soil temperature, real dielectric permittivity and air temperature measurements. The results of the study found that shallow probe depths (vertical and 2.5cm) resulted in an improvement in the radiometer F/T product accuracy over the standard 5cm instrument depth currently utilized at soil moisture networks. Air temperature based approaches for validation result in lower accuracy for F/T events with no snow or wet snow, but performed similarly to soil measurements of temperature and permittivity during dry snow F/T events.

Recent advancement in the understanding of snow-microwave interactions has helped to isolate the considerable potential for radar-based retrieval of snow water equivalent (SWE). There are however, few datasets available to address spatial uncertainties, such as the influence of snow microstructure, at scales relevant to space-borne application. In this study we introduce measurements from SnowSAR, an airborne, dual-frequency (9.6 and 17.2 GHz) synthetic aperture radar (SAR), to evaluate high resolution (10 m) backscatter within a snow-covered tundra basin. Coincident in situ surveys at two sites characterize a generally thin snowpack (50 cm) interspersed with deeper drift features. Structure of the snowpack is found to be predominantly wind slab (65%) with smaller proportions of depth hoar underlain (35%). Objective estimates of snow microstructure (exponential correlation length; l(ex)), show the slab layers to be 2.8 times smaller than the basal depth hoar. In situ measurements are used to parametrize the Microwave Emission Model of Layered Snowpacks (MEMLS3&a) and compare against collocated SnowSAR backscatter. The evaluation shows a scaling factor (phi) between 1.37 and 1.08, when applied to input of l(ex), minimizes MEMLS root mean squared error to < 1.1 dB. Model sensitivity experiments demonstrate contrasting contributions from wind slab and depth hoar components, where wind rounded microstructures are identified as a strong control on observed backscatter. Weak sensitivity of SnowSAR to spatial variations in SWE is explained by the smaller contributing microstructures of the wind slab.

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.