Smooth First-year Sea Ice Research Articles

Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters

Early-summer melt pond fraction is predicted using late-winter C-band backscatter of snow-covered first-year sea ice. Aerial photographs were acquired during an early-summer 2012 field campaign in Resolute Passage, Nunavut, Canada, on smooth first-year sea ice to estimate the melt pond fraction. RADARSAT-2 Synthetic Aperture Radar (SAR) data were acquired over the study area in late winter prior to melt onset. Correlations between the melt pond fractions and late-winter linear and polarimetric SAR parameters and texture measures derived from the SAR parameters are utilized to develop multivariate regression models that predict melt pond fractions. The results demonstrate substantial capability of the regression models to predict melt pond fractions for all SAR incidence angle ranges. The combination of the most significant linear, polarimetric and texture parameters provide the best model at far-range incidence angles, with an R 2 of 0.62 and a pond fraction RMSE of 0.09. Near- and mid- range incidence angle models provide R 2 values of 0.57 and 0.61, respectively, with an RMSE of 0.11. The strength of the regression models improves when SAR parameters are combined with texture parameters. These predictions also serve as a proxy to estimate snow thickness distributions during late winter as higher pond fractions evolve from thinner snow cover.

Ku-, X- and C-Band Microwave Backscatter Indices from Saline Snow Covers on Arctic First-Year Sea Ice

In this study, we inter-compared observed Ku-, X- and C-band microwave backscatter from saline 14 cm, 8 cm, and 4 cm snow covers on smooth first-year sea ice. A Ku-, X- and C-band surface-borne polarimetric microwave scatterometer system was used to measure fully-polarimetric backscatter from the three snow covers, near-coincident with corresponding in situ snow thermophysical measurements. The study investigated differences in co-polarized backscatter observations from the scatterometer system for all three frequencies, modeled penetration depths, utilized co-pol ratios, and introduced dual-frequency ratios to discriminate dominant polarization-dependent frequencies from these snow covers. Results demonstrate that the measured co-polarized backscatter magnitude increased with decreasing snow thickness for all three frequencies, owing to stronger gradients in snow salinity within thinner snow covers. The innovative dual-frequency ratios suggest greater sensitivity of Ku-band microwaves to snow grain size as snow thickness increases and X-band microwaves to snow salinity changes as snow thickness decreases. C-band demonstrated minimal sensitivity to changes in snow salinities. Our results demonstrate the influence of salinity associated dielectric loss, throughout all layers of the three snow covers, as the governing factor affecting microwave backscatter and penetration from all three frequencies. Our “plot-scale” observations using co-polarized backscatter, co-pol ratios and dual-frequency ratios suggest the future potential to up-scale our multi-frequency approach to a “satellite-scale” approach, towards effective development of snow geophysical and thermodynamic retrieval algorithms on smooth first-year sea ice.

Multifrequency Microwave Backscatter From a Highly Saline Snow Cover on Smooth First-Year Sea Ice: First-Order Theoretical Modeling

A theoretical understanding of a multifrequency microwave approach to understand complex microwave interactions from a highly saline snow cover on a relatively smooth first-year sea ice is presented. We examine the sensitivity of Ku-, X-, and C-band ${\sigma }_{{\text {VV}}}^{0}$ and ${\sigma }_{{\text {HH}}}^{0}$ to variability in snow geophysical properties such as salinity, density, temperature, and snow grain radius, sampled from a highly saline snow cover on first-year sea ice. A first-order multilayer snow and ice backscatter model is used to calculate ${\sigma }_{{\text {VV}}}^{0}$ and ${\sigma }_{{\text {HH}}}^{0}$ by taking into account the surface and volume scattering contributions within each snow layer of the snow pack. Penetration depth models are used to calculate the potential penetration of all three frequencies, at initial and perturbed snow property conditions. Sensitivity analyses suggest that variability in salinity and snow grain radius have the greatest effect, followed by density and temperature. This phenomenon is observed for all three frequencies, influencing microwave penetration and backscatter. Dielectric loss associated with highly saline snow covers and substantial changes in scattering contributions from snow grain radius perturbations were found to be the dominant factors affecting microwave penetration and backscatter. Results from this paper demonstrate the potential of using a multifrequency theoretical approach to correlate with active microwave observations to determine the geophysical and electrical state of snow/sea ice system. The paper also represents an evolution in a theoretical understanding on how an active microwave approach using multiple frequencies can be further utilized toward the development of snow thickness and/or snow water equivalent algorithm on smooth FYI.

Ku-, X- and C-band measured and modeled microwave backscatter from a highly saline snow cover on first-year sea ice

Abstract In this study, we inter-compare observed and modeled Ku-, X- and C-band microwave backscatter for two snow temperature conditions for a highly saline snow cover on smooth first-year sea ice. A new surface-based multi-frequency (Ku-, X- and C-bands) microwave scatterometer system is used quasi-coincident with in situ geophysical snow measurements. A multilayer snow and ice backscatter model is used to calculate the total co-polarized backscatter coefficient for two snow temperature conditions. The model provides the surface and volume scattering contributions for each snow layer, as well as the frequency-dependent penetration depth. These results aid interpretation of observed backscatter. Joint use of Ku-, X- and C-band microwaves provide an enhanced understanding of diverse variations in geophysical, thermodynamic and electrical state of snow/sea ice system. Our results indicate that the effect of dielectric loss associated with highly saline snow covers is the dominant factor affecting microwave penetration and backscatter from all three frequencies. The observed and modeled C-band backscatter shows good agreement, followed by X- and Ku-bands, at both snow temperature conditions. Microwave backscatter shows greater sensitivity to variations in plot-scale surface roughness, for all three frequencies. Additionally, Ku-band wavelength exhibits greater sensitivity to snow grain radius, over X-, and C-bands. Our results demonstrate the future potential of a multi-frequency approach towards the development of snow thickness and snow water equivalent algorithms on first-year sea ice.

Comparison of a coupled snow thermodynamic and radiative transfer model with in situ active microwave signatures of snow-covered smooth first-year sea ice

Abstract. Within the context of developing data inversion and assimilation techniques for C-band backscatter over sea ice, snow physical models may be used to drive backscatter models for comparison and optimization with satellite observations. Such modeling has the potential to enhance understanding of snow on sea-ice properties required for unambiguous interpretation of active microwave imagery. An end-to-end modeling suite is introduced, incorporating regional reanalysis data (NARR), a snow model (SNTHERM89.rev4), and a multilayer snow and ice active microwave backscatter model (MSIB). This modeling suite is assessed against measured snow on sea-ice geophysical properties and against measured active microwave backscatter. NARR data were input to the SNTHERM snow thermodynamic model in order to drive the MSIB model for comparison to detailed geophysical measurements and surface-based observations of C-band backscatter of snow on first-year sea ice. The NARR variables were correlated to available in situ measurements with the exception of long-wave incoming radiation and relative humidity, which impacted SNTHERM simulations of snow temperature. SNTHERM snow grain size and density were comparable to observations. The first assessment of the forward assimilation technique developed in this work required the application of in situ salinity profiles to one SNTHERM snow profile, which resulted in simulated backscatter close to that driven by in situ snow properties. In other test cases, the simulated backscatter remained 4–6 dB below observed for higher incidence angles and when compared to an average simulated backscatter of in situ end-member snow covers. Development of C-band inversion and assimilation schemes employing SNTHERM89.rev4 should consider sensitivity of the model to bias in incoming long-wave radiation, the effects of brine, and the inability of SNTHERM89.Rev4 to simulate water accumulation and refreezing at the bottom and mid-layers of the snowpack. These impact thermodynamic response, brine wicking and volume processes, snow dielectrics, and thus microwave backscatter from snow on first-year sea ice.

Sensitivity of C-band synthetic aperture radar polarimetric parameters to snow thickness over landfast smooth first-year sea ice

C-band linear and polarimetric parameters derived from synthesis and decomposition of Radarsat-2 SAR data are analyzed and evaluated with respect to snow covers of variable thickness on smooth first year sea ice (SFYI). The study is conducted for two selected temperature conditions: 1) cold (−7.9°C) and 2) warm (−0.4°C). First, the polarimetric SAR response from the snow cover at cold conditions is analyzed with an objective to investigate the sensitivity of linear and polarimetric backscatter to snow thickness. Second, the changes in these sensitivities are examined as a function of change in surface air temperature (from −7.9°C to −0.4°C) through the changes manifested in snow geophysical properties. The dependence of linear and polarimetric backscatter response on radar incidence angle for different snow thicknesses is also evaluated.Our results demonstrate strong to weak relationships between the polarimetric parameters and snow thickness under cold conditions but virtually no relationship under warm conditions. The strength of this sensitivity varies with parameter type, radar incidence angle and snow geophysical properties. Under the cold conditions, fourteen polarimetric parameters demonstrate a positive relationship and nine demonstrate a negative relationship with snow thickness. This parameter sensitivity to snow thickness indicates an increase in volume or mixed scattering component with increase in snow thickness. The cause of this is primarily an increased microwave interaction with a greater number of dry snow grains, and/or more interaction with increased brine volumes in the brine-wetted snow and upper sea ice layer. A comparison of polarimetric signatures at warm conditions to that at cold conditions shows both an increase and a decrease in response for all snow thicknesses. For thin or medium snow cover, every time a polarimetric parameter response increases or decreases as a consequence of an increase in temperature there is always an opposite response in the same polarimetric parameter for thick snow cover. This is primarily due to the masking effect of moist-snow, likely caused by reduced penetration depth resulting in dominant surface scattering from the air/moist snow interface, and/or the dry snow–brine-wetted snow interface, as opposed to the snow–ice interface. Normal probability density functions (PDFs) employed to determine the snow thickness separation capability of polarimetric parameters show near overlapping signatures of all snow classes under warm conditions, suggesting that snow thickness estimation using snow thermodynamics at warm conditions is not possible.

Sea ice type and open water discrimination using dual co-polarized C-band SAR

We investigate the utility of dual co-polarized C-band synthetic aperture radar (SAR) imagery for discriminating sea ice types and open water during winter. We base our analysis on ENVISAT ASAR alternating vertical and horizontal polarization (VV, HH) medium-resolution imagery of thin sea ice, first-year sea ice, multiyear sea ice, and open water in the Canadian Arctic. We introduce a methodology for generating colour composite imagery (based on VV, HH, and co-polarization ratio channels) that substantially enhances visual discrimination. We statistically compare sampled vertical and horizontal backscatter from sea ice types and open water to assess backscatter magnitudes and polarization differences. The latter are presented as co-polarized ratio (VV/HH) values in decibels (dB). We conclude by presenting a decision-tree classifier using estimated statistical thresholds. Open water is unambiguously discriminated (>99% accuracy) from all sea ice types, except thin sea ice, using a co-polarized ratio threshold of 2 dB. Thin sea ice exhibits high variability in backscatter magnitude and polarization difference, making its discrimination from multiyear sea ice, rough first-year sea ice, and open water ambiguous. Thin sea ice is effectively discriminated (93% accuracy) from smooth first-year sea ice using a co-polarized ratio threshold of 1.3 dB. Smooth snow-covered first-year sea ice exhibits polarization differences that are attributed to volume scattering mechanisms within the brine-wetted snow cover.

Microwave Signatures of Snow on Sea Ice: Modeling

Accurate knowledge of snow-depth distribution over sea ice is critical for polar climate studies. Current snow-depth-over-sea-ice retrieval algorithms do not sufficiently account for variations in snow and ice physical properties that can affect the accuracy of retrievals. For this reason, airborne microwave observations were coordinated with ground-based measurements of snow depth and snow properties in the vicinity of Barrow, AK, in March 2003. In this paper, the effects of snowpack properties and ice conditions on microwave signatures are examined using detailed surface-based measurements and airborne observations in conjunction with a thermal microwave-emission model. A comparison of the Microwave Emission Model of Layered Snowpacks (MEMLS) simulations with detailed snowpack and ice data from stakes along the Elson Lagoon and the Beaufort Sea and radiometer data taken from low-level flights using a Polarimetric Scanning Radiometer (PSR-A) shows that MEMLS can be used to simulate snow on sea ice and is a useful tool for understanding the limitations of the snow-depth algorithm. Analysis of radiance data taken over the Elson Lagoon and the Beaufort Sea using MEMLS suggests that the radiometric differences between the two locations are due to the differences in sea-ice emissivity. Furthermore, measured brightness temperatures suggest that the current snow-depth retrieval algorithm is sufficient for areas of smooth first-year sea ice, whereas new algorithm coefficients are needed for rough first-year sea ice. Snowpack grain size and density remain an unresolved issue for snow-depth retrievals using passive-microwave radiances

The influence of cloud cover on the radiation budget, physical properties, and microwave scattering coefficient (σ°) of first-year and multiyear sea ice

Over sea ice, the shift from predominantly clear conditions in winter to persistent stratus cloud cover in late spring and early summer coincides with a dramatic increase in the net radiation balance at the sea ice surface. Transformation of the boundary layer climate and the geophysical properties of the snow covered sea ice result from this cloud-induced rise in surface energy balance. In this paper, in situ data from the Seasonal Sea Ice Monitoring the Modeling Site (SIMMS'93) experiment are used to examine the extent to which changes in surface radiation, sea ice physical and electrical properties, and microwave scattering are a function of clear versus cloudy conditions over first-year and multiyear sea ice types. The authors use the surface observations to drive a simple forward scattering model used to diagnose the potential mechanisms responsible for the observed changes in microwave scattering by the Earth Resources Satellite-1 (ERS-1). Their results indicate that under cloudy conditions the surface temperature increases because of the surplus in the longwave net radiative energy. This causes an increase in the surface temperature and formation of a temperature wave down through the snow volume into the ice surface. This increases the dielectric constant of the snow basal layer and ice surface over first-year sea ice because of the thin snow cover (relative to the thicker multiyear ice snow cover) and because of the temperature effect on brine volume. The observed increase in scattering is limited to very smooth first-year sea ice types. Models used to diagnose the principal scattering mechanisms suggest that the cloud cover effects the scattering either by creating a dielectrically rough basal layer in the snow volume near the ice surface or by increasing the scattering contribution from snow grains within the basal layer volume. Under either scenario the increase in scattering is small (/spl sim/5 dB) and is only detectable if the target presents a scattering magnitude less than about -20 dB.