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

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 dualfrequency CRISTAL radar altimeter missions

Microwave scattering models and basic experiments

Progress is summarized which has been made in four areas of study: (1) scattering model development for sparsely populated media, such as a forested area; (2) scattering model development for dense media, such as a sea ice medium or a snow covered terrain; (3) model development for randomly rough surfaces; and (4) design and conduct of basic scattering and attenuation experiments suitable for the verification of theoretical models

Impact of Microstructure on Solar Radiation Transfer Within Sea Ice During Summer in the Arctic : A Model Sensitivity Study

The recent rapid changes in Arctic sea ice have occurred not only in ice thickness and extent, but also in the microstructure of ice. To understand the role of microstructure on partitioning of incident solar shortwave radiation within the ice and upper ocean, this study investigated the sensitivity of the optical properties of summer sea ice on ice microstructures such as the volume fraction, size, and vertical distribution of gas bubbles, brine pockets, and particulate matter (PM). The results show that gas bubbles are the predominant scatterers within sea ice. Their effects on the scattering coefficient and ice albedo are 5 and 20 times stronger respectively than the effect of brine pockets. Albedo and transmittance of ice decrease with higher concentration and larger size of PM particles. A 4-cm top layer of ice with high PM concentration (50 g/m(3)) results in a 10% increase in radiation absorption. The role of ice microstructure in the partitioning of radiation transfer is more important for seasonal than for multiyear ice, and more important for ponded than for snow-covered ice. Varying ice microstructure can obviously alter solar radiation transfer in the ice-ocean system, even with a constant ice thickness. Our results suggest that numerical models should take the variable microstructure of sea ice into account to improve model accuracy and to understand the interaction between internal variations in Arctic sea ice and the ocean, especially in summer.Peer reviewe

Impact of a Surface Ice Lid on the Optical Properties of Melt Ponds

To investigate the influence of a surface ice lid on the optical properties of a melt pond, a radiative transfer model was employed that includes four plane-parallel layers: an ice lid, a melt pond, the underlying ice, and the ocean beneath the ice. The thickness H-s and the scattering coefficient sigma(s) of the ice lid are altered. Variations in the spectral albedo and transmittance T due to H-s for a transparent ice lid are limited, and scattering in the ice lid has a pronounced impact on the albedo of melt ponds as well as the vertical distribution of spectral irradiance in ponded sea ice. The thickness of the ice lid determines the amount of solar energy absorbed. A 2-cm-thick ice lid can absorb 13% of the incident solar energy, half of the energy absorbed by a 30-cm-deep meltwater layer below the lid. This has an influence on the thermodynamics of melting sea ice. The color and spectral albedo of refreezing melt ponds depend on the value of the dimensionless number sigma(s) H- s. Good agreement between field measurements and our model simulations is found. The number sigma(s) H- s is confirmed to be a good index showing that the influence of an ice lid with sigma(s) H- s Plain Language Summary Melt ponds are pools of open water that form on sea ice in the warm months of the Arctic Ocean, and they will frequently be refrozen due to loss of heat and then covered by an ice lid or snow even in summer. This lid is very important to the optical properties of melt ponds. If the ice lid is very thin, the change in the reflective characteristics of the melt pond is minimal; that is, the influence of the ice lid is negligible. If snow accumulates on the ice lid, the reflective characteristics of the melt pond change completely. How about the situation between the above two extreme cases? In this study, we find that a dimensionless number is a good index to quantify the impact of the ice lid. Visual inspections on the color of refreezing melt ponds also help to judge the significance of the influence of the ice lid. This will allow for an accurate estimation on the role of surface ice lid during field investigations on the optical properties of melt ponds.Peer reviewe

Remote sensing of earth terrain

A systematic approach for the identification of terrain media such as vegetation canopy, forest, and snow covered fields is developed using the optimum polarimetric classifier. The covariance matrices for the various terrain cover are computed from theoretical models of random medium by evaluating the full polarimetric scattering matrix elements. The optimal classification scheme makes use of a quadratic distance measure and is applied to classify a vegetation canopy consisting of both trees and grass. Experimentally measured data are used to validate the classification scheme. Theoretical probability of classification error using the full polarimetric matrix are compared with classification based on single features including the phase difference between the VV and HH polarization returns. It is shown that the full polarimetric results are optimal and provide better classification performance than single feature measurements

Remote sensing of earth terrain

A mathematically rigorous and fully polarimetric radar clutter model used to evaluate the radar backscatter from various types of terrain clutter such as forested areas, vegetation canopies, snow covered terrains, or ice fields is presented. With this model, the radar backscattering coefficients for the multichannel polarimetric radar returns can be calculated, in addition to the complex cross correlation coefficients between elements of the polarimetric measurement vector. The complete polarization covariance matrix can be computed and the scattering properties of the clutter environment characterized over a broad range of incident angle and frequencies

Investigation of radar backscattering from second-year sea ice

The scattering properties of second-year ice were studied in an experiment at Mould Bay in April 1983. Radar backscattering measurements were made at frequencies of 5.2, 9.6, 13.6, and 16.6 GHz for vertical polarization, horizontal polarization and cross polarizations, with incidence angles ranging from 15 to 70 deg. The results indicate that the second-year ice scattering characteristics were different from first-year ice and also different from multiyear ice. The fading properties of radar signals were studied and compared with experimental data. The influence of snow cover on sea ice can be evaluated by accounting for the increase in the number of independent samples from snow volume with respect to that for bare ice surface. A technique for calculating the snow depth was established by this principle and a reasonable agreement has been observed. It appears that this is a usable way to measure depth in snow or other snow-like media using radar

Remote sensing of earth terrain

A systematic approach for the identification of terrain media such as vegetation canopy, forest, and snow covered fields is developed using the optimum polarimetric classifier. The covariance matrices for the various terrain covers are computed from the theoretical models of random medium by evaluating the full polarimetric scattering matrix elements. The optimal classification scheme makes use of a quadratic distance measure and is applied to classify a vegetation canopy consisting of both trees and grass. Experimentally measured data are used to validate the classification scheme. Theoretical probability of classification error using the full polarimetric matrix are compared with classification based on single features including the phase difference between the VV and HH polarization returns. It is shown that the full polarimetric results are optimal and provide better classification performance than single feature measurements. A systematic approach is presented for obtaining the optimal polarimetric matched filter which produces maximum contrast between two scattering classes, each represented by its respective covariance matrix