Randall K. Scharien

All-Season Compact-Polarimetry C-band SAR Observations of Sea Ice

Abstract. Compact-polarimetry (CP) synthetic aperture radar (SAR) observations are presented for major sea ice types in each ice season. CP data for three wide-swath Radarsat Constellation Mission (RCM) modes were simulated and evaluated. Regression models and statistical distances as functions of incidence angles were calculated for 26 CP parameters, based on 969 samples of user-selected homogeneous regions of sea ice. CP parameters, best able to discriminate sea ice types and open water, were quantitatively identified in three incidence angle ranges (19–29°, 30–39°, 40–49°). These parameters will likely provide discrimination of sea ice types and open water for both visual interpretation and automated classification. Several parameter–ice type combinations exhibit novel scattering responses, which present new opportunities for ice type discrimination and for inferring scattering mechanisms. Specifically, phase-related parameters with early-stage ice types provide discrimination for ice type pairings that are difficult with co- or dual-polarized data. CP parameters change with incidence angle, which necessitates the use of certain CP parameters at smaller incidence angles and others at larger incidence angles in wide-swath RCM modes. The Canadian Ice Service will implement CP SAR data in their operational workflows once the RCM is operational. Prelaunch study results provide a valuable resource for early adoption of CP data. Résumé. Des observations SAR en polarimétrie compacte (CP) sont présentées pour les principaux types de glace de mer pour la saison des glaces. Des données en CP pour trois modes à large fauchée de la mission de la Constellation RADARSAT (MCR) ont été simulées et évaluées. Des modèles de régression et des distances statistiques en fonction de l’angle d’incidence ont été calculés pour 26 paramètres en CP d’après 969 échantillons de régions homogènes de glace de mer sélectionnées par l’utilisateur. Les paramètres en CP les plus aptes à distinguer les types de glace de mer et l’eau libre ont été quantitativement identifiés dans trois gammes d’angles d’incidence (19–29 °, 30–39 °, 40–49 °). Ces paramètres permettront probablement la discrimination des types de glace de mer et de l’eau libre, à la fois pour l’interprétation visuelle et la classification automatisée. Plusieurs combinaisons de paramètres et de types de glace montrent de nouvelles réponses de diffusion, présentant ainsi de nouvelles possibilités pour la discrimination du type de glace et pour déduire les mécanismes de diffusion. Plus précisément, les paramètres liés à la phase pour les types de glace à un stade précoce permettent la discrimination du type de glace, ce qui est difficile avec des données copolarisées ou en polarisation double. Les paramètres en CP changent avec l’angle d’incidence, ce qui nécessite l’utilisation de certains paramètres à des angles d’incidence plus petits et d’autres paramètres à des angles d’incidence plus grands dans les modes à large fauchée de la MCR. Le Service canadien des glaces utilisera les données SAR en CP dans leur flux de travaux opérationnels lorsque la MCR sera opérationnelle. Les résultats de l’étude de prélancement fournissent une ressource précieuse pour l’adoption précoce des données en CP.

Inversion-Based Sensitivity Analysis of Snow-Covered Sea Ice Electromagnetic Profiles

For the microwave remote sensing of snow-covered sea ice dielectric profiles, the sensitivity of the normalized radar cross-section data with respect to the complex permittivity and thickness values is investigated. Our results show that the data collected closer to the nadir in monostatic setups, and the data collected closer to the specular angle in bistatic setups represent higher sensitivity values. Using both synthetically and experimentally collected data sets, we demonstrate that the inversion of data sets having higher sensitivity values results in enhanced reconstruction accuracy.

Retrieval of Young Snow-Covered Sea-Ice Temperature and Salinity Evolution Through Radar Cross-Section Inversion

This paper utilizes an electromagnetic inverse-scattering algorithm to quantitatively reconstruct the vertical temperature and salinity profiles of snow-covered sea ice from time-series monostatic polarimetric normalized radar cross-section (NRCS) data. The reconstructed profile at a given time step is utilized to provide a priori information for the profile reconstruction at the subsequent time step. This successive use of a priori information in the inversion algorithm results in achieving high reconstruction accuracy over the time period of interest. This inversion scheme is evaluated against the experimental data collected from snow-covered sea ice grown in an Arctic ocean mesocosm facility. It will be shown that the time evolution of the temperature, salinity, and density profiles of an artificially grown snow-covered sea ice can be quantitatively reconstructed using single-frequency time-series radar cross-section data assuming that these profiles are initially known with sufficient accuracy.

Effect of Snow Salinity on CryoSat‐2 Arctic First‐Year Sea Ice Freeboard Measurements

The European Space Agencys CryoSat-2 satellite mission provides radar altimeter data that are used to derive estimates of sea ice thickness and volume. These data are crucial to understanding recent variability and changes in Arctic sea ice. Sea ice thickness retrievals at the CryoSat-2 frequency require accurate measurements of sea ice freeboard, assumed to be attainable when the main radar scattering horizon is at the snow/sea ice interface. Using an extensive snow thermophysical property dataset from late winter conditions in the Canadian Arctic, we examine the role of saline snow on first-year sea ice (FYI), with respect to its effect on the location of the main radar scattering horizon, its ability to decrease radar penetration depth, and its impact on FYI thickness estimates. Based on the dielectric properties of saline snow commonly found on FYI, we quantify the vertical shift in the main scattering horizon. This is found to be approximately 0.07 m. We propose a thickness-dependent snow salinity correction factor for FYI freeboard estimates. This significantly reduces CryoSat-2 FYI retrieval error. Relative error reductions of ~ 11% are found for an an ice thickness of 0.95 m and ~ 25% for 0.7 m. Our method also helps to close the uncertainty gap between SMOS and CryoSat-2 thin ice thickness retrievals. Our results indicate that snow salinity should be considered for FYI freeboard estimates.

Winter Sentinel-1 Backscatter as a Predictor of Spring Arctic Sea Ice Melt Pond Fraction: Melt Pond Fraction Prediction From SAR

Spring melt pond fraction (fp) has been shown to influence September sea ice extent and, with a growing need to improve melt pond physics in climate and forecast models, observations at large spatial scales are needed. We present a novel technique for estimating fp on sea ice at high spatial resolution from the Sentinel-1 satellite during the winter period leading up to spring melt. A strong correlation (r = -0.85) is found between winter radar backscatter and fp from first-year and multiyear sea ice data collected in the Canadian Arctic Archipelago (CAA) in 2015. Observations made in the CAA in 2016 are used to validate a fp retrieval algorithm, and a fp prediction for the CAA in 2017 is made. The method is effective using the horizontal transmit and receive polarization channel only and shows promise for providing seasonal, pan-Arctic, fp maps for improved understanding of melt pond distributions and forecast model skill.