A new sea ice motion tracking algorithm that operates with two sequential synthetic aperture radar (SAR) RADARSAT-2 ScanSAR images is presented. The feature tracking approach is based on the combination of the phase-correlation and cross-correlation methods. An algorithm for selecting control points, a matching technique, an approach for filtering out error vectors, and a confidence levels setting for output drift vectors were specifically developed in order to increase the system's robustness and accuracy. We evaluated ice motion tracking results derived from HH and HV channels of RADARSAT-2 ScanSAR imagery and formulated a condition where the HV channel is more reliable than the HH channel for ice tracking. Furthermore, we found that the ice motion tracking from the HV channel is not affected by noise floor stripes, which are prominent in the cross-polarization RADARSAT-2 ScanSAR images. The developed sea ice tracking technology was implemented at the Canadian Ice Service, Environment Canada for operational use. The system was successfully run to provide operational support of field work in the Arctic Ocean in compliance with the United Nations Convention on the Law of the Sea in the spring of 2010.

[1] Sea ice is exchanged between the Arctic Ocean and Canadian Arctic Archipelago (CAA) but has not been quantified over long time periods. The corresponding mechanisms responsible for recent variability and change also remain unidentified. To address this, we estimated the sea ice area flux between the Arctic Ocean and the M'Clure Strait and Queen Elizabeth Islands (QEI) from 1997 to 2012 for the months of May to November. Over the period, there was a mean flux of −1 × 10³ km² (±21 × 10³ km²) at the M'Clure Strait and mean flux of +8 × 10³ km² (±8 × 10³ km²) at the QEI (positive and negative flux signs correspond to Arctic Ocean ice inflow and outflow, respectively). The M'Clure Strait had a mean flux of +5 × 10³ km² from May to September and a mean flux of −7 × 10³ km² from October to November. The QEI gates had a mean flux of +4 × 10³ km² from August to September with negligible ice exchange from May to July and October to November. More frequent high sea level pressure anomalies over the Beaufort Sea and Canadian Basin since 2007 have reduced Arctic Ocean multiyear ice (MYI) inflow into the M'Clure Strait. The presence of MYI in the CAA originating from the Arctic Ocean has been maintained by inflow at the QEI, which has increased since 2005. These recent increases in Arctic Ocean MYI inflow into the QEI can be attributed to increased open water area within the CAA that have provided more leeway for inflow to occur.

As Arctic sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of sea ice. Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014 and Sentinel-1B (S1) in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent five spaceborne SAR sensors that together routinely cover the pan-Arctic sea ice domain. Here, we describe, apply, and validate the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS) that routinely generates large-scale sea ice motion (SIM) over the pan-Arctic domain using SAR images from S1 and RCM. We applied the ECCC-ASITS to the incoming image streams of S1 and RCM from March 2020 to October 2021 using a total of 135 471 SAR images and generated new SIM datasets (7 d 25 km and 3 d 6.25 km) by combining the image stream outputs of S1 and RCM (S1 + RCM). Results indicate that S1 + RCM SIM provides more coverage in the Hudson Bay, Davis Strait, Beaufort Sea, Bering Sea, and directly over the North Pole compared to SIM from S1 alone. Based on the resolvable S1 + RCM SIM grid cells, the 7 d 25 km spatiotemporal scale is able to provide the most complete picture of SIM across the pan-Arctic from SAR imagery alone, but considerable spatiotemporal coverage is also available from 3 d 6.25 SIM products. S1 + RCM SIM is resolved within the narrow channels and inlets of the Canadian Arctic Archipelago, filling a major gap from coarser-resolution sensors. Validating the ECCC-ASITS using S1 and RCM imagery against buoys indicates a root-mean-square error (RMSE) of 2.78 km for dry ice conditions and 3.43 km for melt season conditions. Higher speeds are more apparent with S1 + RCM SIM as comparison with the National Snow and Ice Data Center (NSIDC) SIM product and the Ocean and Sea Ice Satellite Application Facility (OSI SAF) SIM product indicated an RMSE of u=4.6 km d⁻¹ and v=4.7 km d⁻¹ for the NSIDC and u=3.9 km d⁻¹ and v=3.9 km d⁻¹ for OSI SAF. Overall, our results demonstrate the robustness of the ECCC-ASITS for routinely generating large-scale SIM entirely from SAR imagery across the pan-Arctic domain.

The dielectric properties of sea ice are important for both passive and active microwave remote sensing of sea ice. In this paper, we present a new technique for dielectric measurements of artificially grown sea ice in the frequency range between 0.3 and 12 GHz using an open-ended coaxial probe. To provide a solid contact between the probe and ice, we slightly submerge and then freeze the probe's flange in sea water in a cold laboratory with a preset temperature. Once the ice is formed, we conduct a measurement of the complex reflection coefficient in the cold room using a vector network analyzer. To calibrate the system, we propose a set of measurements from air, shorting block (short), and pure methanol to be conducted immediately after. Both the real and imaginary parts of the complex dielectric constant as functions of frequency are then derived using a coaxial probe inverse model fed by these data. X-ray microtomography analysis of our samples revealed that the ice formed under the described conditions has completely isotropic microstructure typical for the frazil layer of natural first-year sea ice. To evaluate the experimental system's accuracy, we conducted extensive test measurements of standard materials (saline water, methanol, butanol, and pure ice). We also demonstrate that our sea ice dielectric measurements are close to corresponding values previously reported in the literature. The proposed measurement technique is valuable for developing a sea ice dielectric mixture model at microwave frequencies for different temperatures and salinities.

Microwave scattering from sea ice is partially controlled by the ice surface roughness. In this paper, we propose a technique for calculating 2-D centimeter-scale surface roughness parameters, including the rms height, correlation length, and form of autocorrelation function, from 3-D terrestrial light detection and ranging data. We demonstrate that a single scale of roughness can be extracted from complex sea ice surfaces, incorporating multiple scales of topography, after sophisticated 2-D detrending, and calculate roughness parameters for a wide range of artificial and natural sea ice surface types. The 2-D technique is shown to be considerably more precise than standard 1-D profiling techniques and can therefore characterize surface roughness as a stationary single-scale process, which a 1-D technique typically cannot do. Sea ice surfaces are generally found to have strongly anisotropic correlation lengths, indicating that microwave scattering models for sea ice should include surface spectra that vary as a function of the azimuthal angle of incident radiation. However, our results demonstrate that there is no fundamental relationship between the rms height and correlation length for sea ice surfaces if the sampling area is above a threshold minimum size.

Two new models for wind speed retrieval from C-band synthetic aperture radar (SAR) data have been developed, based on a large body of statistics on buoy observations collocated and coinciding with RADARSAT-1 and -2 ScanSAR images. The first model's independent variables are co-polarization (HH) normalized radar cross-section (NRCS), and antenna beam incidence angle. The second model's predictors are HH NRCS, cross-polarization (HV) NRCS, instrument noise floor, and incidence angle. The latter model has better accuracy than the first because of using an additional HV variable. Furthermore, we found that the proposed models without wind direction input demonstrated a better accuracy than CMOD_IFR2 and CMOD5.N models in combination with the SAD HH co-polarization ratio (VV/HH), which require wind direction input. These results were confirmed on a large independent subset of collected data. The developed wind speed retrieval models, in conjunction with our previously developed ice motion tracking algorithm, can be a useful tool for studying sea ice dynamics processes in the marginal ice zone. The developed models have been integrated into a quasi-operational system at the Meteorological Service of Canada.