Abstract:Wetlands are an important natural resource that requires monitoring. A key step in environmental monitoring is to map the locations and characteristics of the resource to better enable assessment of change over time. Synthetic Aperture Radar (SAR) systems are helpful in this way for wetland resources because their data can be used to map and monitor changes in surface water extent, saturated soils, flooded vegetation, and changes in wetland vegetation cover. We review a few techniques to demonstrate SAR capabilities for wetland monitoring, including the commonly used method of grey-level thresholding for mapping surface water and highlighting changes in extent, and approaches for polarimetric decompositions to map flooded vegetation and changes from one class of land cover to another. We use the Curvelet-based change detection and the Wishart-Chernoff Distance approaches to show how they substantially improve mapping of flooded vegetation and flagging areas of change, respectively. We recommend that the increasing availability SAR data and the proven ability of these data to map various components of wetlands mean SAR should be considered as a critical component of a wetland monitoring system.

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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.

Abstract. Waves can drastically transform a sea ice cover by inducing break-up over vast distances in the course of a few hours. However, relatively few detailed studies have described this phenomenon in a quantitative manner, and the process of sea ice break-up by waves needs to be further parameterized and verified before it can be reliably included in forecasting models. In the present work, we discuss sea ice break-up parameterization and demonstrate the existence of an observational threshold separating breaking and non-breaking cases. This threshold is based on information from two recent field campaigns, supplemented with existing observations of sea ice break-up. The data used cover a wide range of scales, from laboratory-grown sea ice to polar field observations. Remarkably, we show that both field and laboratory observations tend to converge to a single quantitative threshold at which the wave-induced sea ice break-up takes place, which opens a promising avenue for robust parametrization in operational forecasting models.

Simulated compact polarimetry from the RADARSAT Constellation Mission (RCM) is evaluated for sea ice classification. Compared to previous studies that evaluated the potential of RCM for sea ice classification, this study focuses on the High Resolution (HR) Synthetic Aperture Radar (SAR) mode of the RCM associated with a higher noise floor (Noise Equivalent Sigma Zero of −19 dB), which can prove challenging for sea ice monitoring. Twenty three Compact Polarimetric (CP) parameters were derived and analyzed for the discrimination between first year ice (FYI) and multiyear ice (MYI). The results of the RCM HR mode are compared with those previously obtained for other RCM SAR modes for possible CP consistency parameters in sea ice classification under different noise floors, spatial resolutions, and radar incidence angles. Finally, effective CP parameters were identified and used for the classification of FYI and MYI using the Random Forest (RF) classification algorithm. This study indicates that, despite the expected high noise floor of the RCM HR mode, CP SAR data from this mode are promising for the classification of FYI and MYI in dry ice winter conditions. The overall classification accuracies of CP SAR data over two test sites (96.13% and 96.84%) were found to be comparable to the accuracies obtained using Full Polarimetric (FP) SAR data (98.99% and 99.20%).