This paper presents the findings related to the design solution options for a next-generation C-band Synthetic Aperture Radar (SAR) mission, developed to address the Harmonized User Needs (HUN) in Earth observation (EO) data as defined by several departments of the Government of Canada. The work analyses various mission solution options, including multi-satellite constellations, and their performance to evaluate feasibility and assess their compliance with the HUN as well as minimize the associated lifecycle costs, technical risks, implementation schedule, and programmatic challenges. This mission concept contributes to the advancement of space-based surveillance solutions aligned with Canada’s long-term strategic objectives to ensure service continuity for Earth Observation and national security applications. Systematic user needs analysis helped to reveal the importance of high-resolution (1–5 m), enhanced interferometric, polarimetric SAR interferometry (PolInSAR) and other capabilities. Two satellite constellation configurations are proposed: (1) a three-medium-satellite setup with a tandem pair, and (2) a five-large-satellite system incorporating tandem and optimal orbits. Employing High-Resolution Wide Swath (HRWS) imaging modes and full polarimetric capability. Performance simulations indicate low Noise Equivalent Sigma Zero (NESZ) with wide swath width fully addresses driving needs for sea ice and ocean monitoring, covering most of the Canadian areas of interest, with the revisit time of less than 4–6 hours. Orbit optimization ensures high revisit rates, enabling novel interferometric SAR (InSAR) capabilities with observations separated by only a few hours. This mission concept, considering two options with three medium and with five large satellites, respectively, offers a flexible, scalable, and strategically impactful solution for Earth Observation (EO) service continuity and technological leadership for Canada until 2050 and beyond.

Spaceborne synthetic aperture radar (SAR) is an important technology for ship detection applications. It can provide timely information on small boat locations, which is important for security and safety applications. This letter describes the advantages of using very-high-resolution TerraSAR-X data acquired in staring spotlight mode for detecting small boats. Coincident to the SAR image acquisitions, electro-optical (EO) satellite imagery was used, together with field photographs of boats. The results demonstrate the ability to distinguish SAR signatures of small wooden and fiberglass vessels with the size of up to 4 m in length.

The satellite-based techniques for the monitoring of extreme ice features (EIFs) in the Canadian Arctic were investigated and demonstrated using synthetic aperture radar (SAR) and electro-optical data sources. The main EIF types include large ice islands and ice-island fragments, multiyear hummock fields (MYHF) and other EIFs, such as fragments of MYHF and large, newly formed hummock fields. The main objectives for the paper included demonstration of various satellite capabilities over specific regions in the Canadian Arctic to assess their utility to detect and characterize EIFs. Stereo pairs of very-high-resolution (VHR) imagery provided detailed measurements of sea ice topography and were used as validation information for evaluation of the applied techniques. Single-pass interferometric SAR (InSAR) data were used to extract ice topography including hummocks and ice islands. Shape from shading and height from shadow techniques enable us to extract ice topography relying on a single image. A new method for identification of EIFs in sea ice based on the thermal infrared band of Landsat 8 was introduced. The performance of the methods for ice feature height estimation was evaluated by comparing with a stereo or InSAR digital elevation models (DEMs). Full polarimetric RADARSAT-2 data were demonstrated to be useful for identification of ice islands.

Ship detection and classification in very high resolution (VHR) EO/IR satellite imagery, as primary objectives, were investigated using multiple techniques. Automated algorithms were developed and their performance was evaluated using different satellite image sources (Pleiades, WorldView-2/3). Performance of ship detection algorithms based on traditional (thresholding and saliency) techniques reached probability of detection 80% for low false alarm rates. Deep learning techniques based on convolutional neural networks (CNNs) (YOLOv4 and Mask R-CNN) achieved average precision of 94–95% with 3% of false positives without the need of accurate land and cloud masking. Mask R-CNN also allows accurate determining ship size parameters. The problem of ship and non-ship classification was investigated using traditional and CNN based techniques. Linear Discriminant Analysis, Support Vector Machines and combined classifiers achieved classification accuracies close to 80–90%. At the same time, the usage of a technique based on GoogleNet CNN achieved 99% classification accuracy for ship, small boats and background targets.

The Conne River watershed is dominated by wetlands that provide valuable ecosystem services, including contributing to the survivability and propagation of Atlantic salmon, an important subsistence species that has shown a dramatic decline over the past 30 years. To better understand and improve the management of the watershed, and in turn, the Atlantic salmon, a wetland inventory of the area is developed using advanced remote sensing methods including field-collected data, object-based image analysis of Sentinel-1, Sentinel-2, and digital elevation model Earth observation data. The resulting classification maps consisted of bog, fen, swamp, marsh, and open water wetlands with an overall accuracy of 92% and a kappa coefficient of 0.916. Among wetland classes, user and producer accuracies range between 84% and 100%. Results show the dominance of peatland wetlands such as bog and fen, and the relative rareness of marsh wetlands. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

Thanks to increasing urban development, it has become important for municipalities to understand how ecological processes function. In particular, urban wetlands are vital habitats for the people and the animals living amongst them. This is because wetlands provide great services, including water filtration, flood and drought mitigation, and recreational spaces. As such, several recent urban development plans are currently needed to monitor these invaluable ecosystems using timeand cost-efficient approaches. Accordingly, this study is designed to provide an initial response to the need of wetland mapping in the City of St. John's, Newfoundland and Labrador (NL), Canada. Specifically, we produce the first high-resolution wetland map of the City of St. John's using advanced machine learning algorithms, very high-resolution satellite imagery, and airborne LiDAR. An object-based random forest algorithm is applied to features extracted from WorldView-4, GeoEye-1, and LiDAR data to characterize five wetland classes, namely bog, fen, marsh, swamp, and open water, within an urban area. An overall accuracy of 91.12% is obtained for discriminating different wetland types and wetland surface water flow connectivity is also produced using LiDAR data. The resulting wetland classification map and the water surface flow map can help elucidate a greater understanding of the way in which wetlands are connected to the city's landscape and ultimately aid to improve wetland-related conservation and management decisions within the City of St. John's.

Mapping iceberg locations and geometrical parameters is important for marine operational applications and climate science. The innovative TanDEM-X mission (TDM) was used for 3D mapping of icebergs in sea ice with the single-pass SAR interferometry (InSAR) method. The extracted digital elevation model (DEM) from TDM InSAR data over icebergs in sea ice was compared to a DEM generated from very-high-resolution (VHR) electro-optical stereo data. The comparison demonstrated a good correspondence between the electro-optical and InSAR-derived DEMs. A significant decrease in root-mean-squared error (RMSE) was achieved after applying spatial filtering. The resulting RMSE for the areas with selected icebergs in sea ice was 2 m.

The detection of sea ice features, which are hazardous for marine transportation and offshore operations, is important for Canada since it has the world’s longest coastline. The hazardous sea ice features investigated in this work included pressure ridges, rubble fields, hummocks, icebergs and ice islands. The information on sea ice ridges also plays an important role in estimating the total volume of sea ice for the climate science. Previous C-CORE projects supported by the Canadian Space Agency and offshore oil and gas industries have investigated advanced capabilities of Synthetic Aperture Radar (SAR) satellites to detect, characterize and track sea ice features. Validation was performed by comparing SAR results with the results from very high resolution electro-optical imagery. During the last decade, the new satellite-based SAR techniques including polarimetry, interferometry and high resolution imaging performances became available for sea ice applications. RADARSAT-2 (RS-2) full polarimetric capabilities offered an innovative approach for application of polarimetric decompositions and creating solutions to meet the various requirements for sea ice monitoring. It was demonstrated that full and dual polarimetric data are useful for identifying glacier ice (icebergs and ice islands) in sea ice by applying Pauli decomposition and generating color composite images. High resolution modes of RS-2, such as Spotlight and Ultra-Fine are capable for mapping sea ice deformation features including ridges and rubble fields. Stereo capabilities of RS-2 were investigated with extracting digital elevation models of icebergs in sea ice. Capabilities of satellites operating in X-band, such as TerraSAR-X/TanDEM-X (TDM) and COSMO SkyMed (CSK), for mapping of ice features were also investigated. The unique single-pass interferometry with TDM data demonstrated advanced performance in extracting icebergs and ice topography. Three-dimensional information is helpful for identification and characterization of icebergs and hummocks. In addition, the techniques of data fusion and tracking using RS-2 imagery and X-band data from TerraSAR-X and CSK demonstrated promising results in identifying hummocks and icebergs in sea ice.

Information on icebergs and ice islands is important for climate science and for various marine operations in Arctic and Antarctic. This work investigates capabilities of RADARSAT-2 polarimetric data for detection of icebergs in sea ice. Several iceberg detectors were analyzed with the full polarimetric data acquired in Fine Quad and Fine Quad Wide modes. The results of iceberg detection were validated with the information extracted from very high and medium resolution electro-optical satellite data including stereo datasets. It was demonstrated that the accuracy of iceberg detection depends on polarimetric bands, parameters of detection algorithm and sea ice types. The novelty of the work also includes a demonstration of detection characteristics including false alarm rates. Improvement of detection results for icebergs in pack ice was achieved using Pauli decomposition components, span and advancing detection algorithm.

Abstract The Canadian Arctic is a highly dynamic environment that has the oldest and thickest sea ice in the world. The ice includes various features hazardous for shipping and offshore operations. The paper describes a technology addressing the problem of satellite based monitoring of hazardous features, which include ice ridges, hummocks and rubble fields. Additional attention was paid to identifying glacier ice (ice islands and icebergs). The technology was demonstrated using images collected over the Canadian Arctic in 2013-2014 and ice features were verified by analyzing high resolution satellite optical images that overlap spatially and temporally with the synthetic aperture radar (SAR) data. Various satellite images and data fusion techniques have been explored for identifying ice features and retrieving their characteristics. Ice parameters being studied include height, size and frequency of ice features.

Information on the locations and characteristics of extreme sea ice features (such as, hummocks, ridges, stamukhas and icebergs) is important for various marine applications. Imagery acquired by high resolution optical satellites was previously used for qualitative image interpretation to identify various sea ice features and it is especially valuable when detailed ground validation is not available. Current optical satellites, such as GeoEye-1, are able to acquire images with very high resolution of 0.5m. This work addresses the problem of quantitative retrieval of ice feature parameters from very high resolution optical imagery. The developed algorithms facilitate extraction of ice feature height from shadow and derivation of statistical information on ice deformation parameters. Automated processing of GeoEye-1 image demonstrated capabilities of retrieval of ridge frequency and segmentation of rubble fields.

Research on automatic detecting, tracking and characterizing extreme ice features in the Arctic is based on analyzing and processing satellite synthetic aperture radar (SAR) and optical images. Algorithms to identify ridges from very high-resolution optical data have an accuracy of 86.4% when compared to manual extraction and ridge height has been estimated from shadow. SAR signatures of various ice features have been analyzed and the results indicate that it is possible to identify rubble fields from other ice types.

Information on extreme ice features (ridges, icebergs etc.) is important for various marine operations. Satellite synthetic aperture radar (SAR) imagery is capable of monitoring sea ice, identifying and tracking ice features over broad spatial scales. This work investigates possibilities of extreme sea ice features retrieval from various RADARSAT-2 data. Several different beam modes and analysis techniques, such as polarimetric decompositions, were investigated. It was demonstrated that the spatial frequency of sea ice ridges has a very good correlation with the SAR backscatter coefficient. The problem of discriminating glacier ice from sea ice can be resolved by applying Pauli decomposition to full polarimetric data.

Karen Russell, Sherry Warren, Carl Howell, Thomas Puestow, Charles Randell C-CORE Morrissey Road, St. John’s, NL A1B 3X5 Karen.Russell@c-core.ca; Sherry.Warren@c-core.ca; Carl.Howell@c-core.ca Thomas.Puestow@c-core.ca; Charles.Randell@c-core.ca Ali Khan NL Department of Environment and Conservation, St. John’s, NL Chandra Mahabir Alberta Environment, Edmonton, Alberta Patrick Tang New Brunswick Department of Environment, Fredericton, NB Dmitri Burakov Hydrological Forecasts Department, Krasnoyarsk Region, Russia Natalie Novik Northern Forum, Anchorage, Alaska