The performance tradeoffs required for a small satellite synthetic aperture radar (SAR) system designed to measure surface deformations using persistent scatterer interferometry (PSI) are investigated. Existing X-band satellite data is systematically deteriorated to account for the increased range resolution and noise-equivalent sigma zero (NESZ). It is found that with an NESZ below 0 dB, the deformation signal of a selected region of interest (ROI) can be captured with mean absolute errors of 5 and 15 mm, for ground range resolutions of 5 and 20 m, respectively. This analysis is used to develop preliminary SAR system designs suitable for small satellites.

The detection of oil spills in oceans has attracted considerable attention due to their adverse effects on marine ecosystems. Synthetic Aperture Radar (SAR) has emerged as a crucial tool for monitoring maritime pollution. Effective oil spill detection via SAR requires a minimal noise floor, extensive coverage, and polarization diversity to improve the identification and discrimination of pollution characteristics. The RADARSAT Constellation Mission (RCM) allows for the acquisition of Hybrid Polarimetric (HP) SAR imagery across all operational imaging modes. In this study, we investigate the effectiveness of a comprehensive set of CP features for detecting oil spills, focusing on the incident involving the bulk carrier MV Wakashio, which ran aground in July 2020 near the southeastern shores of Mauritius. Two SAR images were obtained over the experimental site using the RCM 16MCP imaging mode. Our results demonstrate that numerous CP features show promising capabilities in distinguishing various concentrations of oil spills.

Water ecosystems as oceans, lakes and rivers contribute significantly to global biodiversity, influence the ecological balance and play a remarkable role in different aspects of human society and economy. Therefore, the detection of changes associated to natural and human-induced water dynamics is of great importance for ecosystem preservation, disaster warnings and water conservation projects. To this aims, it is well established that satellite remote sensing tools represent an invaluable source of information.Under this framework, this study focuses on the analysis of dual-polarimetric C-band synthetic aperture radar imaging modes, including compact and linear ones, to extract lake waterline and, therefore, monitor the water-covered area. A reference waterline extraction scheme is adopted for this purpose, which is feed by polarimetric features associated to the two backscattering channels, namely δ and ρ for the compact polarimetric Radarsat Constellation Mission and the linear polarization Sentinel-1 mission. The Athabasca lake in Alberta, Canada, is selected as a meaningful test site. The experimental results show that the dual-polarimetric C-band synthetic aperture radar data can be effectively used to get lake extent information, with subtle differences that apply between RCM compact-polarimetric and Sentinel-1 linear polarization imaging modes.

Global Navigation Satellite System-Interferometric Reflectometry (GNSS-IR) is an emerging sensor technique that has become well-established for water level monitoring. While GNSS-IR has previously been employed for monitoring properties of lake ice and sea ice, it has not been applied for monitoring river ice. This paper presents results from monitoring river ice breakup at three sites in Canada. GNSS-IR data was compared to co-located time-lapse camera imagery and it was found that GNSS-IR signal was sensitive to periods where there is rough or broken ice in view of the sensor. Using data from Sentinel-1 and the RADARSAT Constellation Mission (RCM), the first ever comparison of GNSS-IR with Synthetic Aperture Radar (SAR) imagery is presented and a negative correlation of -0.8 is found between the GNSS-IR spectral power and SAR backscatter. Three classification algorithms of varying complexity (K-means clustering, neural network and random forest) are explored for detecting river ice using GNSS-IR. Using a shallow neural network with two hidden layers, an optimal accuracy of up to 94% is achieved over all three sites, or 97% when mixed water-ice conditions are excluded from the analysis. In summary, GNSS-IR has strong potential for ice monitoring applications, including monitoring the formation of ice jams.

The Canadian RADARSAT Constellation Mission (RCM) has passed its early operation phase, with the current performance evaluation. In this study, we provide results update on RCM performance for selected SAR applications. The RCM was designed to address three core applications-disaster management, maritime surveillance, and ecosystem monitoring. Our study shows a promising level of agreement between RCM and RADARSAT-2 performance in flood mapping using dual-polarized HH-HV SAR data over Red River, Manitoba. Visual analysis of coincident RCM compact polarimetric and RADARSAT-2 dual-polarized HH-HV SAR imagery over the Resolute Passage, Canadian Central Arctic, highlighted an improved contrast between sea ice classes in dry ice winter conditions. Object-oriented classification of a wetland area in Newfoundland and Labrador by fusion of RCM dual-polarized VV-VH data and Sentinel-2 optical imagery revealed promising classification results, with an overall accuracy of 91.1% and a kappa coefficient of 0.87.

Synthetic aperture radar (SAR) is a valuable tool for lake ice monitoring. The recently proposed SAR configuration for Earth observation called compact polarimetric (CP) SAR could be a good compromised choice between conventional (single or dual) and fully polarimetric (FP) SAR for operational ice applications, including lake ice. Given its enhanced radar target information compared with conventional SAR systems over wider swath coverage compared with FP SAR, CP systems could play important role in the new generation of Earth observation systems. Herein, we study the evolution of CP SAR parameters from simulated CP SAR data in relation to early ice growth. Focus of the study is on four lakes located in Cornwallis Island, Canadian Central Arctic. We adopt parameters extracted from dual circular polarimetric and right circular transmit, linear (horizontal and vertical) receive configurations. In this study, we consider the ice thickness calculated from an established empirical model. Meteorological and ice climatological data were used to support the analysis. Results demonstrated a potential connection between a number of CP parameters and lake ice growth. Furthermore, we were able to highlight the relationship between the density of air bubbles in ice layer and the intensity of volume scattering mechanism, leading to the identification of lakes with increased gas production activities. Thus, differences between lakes in terms of density of air bubbles were detected and statistically evaluated.

Synthetic Aperture Radar (SAR) remote sensing has become a valuable tool for sea ice monitoring. A recently proposed SAR configuration for Earth observation called compact polarimetric (CP) SAR could be a good compromised choice between conventional (single or dual) and fully polarimetric SAR for operational sea ice observation. Given its enhanced radar target information compared to conventional SAR systems over wider swath coverage compared to fully polarimetric SAR systems, CP SAR systems could play important role in the new generation of Earth observation systems. In this study, fully polarimetric SAR images were collected over the Resolute Bay area during the fall of 2017. Acquired images are used for the simulation of CP SAR images and the derivation of a set of 23 CP SAR parameters from each image. The derived CP parameters were analysed in relation to the ice thickness and salinity of lake ice and fast sea ice. Results are compared against backscattering and decomposition parameters derived from the fully polarimetric SAR imagery.

This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture (SMC) from SAR acquisitions at C- and X-band.The study was conducted on an alpine test area in Italy and two agricultural areas in Canada, for which series of Radarsat-2 (RS2) and COSMO-SkyMed (CSK) images were available along with direct measurements of SMC from in-situ stations. The analysis confirmed the sensitivity of SAR backscattering (σ°) from both sensors to the SMC variations, with similar correlations (R ≃0.5). The comparison of SMC with the Compact Polarimetric (CP) parameters, computed from the RS2 acquisitions by Radarsat Constellation Mission (RCM) data simulator pointed out that the right and left polarized signals and the Shannon entropy intensity also have some sensitivity to SMC variations, with R ≃0.4 for all the three parameters.Based on these results, two different machine learning (ML) algorithms, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been implemented and tested on the available data. On the South Tyrol test area, both SVR and ANN tested with different combinations of RS2 and CSK data were able to retrieve SMC with a RMSE between 4% and 6% of SMC and R between 0.78 and 0.88, depending on the combination of inputs. The ANN algorithm based on CP data was tested on the Canada areas, being able to estimate SMC with a RMSE between 2% and 5% of SMC and R between 0.85 and 0.96.

Significant wave height is an extremely important descriptor of the ocean wave field. We have implemented the CWAVE algorithm using linear regression, with elastic net term selection, and single-layer feed-forward neural network using buoy observations and RADARSAT-2 Fine Quad image data as model inputs. We used a number of standard performance metrics and found that the neural network models comprehensively outperformed the regression models. We explored the effect of incidence angle and polarization on model performance and found that the most accurate models were implemented within incidence angle bins between 1° and 2°, rather than including incidence angle as an independent variable. We found that the performance of copol (horizontal-horizontal, vertical-vertical, and RL) and hybrid-pol (right-circular-horizontal and right-circular-vertical) channels was comparable, and that these channels outperformed cross-pol channels (horizontal-vertical and right-circular-right-circular). The accuracy of our Hsestimates was significantly higher than other published linear regression and neural network results. We demonstrate that a major factor in improving the accuracy of Hsestimation is to use buoy observations rather that operation wave model hindcasts as training data. We demonstrate an application of our model by creating two high-resolution Hsmaps.

Synthetic Aperture Radar (SAR) remote sensing has become a valuable tool for sea ice characterization. Operational sea ice monitoring usually relies on SAR data from single- or dual-polarized beam modes such as the ScanSAR mode of RADARSAT-2. However, such imagery cannot accurately discriminate certain sea ice types and open water states at all times during the year. Thus, the recently proposed compact polarimetric (CP) SAR configuration for Earth observation could be a compromised choice for operational sea ice observation. This configuration will be included in the future RADARSAT Constellation Mission (RCM). In this study, simulated CP SAR data of the RCM High Resolution (HR) SAR mode is evaluated for sea ice classification. Results indicate promising performance of the RCM HR mode for First Year Ice (FYI) and Multiyear Ice (MYI) classification using CP SAR data.

Operational detection and discrimination of oil spills over oceans has received considerable attention due to its impact on marine ecosystem from environmental and political points of view. Synthetic Aperture Radar (SAR) is a valuable instrument for maritime pollution monitoring. The three main requirements for effective operational oil spill detection using SAR are: 1) low noise floor, 2) large area coverage, and 3) maximizing detection and discrimination of pollution and `lookalike' features, by polarization diversity, multiple frequency, etc. In order to reconcile the advantages of fully polarimetric SAR with larger area coverage, compact polarimetry (CP) acquisitions offer a trade-off between the above mentioned requirements. The future Canadian RADARSAT Constellation Mission (RCM) will enable the acquisition of CP SAR data in wide swath imagery, including ScanSAR modes. In this study, we investigate the potential of CP from three RCM SAR modes for oil spill detection. Results indicate that the RCM MR30 SAR mode has promising oil spill detection performance.

This research aims at exploiting the integration of C- and X-band SAR data for the monitoring of Soil Moisture Content (SMC). Time series of Radarsat2 (RS2) and COSMO-SkyMed (CSK) images are collected on two test areas, located in Italy and in Canada. The backscattering sensitivity to SMC measured by in-situ stations is investigated considering the available sensor frequencies and polarizations. In addition, for exploiting the potential of fully polarimetric acquisitions of RS2, simulated Compact Polarimetric (CP) data are computed by using a Radarsat Constellation Mission (RCM) data simulator, and their sensitivity to the target SMC is examined. Based on the experimental findings, two machine learning (ML) approaches to the SMC retrieval, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN) are implemented and tested on the two areas. Looking at the preliminary results, the integration of X- and C-band images does provide valuable information for the retrieval of SMC, while the simulated CP parameters exhibit a certain sensitivity to SMC. On the South Tyrol test area, both SVR and ANN tested with different combinations of RS2 and CSK data were able to retrieve SMC with a RMSE between 2% and 4% of SMC and correlation coefficient R between 0.85 and 0.97, depending on the combination of inputs. The application of the ML algorithms to the other available images on the Mazia test area and the implementation of the ML retrieval algorithm using CP data are still under investigation.

The RADARSAT Constellation Mission (RCM) is a future Canadian Synthetic Aperture Radar (SAR) mission to be launched in July 2018. The hybrid polarity SAR architecture will be included in the RCM mission, allowing the acquisition of compact polarimetric (CP) SAR data in wide swath imagery. In this study, we investigate the potential of the RCM CP SAR StripMap medium resolution mode for multi-temporal wetlands monitoring using a time series of simulated RCM CP data. Test site for this study is the Bay of Quinte, located on the northern shore of Lake Ontario, Canada. Results show that the tested RCM CP mode is promising for wetland monitoring through change detection.

Synthetic Aperture Radar (SAR) remote sensing has become a valuable tool for maritime pollution monitoring with three major requirements: 1) low noise floor, 2) large area coverage, and 3) polarization diversity to maximize detection and discrimination of pollution features. In order to reconcile the advantages of fully polarimetric SAR with larger area coverage, compact polarimetry (CP) acquisitions offer a trade-off between the above mentioned requirements. The future Canadian RADARSAT Constellation Mission (RCM) will enable the acquisition of CP SAR data in wide swath imagery, including ScanSAR modes. In this study, we investigate the potential of CP for four RCM SAR modes for oil spill detection. These modes have different spatial resolutions and noise floors. An initial visual interpretation of the results indicates potential of some CP features for the discrimination between oil spills and lookalike.

This paper shows that the m-chi decomposition, the Shannon-Entropy model and the Wishart-Chernoff distance can be used to map and monitor wetlands. Areas which changed from flooded vegetation to non-flooded vegetation were accurately mapped using the m-chi decomposition, and areas that changed from saturated soil to unsaturated soil were visible with the Shannon-Entropy model. In addition, the Wishart-Chernoff distance was able to map wetland areas which had changed to a different land cover type over time.

A new unsupervised classification approach for polarimetric synthetic aperture radar (POLSAR) data is proposed in this paper. The Wishart-Chernoff distance is calculated and used in an agglomerative hierarchical clustering approach. Initial segmentation of POLSAR data into clusters is obtained based on the total backscattering power (SPAN) combined with the entropy, alpha angle, and anisotropy. The complex Wishart clustering is performed to optimize the initialization. Optimized clusters with minimum Wishart-Chernoff distance are merged hierarchically into an appropriate number of classes. The appropriate number of classes is estimated based on the data log-likelihood algorithm. Classification results show that the use of Wishart-Chernoff distance is superior to that of the Wishart test statistic distance. The effectiveness of the proposed Wishart-Chernoff distance is demonstrated using Advanced Land Observing Satellite POLSAR data.

The recently observed reduction in perennial ice in the Arctic has given rise to a corresponding increase in seasonal ice, which includes young ice (YI). This type of ice has a major impact on the weather and climate systems. However, only a limited number of studies have been dedicated to explore its spatial coverage and duration. This is mainly due to the lack of remote sensing tools that can identify it. This study uses an ice type and concentration retrieval algorithm, namely, Environment Canada's Ice Concentration Extractor, to study YI distribution and duration in the Arctic during seven ice formation seasons: 2002-03 to 2008-09. Results on the YI area, peak period, duration, and its interannual variability are presented in six regions covering the Arctic Basin. Duration is presented in terms of two parameters that describe the peak period and the number of days when YI concentration exceeds 50%. Probability distribution of the latter parameter shows that YI survives very few days before it grows into first-year ice. The summer of the minimum ice record in 2007 did not leave a remarkable impact on the subsequent YI area or its duration, although a delay in ice formation is observed. YI in the North Water polynya is also studied and shows no particular trend, although it varies between years. Anomalies are explained in terms of modeled surface temperature and wind.

Image segmentation is a basic step of any segment-based classification method. Various segmentation approaches of polarimetric SAR data, such as region growing and splitmerge to name a few, have been proposed recently. This paper describes the development of a new segmentation approach that improves the polarimetric SAR data analysis by including information from the backscattering behavior of objects in the Freeman-Durden analysis images. This method is based on the main scattering mechanism that appears in each image pixel and the second most important scattering mechanism that might have been contributed significantly in the scattering process. Further segmentation is performed based on the calculated histograms of sub-regions. The state-of-art ALOS polarimetric SAR data are used in this study. The study area is located in the south of the United Kingdom and includes the city of Minehead.