RADARSAT Constellation Mission Research Articles

Baffin Bay Ice Export and Production From Sentinel‐1, the RADARSAT Constellation Mission, and CryoSat‐2: 2016–2022

AbstractBaffin Bay is located between Greenland and several islands of the Canadian Arctic, providing a conduit for the downstream transport of ice and freshwater to the North Atlantic via Davis Strait. Using satellite observations from Sentinel‐1, the RADARSAT Constellation Mission, and CryoSat‐2, we estimated the sea ice export through Davis Strait and winter ice production in Baffin Bay from 2016 to 2022. The average annual ice export for this 6‐year period was 981 ± 193 × 103 km2 for area 816 ± 130 km3 for volume, and 653 ± 130 km3 for solid freshwater, all of which are considerably higher than previously reported estimates. The average winter ice area production upstream of Davis Strait was 758 × 103 km2 and the volume production was 589 km3 indicating that more than 80% of the ice exported out of Baffin Bay was produced locally. Compared to Fram Strait, sea ice fluxes through Davis Strait represent ∼59% of the volume and ∼111% of the area.

Seasonal Variability of Ice Motion for Hubbard and Valerie Glaciers, Alaska

Hubbard Glacier is a large fast-flowing tidewater-terminating glacier in the St. Elias Mountains and is connected at its terminus to Valerie Glacier. Although Hubbard Glacier has been shown to experience large intra-annual velocity changes and a long-term deceleration, previous seasonality studies have had limited timescale without a dense record of motion. Valerie Glacier’s variability has also been understudied, with only one study reporting its seasonal behaviour. The goal of this study was to combine ITS_LIVE, RADARSAT-2, RADARSAT Constellation Mission, and TerraSAR-X/TanDEM-X derived velocity data to create the densest record of motion ever constructed for Hubbard and Valerie glaciers from July 2013-April 2022 in order to explore seasonal velocity variability of both glaciers. Air temperature (NCEP-NCAR Reanalysis) was used to estimate surface melt on the glaciers, which was explored as a potential driver for seasonal velocity changes. Valerie Glacier had a seasonal pattern of fast flow in May, with minimum flow between August-November before accelerating again. Hubbard Glacier displayed a unique seasonal pattern that has not been previously observed on this glacier, with two periods of fast motion: one in May and one in December-February. It is inferred that the spring peaks and late summer/fall minimums on both glaciers are due to meltwater reaching the glacier bed and influencing the subglacial hydrology. The cause of the winter peak and slight velocity drop before the spring peak on Hubbard Glacier has not been determined and should be a topic for future studies, although it is hypothesized to influenced by its geometry.

River ice breakup classification using dual- (HH&HV) or compact-polarization RADARSAT Constellation Mission data

Ice jams and associated flooding during river ice breakup are seasonal hazards for many northern communities. Synthetic Aperture Radar (SAR) data enable timely monitoring of river ice conditions in inclement weather. River ice types and open water are discriminated during breakup using dual-polarized (HH&HV; DPH) and compact polarimetric (CP) C-band SAR imagery from the RADARSAT Constellation Mission (RCM). Study areas comprise nineteen locations on rivers in northern Ontario, the Northwest Territories, and Alberta, Canada. Rubble ice, sheet ice, and open water areas are sampled in RCM imagery using corroborating evidence from shoreline cameras and oblique airborne photography. Samples are obtained in the incidence angle range 19° to 48°, for open water with various wind speeds and flow conditions, and for rubble ice and sheet ice with varying surface roughness, snow cover, and wetness. Discrimination relies on a primary classification of rubble ice, sheet ice, and open water, and a secondary classification of ice roughness. The primary classification model development uses a recursive-partitioning machine-learning technique. Overall accuracies for the best DPH models are 83.8% to 89.6%, depending on image noise floor. DPH models use both HH and HV polarizations for low noise floor image modes, whereas only HH is used for higher noise floor image modes. The best CP model accuracy is 90.2%, using the RL, RR, and RVRH parameters. Secondary classification associates increasing ice roughness with increasing HH backscatter for DPH data, and with increasing RH backscatter for CP data. The angular dependencies of rubble ice or sheet ice are used to normalize backscatter, which is then divided into roughness categories. The DPH and CP classification models are named IceBC-DP and IceBC-CP, respectively. Qualitative analysis illustrates good overall ice type and open water classification. Confounding situations are mixed pixel issues, whitewater and wind roughening of water, moist snow microwave absorption, and superficial water on sheet ice. The development of robust methods for monitoring ice jam formation with RCM is an operational concern for departments within the Government of Canada and within Provincial and Territorial Governments.

Surface Soil Moisture Estimation from Time Series of RADARSAT Constellation Mission Compact Polarimetric Data for the Identification of Water-Saturated Areas

Soil moisture is one of the main factors affecting microwave radar backscatter from the ground. While there are other factors that affect backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, medium resolution, multi-temporal synthetic aperture radar (SAR). Understanding the distribution and identification of water-saturated areas using SAR soil moisture can be important for wetland mapping. The SAR soil moisture retrieval algorithm provides a relative assessment and requires calibration over wet and dry periods. In this work, relative soil moisture indicators are derived from a time series of the RADARSAT Constellation Mission (RCM) SAR compact polarimetric (CP) data over reclaimed areas of an oil sands mine in Alberta, Canada. An evaluation of the soil moisture product is performed using in situ measurements showing agreement from June to September. The surface scattering component of m-chi CP decomposition and the RL SAR products demonstrated a good agreement with the field data (low RMSE values and a perfect alignment with field-identified wetlands).

Sea ice transport and replenishment across and within the Canadian Arctic Archipelago, 2016–2022

Abstract. The Canadian Arctic Archipelago (CAA) serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. We use observations from Sentinel-1, RADARSAT-2, the RADARSAT Constellation Mission (RCM), and CryoSat-2, together with the Canadian Ice Service ice charts, to quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage shipping route. Results indicate that the CAA primarily exports ice to the Arctic Ocean and Baffin Bay, with an average annual (October to September) ice area flux of 137 ± 72 × 103 km2 and a volume flux of 58 ± 68 km3. The CAA contributes a larger area but smaller volume of ice downstream to the North Atlantic than what is delivered via Nares Strait. The average annual ice area flux from the Queen Elizabeth Islands to Parry Channel was 27 ± 10 × 103 km2 and the volume flux was 34 ± 12 km3, with a majority occurring through Byam Martin Channel, which is directly above the central region of Northwest Passage. Over our study period, annual multi-year ice (MYI) replenishment within the CAA was resilient, with an average of 14 ± 38 × 103 km2 imported from the Arctic Ocean and an average of 56 ± 36 × 103 km2 of first-year ice (FYI) retained following the melt season. The considerable ice flux to Parry Channel, together with sustained MYI replenishment, emphasizes the continued risk that sea ice poses to practical utilization of key shipping routes in the CAA, including the Northwest Passage.

Early-Season Crop Classification Based on Local Window Attention Transformer with Time-Series RCM and Sentinel-1

Crop classification is indispensable for agricultural monitoring and food security, but early-season mapping has remained challenging. Synthetic aperture radar (SAR), such as RADARSAT Constellation Mission (RCM) and Sentinel-1, can meet higher requirements on the reliability of satellite data acquisition with all-weather and all-day imaging capability to supply dense observations in the early crop season. This study applied the local window attention transformer (LWAT) to time-series SAR data, including RCM and Sentinel-1, for early-season crop classification. The performance of this integration was evaluated over crop-dominated regions (corn, soybean and wheat) in southwest Ontario, Canada. Comparative analyses against several machine learning and deep learning methods revealed the superiority of the LWAT, achieving an impressive F1-score of 97.96% and a Kappa coefficient of 97.08% for the northern crop region and F1-scores of 98.07% and 97.02% for the southern crop region when leveraging time-series data from RCM and Sentinel-1, respectively. Additionally, by the incremental procedure, the evolution of accuracy determined by RCM and Sentinel-1 was analyzed, which demonstrated that RCM performed better at the beginning of the season and could achieve comparable accuracy to that achieved by utilizing both datasets. Moreover, the beginning of stem elongation of corn was identified as a crucial phenological stage to acquire acceptable crop maps in the early season. This study explores the potential of RCM to provide reliable prior information early enough to assist with in-season production forecasting and decision making.

Addressing the Orbital Debris Threat Directionality for Enhanced Protection of Low-Earth-Orbit Robotic Spacecraft

This study conducts a detailed investigation into addressing the directionality of micrometeoroid and orbital debris (MMOD) threats, emphasizing the strategic placement of protective resources on a spacecraft. The research considers the conversion of the structural sandwich panel and the multilayer thermal blanket into multifunctional elements that provide MMOD protection without causing substantial weight gain or affecting operational parameters. The effectiveness of the reconfigured components, such as the foam core sandwich panel and multilayer insulation blanket toughened by Nextel fabrics, is evaluated using developed and validated numerical models and experimental methods. Their performance is demonstrated by meeting the protection needs of the Canadian robotic Radarsat Constellation Mission spacecraft.

Arctic Sea Ice Topography Information From RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) Backscatter

AbstractSea ice topography information can be obtained from altimetry but these data are spatially and temporally limited compared to recent synthetic aperture radar (SAR) missions such as the RADARSAT Constellation Mission (RCM). We analyze the relationship between sea ice roughness and height obtained from three Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 tracks on two dates in March 2022, with RCM backscatter from 17 images in the McClintock Channel, Canadian Arctic. We analyze how this relationship varies with ice type, polarization, and incidence angle. We find particularly notable relationships between sea ice roughness and horizontal‐transmit/vertical‐receive backscatter for first‐year ice, and sea ice height and backscatter for multi‐year ice. We develop a preliminary model for winter sea ice roughness retrieval using RCM. In comparison with independent ICESat‐2 data in our study region, we find the model performs effectively at estimating a roughness distribution and key roughness statistics, and characterizes spatial variations in roughness at a sub‐kilometer scale.

Ground deformation due to natural resource extraction in the Western Canada Sedimentary Basin

The study links multiple natural resource extraction activities in the Western Canada Sedimentary Basin (WCSB) with observations of ground deformation. Sentinel-1, RADARSAT-2, and RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) data acquired as early as 2015 were used to compute deformation maps and ground deformation time series. Ground deformation produced by induced earthquakes (2015 Mw 4.6 in British Columbia and 2022/23 Mw 5.1/4.6 in Alberta; and 2017/18 Mw 5.0/4.2 slow slip events in British Columbia) were mapped with individual interferograms. Long-lasting ground deformation due to Steam-Assisted Gravity Drainage (SAGD) and Cyclic Steam Stimulation (CSS) enhanced oil recovery and coal and potash underground mining were mapped with the Multidimensional Small Baseline Subset (MSBAS) time series method. When both ascending and descending data were available, MSBAS was used to compute two-dimensional (2D) deformation time series and rates. It was observed that seismic and aseismic ground deformation due to hydraulic fracturing and saltwater injection in WCSB had a similar surface expression (paired anomaly), spatial extent (5-10 km) and orientation (striking NW-SE), suggesting that triggering processes might be technogenic. In the case of hydraulic fracturing, ground deformation appeared soon after the beginning of the operation, while not with saltwater injection, as it took years. The source mechanism of the 2015 Mw 4.6 earthquake was obtained through inverse modelling of the two descending interferograms. The rectangular source model corresponded to a fault at 1.98 ± 0.09 km depth, with 126° ± 7° strike, 14° ± 2° dip, 72° ± 7° rake, 1.61 ± 0.24 km width, 2.54 ± 0.23 km length, and 0.19 ± 0.07 m slip. In the case of in-situ enhanced oil recovery, ground deformation varied significantly depending on the technique. Continuous uplift observed at the SAGD site had a rate of only about 0.05 m/year, while reversible uplift and subsidence at CSS sites exceeded 0.2 m/month, even though wells in both cases were located at approximately similar depths (380 m for SAGD vs 520 m for CSS). In the case of underground mining, the spatial extent of subsidence depended on the extraction depth (200-500 m for coal vs 1000 m for potash). When a mining operation was conducted in an area with topographic relief, horizontal deformation was observed in addition to subsidence. Various observed deformation signals may precede more consequential deformation events.

Development of an algal bloom satellite and in situ metadata hub with case studies in Canada

Satellite remote sensing of algal blooms has been in use for almost five decades and has advanced with an increasing number of sensors, improvements in sensor performance, and developments in algorithms. This progress has enabled the detection and monitoring of blooms in increasingly optically complex water bodies and small lakes, and at finer spatial and temporal resolutions. With climatic and anthropogenic stressors impacting bloom occurrence, duration, and severity, it is critical to understand and characterize blooms across a wide range of spatial and temporal scales. However, resolving blooms at both fine spatial and temporal scales, and simultaneously across broad spatial extents, provides challenges to the limitations of current sensors. This study introduces the Algal Bloom Metadata Hub, an application that can be used to investigate freely available remote sensing data from contemporary satellite sensors to streamline data management and acquisition, particularly when interested in data from multiple satellite sensors. Optical sensors hosted by the hub include the Sentinel-3 Ocean and Land Colour Instrument (OLCI), Sentinel-2 MultiSpectral Instrument (MSI), LANDSAT-8 Operational Land Imager (OLI) and LANDSAT-9 OLI-2; SAR sensors include Sentinel-1, RADARSAT-2, and RADARSAT Constellation Mission (RADARSAT-CM). The application also includes an opportunity for in situ reporting of blooms, providing the potential to improve understanding of bloom occurrence distribution at a wide spatial scale. Case studies in the Western Basin of Lake Erie and Ramsey Lake in Ontario, Canada, demonstrate its utility in identifying suitable remote sensing data. Particularly in Ramsey Lake, the opportunity to leverage scenes from multiple sensors, and the integration of synthetic aperture radar (SAR) data on cloudy days, improved the temporal resolution of bloom monitoring at higher spatial resolutions. This finding was corroborated by performing an investigation into data availability for all small water bodies in Ontario. Further work can be done to integrate data from additional satellite sensors, update the application to add data in real-time, and to quality test in situ data submitted by (citizen) scientists.

Optimizing Soil Moisture Retrieval: Utilizing Compact Polarimetric Features with Advanced Machine Learning Techniques

Soil moisture plays a crucial role in various environmental processes and is essential for agricultural management, hydrological modeling, and climate studies. Synthetic Aperture Radar (SAR) remote sensing presents significant potential for estimating soil moisture due to its ability to operate in all weather conditions and provide day-and-night imaging capabilities. Among the SAR configurations, the Compact Polarimetric (CP) mode has gained increasing interest as it relaxes system constraints, improves coverage, and enhances target information compared to conventional dual polarimetric SAR systems. This paper introduces a novel approach for soil moisture retrieval utilizing machine learning algorithms and CP SAR features. The CP SAR features are derived from a series of RADARSAT Constellation Mission (RCM) CP SAR imagery acquired over Canadian experimental sites equipped with Real-Time In Situ Soil Monitoring for Agriculture (RISMA) stations. This study employs a diverse dataset of compact polarimetric SAR features and corresponding ground truth soil moisture measurements for training and validation purposes. The results of our study achieved a Root Mean Square Error (RMSE) of 6.88% with a coefficient of determination R2 equal to 0.60, which corresponds to a correlation R between true and predicted soil moisture values of 0.75, using optimized Ensemble Learning Regression (ELR) with a decision-tree-based model. These results improved, yielding an RMSE of 5.67% and an R2 equal to 0.73 (R = 0.85), using an optimized Gaussian Process Regression (GPR) model.

Region-Based Sea Ice Mapping Using Compact Polarimetric Synthetic Aperture Radar Imagery with Learned Features and Contextual Information

Operational sea ice maps are usually generated manually using dual-polarization (DP) synthetic aperture radar (SAR) satellite imagery, but there is strong interest in automating this process. Recently launched satellites offer compact polarimetry (CP) imagery that provides more comprehensive polarimetric information compared to DP, which compels the use of CP for automated classification of SAR sea ice imagery. Existing sea ice scene classification algorithms using CP imagery rely on handcrafted features, while neural networks offer the potential of features that are more discriminating. We have developed a new and effective sea ice classification algorithm that leverages the nature of CP data. First, a residual-based convolutional neural network (ResCNN) is implemented to classify each pixel. In parallel, an unsupervised segmentation is performed to generate regions based on CP statistical properties. Regions are assigned a single class label by majority voting using the ResCNN output. For testing, quad-polarimetric (QP) SAR sea ice scenes from the RADARSAT Constellation Mission (RCM) are used, and QP, DP, CP, and reconstructed QP modes are compared for classification accuracy, while also comparing them to other classification approaches. Using CP achieves an overall accuracy of 96.86%, which is comparable to QP (97.16%), and higher than reconstructed QP and DP data by about 2% and 10%, respectively. The implemented algorithm using CP imagery provides an improved option for automated sea ice mapping.

Monitoring autumn agriculture activities using Synthetic Aperture Radar (SAR) and coherence change detection

Across Canada, farmers are encouraged to adopt beneficial management practices (BMPs) to protect soil heath, reduce green house gas emissions and mitigate off-site impacts from agriculture. Measuring the uptake of BMPs, including the implementation of conservation tillage, helps gauge the success of policies and programs to promote adoption. Satellites are one way to monitor BMP adoption and Synthetic Aperture Radars (SARs) are of particular interest given their all-weather data collection capability. This research investigated coherent change detection (CCD) to determine when farmers harvest and till their fields. A time series of both Sentinel-1 and RADARSAT Constellation Mission (RCM) images was acquired over a site in the Canadian Lake Erie basin, during the autumn of 2021, when farmers were harvesting and tilling fields of corn, soybeans and wheat. 16 CCD pairs were created and coherence values were interpreted based on observations collected for 101 fields. An m-chi decomposition was applied to the RCM data, and the Volume/Surface (V/S) ratio was calculated as an additional source of information to interpret results. Change events due to harvest, tillage, autumn seeding and chemical termination resulted in coherence values below 0.20. The mean and standard deviation for fields with observed change was 0.18 ± 0.03. Coherence values were 0.42 ± 0.15 for fields where no change was noted. Tests confirmed that the coherence associated with changed and unchanged fields was significantly different. Coherence values could also differentiate between some types of management events, including tillage and harvest. CCD could also separate harvest as a function of crop type (corn or soybeans). V/S ratios declined after tillage events but increased after both harvesting and chemical termination. Narrowing the date of harvest and tillage is as important as detecting change. To meet this requirement, Sentinel-1 and RCM CCD products with values below 0.20 (indicating change had occurred), were graphically overlaid. With this approach, the timing of corn harvest was identified as occurring within a 5-day window. The tilling of corn, soybeans and wheat was narrowed to a 4-day window. The results of this research confirmed that CCD can be used to capture change due to autumn agricultural activities, and this technique can also separate change due to harvest and tillage. Finally, this study demonstrated that when data from different SAR missions are combined in a virtual constellation, timing of harvest and tillage can be more precisely defined.

FULL POLARIMETRIC TIME SERIES IMAGE ANALYSIS FOR CROP TYPE MAPPING

Abstract. Crop information and quality are not only fundamental for experts using spatial decision support systems but also have many applications in irrigation management, economic analysis for import or export, food safety, and achieving sustainable agriculture. Remote sensing is a cheap and fast way of reaching this goal. Full polarimetric SAR unlike optical sensors is an all-weather system providing geometrical and physical properties of the earth’s surface events. Due to the dynamic changes in crop properties through their phenological stages, crop type mapping has been challenging. As a result, accurate, reliable, and cost-effective crop type mapping using minimum data and processing has been the goal of the remote sensing and precision agriculture community. In this study, a new method based on time series analysis of full polarimetric SAR data combined with radar indices, polarimetric decompositions followed by the three αs extracted from H/A/α decomposition, and unsupervised H/α/Wishart classification bands as features generated from only 5 dates of RADARSAT CONSTELLATION MISSION 2 data were used for classification of crops. Applying random forest and cat boost algorithm as classifiers an accuracy of 87.4% and 75% was respectively achieved. indicating that both algorithms have promising results. Although the random forest algorithm had better results, the cat boost algorithm had less noise in each field and more homogenous farms were detected.

Radarsat Constellation Mission Derived Winter Glacier Velocities for the St. Elias Icefield, Yukon/Alaska: 2022 and 2023

Here we use high resolution (5 m) Radarsat Constellation Mission (RCM) imagery acquired in winters 2022 and 2023 to determine motion across glaciers of the St. Elias Icefield in Yukon/Alaska. Our regional velocity mapping largely conforms with previous studies, with faster motion (>600 m/yr) for the glaciers originating in the Yukon that drain southward and westward to the coast of Alaska and relatively slower motion (100–400 m/yr) for the land terminating glaciers that drain eastward and northeastward and stay within the Yukon. We also identify two new glacier surges within the icefields: the surge of Nàłùdäy (Lowell) Glacier in Winter 2022, and Chitina Glacier in Winter 2023, and track the progression of each surge from January to March utilizing ∼4-day repeat RCM imagery. To evaluate the quality of RCM-derived velocities, we compare our results with 50 simultaneous measurements at three on-ice dGPS stations located on two Yukon glaciers and find the average absolute difference between measurements to be 6.6 m/yr. Our results demonstrate the utility of RCM data to determine glacier motion across large regions with complex topography, to support process-based studies of fast flowing and surge-type glaciers and continue the legacy of velocity products derived from the Radarsat-2 mission.

Derivation and assessment of forest-relevant polarimetric indices using RCM compact-pol data

ABSTRACT Three radar polarimetric indices, Radar Vegetation Index (RVI), Canopy Structure Index (CSI) and Radar Forest Degradation Index (RFDI)—normally associated with quad-pol (QP) synthetic aperture radar (SAR) sensors—were derived using compact polarimetry (CP) data from the Radarsat Constellation Mission (RCM). Indices were generated over a 10,000-hectare temperate mixedwood forest containing a range of complex forest structures. For comparative purposes, the same indices were generated using Radarsat-2 QP data. Agreement between CP and QP indices were assessed across broad vegetated land cover types, at the forest stand-level wherein ground plots and airborne LiDAR data were available, and at the pixel level within validation stands representing different forest types. Agreement was consistently strong for the RVI and weak for the CSI, with agreement stronger when generalized to the stand level. Indices were more informative on differences between vegetation types (e.g. forest and open wetland) than between forest types with different structures. With a radar nominal off-nadir incidence angle at 38°, RCM CP indices had narrower dynamic ranges compared to Radarsat-2 QP indices, especially the CSI, contributing to a lower level agreement for the CSI. CP data enables derivation of RVI, CSI and RFDI indices simultaneously, and provides large spatial coverage and capacity for dense time-series collection, features which are unavailable from current QP sensors.

Local and Global Spatial Information for Land Cover Semisupervised Classification of Complex Polarimetric SAR Data

Each of the three satellites constituting the RADARSAT Constellation Mission (RCM) provide compact polarimetric synthetic aperture radar (CP SAR) data. The complex CP data have similar properties to the complex quad polarimetric (QP) data provided by prior RADARSAT missions. In this paper, a land cover classification method using spatial information is designed based on the statistical characteristics of the complex CP and QP SAR data. First, the local spatial dependency among pixels is captured by superpixels. Second, a graph is constructed on the superpixels to model the global spatial dependency among superpixels. The land cover classification image with land cover type labels is then estimated by propagating labels from the few labeled superpixels to the unlabeled superpixels. Classification of two RCM complex CP and QP scenes demonstrates that the proposed method, with few labeled pixels, provides much higher classification accuracy than methods which do not exploit global spatial dependency.

Retrieval performances of different crop growth descriptors from full- and compact-polarimetric SAR decompositions

This study investigates the different performances between Compact-Polarimetric (CP) and Full-Polarimetric (FP) SAR to retrieve multiple crop growth parameters including Vegetation Water Content (VWC), Leaf Area Index (LAI), height, and dry biomass. The objective is to study at which conditions the CP SAR can obtain comparable retrieval accuracy as FP SAR for specific crop types and growth descriptors. Polarimetric decompositions were used to extract CP and FP explanatory variables to quantify crop growth status. Then, the sensitivities of CP and FP variables to different crop descriptors were analyzed, revealing higher sensitivity to height than LAI, VWC, and dry biomass. In order to reduce the information redundancy of sets of CP and FP SAR variables, Partial Least Square (PLS) approach was used to develop new orthogonal SAR parameters, while Step-Wise Regression (SWR) was used to determine the optimal SAR parameters, followed by retrievals of multiple crop growth descriptors using the developed estimators. Validated by the ground measurements, the retrieval performances are found to be highly dependent on polarimetry dimension (CP or FP), crop types, and targeted crop descriptors. With crop growth and enhanced depolarization effects, the CP SAR can capture the increasing volume power and decreasing surface power but at a lower rate than FP SAR. The m-δ and m-χ decompositions provide similar scattering components which differ from the Random Volume Over Ground (RVOG) decomposition. The third Stokes parameter g3 of the CP SAR data that indicates the strength of the circular polarization component is a common important parameter to infer the multiple crop parameters of interest. In agreement with the sensitivity analysis, better retrieval accuracies were obtained for height and dry biomass than VWC and LAI. For short vegetation such as soybean, full polarimetry is required to obtain the estimates of all four crop growth descriptors. On the contrary, for tall crop growth parameters, such as canola (height and dry biomass), corn (height, VWC, dry biomass), and wheat (LAI, VWC, dry biomass), the CP SAR features can provide comparable robust retrieval accuracy as the FP SAR features. This study deepens our insights into the CP SAR of the RADARSAT Constellation Mission (RCM) to timely monitor crop growth and health status.

Monitoring Crops Using Compact Polarimetry and the RADARSAT Constellation Mission

The RADARSAT Constellation Mission (RCM) can acquire imagery in Compact Polarimetric (CP) mode. With this new mode, and the increased revisit with three satellites, RCM can contribute to operational crop monitoring at national scales. The four Stokes (S0, S1, S2 and S3) and three m-chi decomposition (surface, double bounce, volume) parameters were used to identify crops (pasture/forage, barley, wheat, canola, flaxseed, peas, lentils) with a Random Forest classifier. The Stokes and m-chi parameters delivered maps of similar accuracies (95% overall accuracy) and were only slightly less accurate than a classification using optical satellite imagery (97%). To understand why Stokes parameters worked well in classifying crops, scattering responses for wheat, canola, lentils and peas were plotted on the Poincaré sphere. These responses were interpreted in the context of the degree of polarization and were related to crop phenology. These plots revealed that early and late in the season the polarized component of the scattered wave remained circular. However, in the active season when crop structure was changing, scattered waves became more elliptically polarized. Although the amount of polarized scattering was lower mid-season, the change in ellipticity was helpful in separating crop types.

Oil Spill Candidate Detection Using a Conditional Random Field Model on Simulated Compact Polarimetric Imagery

Although the compact polarimetric (CP) synthetic aperture radar (SAR) mode of the RADARSAT Constellation Mission (RCM) offers new opportunities for oil spill candidate detection, there has not been an efficient machine learning model explicitly designed to utilize this new CP SAR data for improved detection. This paper presents a conditional random field model based on the Wishart mixture model (CRF-WMM) to detect oil spill candidates in CP SAR imagery. First, a “Wishart mixture model” (WMM) is designed as the unary potential in the CRF-WMM to address the class-dependent information of oil spill candidates and oil free water. Second, we introduce a new similarity measure based on CP statistics designed as a pairwise potential in the CRF-WMM model so that pixels with strong spatial connections have the same class label. Finally, we investigate three different optimization approaches to solve the resulting maximum a posterior (MAP) problem, namely iterated conditional modes (ICM), simulated annealing (SA), and graph cuts (GC). The results show that our proposed CRF-WMM model can delineate oil spill candidates better than the traditional CRF approaches, and that the GC algorithm provides the best optimization.