Satellite SAR Data Research Articles

Flood Inundation Mapping and Impact Assessment Using Multi-Temporal Optical and SAR Satellite Data: a Case Study of 2017 Flood in Darbhanga District, Bihar, India

Flooding is a recurrent hazard in east Gangetic plains, largely on account of natural factors that pose risks to life and property. Bagmati and Burhi Gandak rivers draining parts of North Bihar causes substantial flooding owing to higher rainfall. This comprehensive study was carried out to map near real-time flood inundation using multi-temporal Sentinel-1A (SAR) and Moderate-resolution Imaging Spectroradiometer Near Real-Time (MODIS NRT) flood data (Optical and 3-day composite) over Darbhanga district of North Bihar during August and September 2017. Floodwater pixels were extracted using the binarization technique, wherein the threshold was applied as −22.5, −23.4, −23.8 and − 22.7 over VH polarization image. The key results revealed that during peak flooding stage (23rd August), 13% of areas were submerged based on SAR data, whereas overestimation by >20% was estimated using MODIS data. As shown in the composite flood inundated map, the inundated patches are quite similar in both the optical and SAR based data. Notably, there were higher flood patches observed in the central, northern, and western parts of the district due to the presence of more water channels in those regions. Our findings suggested that agriculture patches of ~392 sq.km area were inundated due to flood followed by vegetation clutters (16.07 sq.km) and urban (8.46 sq.km). These results indicated the impact of floodwater on agriculture and urban patches. These findings are crucial for policymakers to assess flood impacts. It can be inferred that flood prognosis using SAR data will lead to spatial accuracy and can be improved when coupling with various hydro-meteorological parameters and hydrological models.

Application and Evaluation of the Gaofen-3 Satellite on a Terrain Survey with InSAR Technology

The Gaofen-3 satellite is the first SAR satellite independently developed in China that achieves interferometric imaging and measurement, which improves upon Chinese civil SAR satellite data. To verify the ability of the Gaofen-3 satellite’s InSAR technology, we acquired data from Dengfeng, China, to evaluate the application and accuracy of an InSAR terrain survey. To reduce the effects introduced by data processing of Gaofen-3 data, high-accuracy InSAR image pair co-registration and phase filtering methods were adopted. Six GCPs data and 1:2000-scale DEM data were used to evaluate the elevation accuracy. In addition, for comparison with other satellites, we processed the dataset of the same area acquired by the non-civilian Yaogan-29 satellite with the same methods and evaluated the results. The experimental results indicated that the interferometric data of the Gaofen-3 satellite can achieve an accuracy of higher than 4 m of interferometric height measurement. Therefore, it will have broad prospects in the domestic InSAR application. Our research provides a certain value for the reference of the development of InSAR sensors in China.

Toward the GMF for Wind Speed and Surface Stress Retrieval in Hurricanes Based on the Collocated GPS-Dropsonde and Remote Sensing Data

This article describes the first step toward the development of the geophysical model function (GMF) for the retrieval of wind speed and wind stress in hurricanes, based on developing a relation between the cross-polarized satellite SAR data from Sentinel-1 and winds/stress observed from collocated NOAA GPS-dropsondes data. Field measurements and remote sensing data for tropical cyclones in the Atlantic Ocean were analyzed. Using the data measured by GPS-dropsondes, average wind velocity profiles were obtained, while the parameters of the wind boundary layer (drag coefficient and friction velocity) were restored from the “wake” part of the velocity profiles using the self-similarity property. The self-similarity of the velocity profile “defect” in the boundary layer, known from the fluid dynamics, was used to retrieve the parameters of the atmospheric boundary layer (the surface wind velocity, drag coefficient, and friction velocity) from the dropsonde wind velocity profiles in ten major hurricanes. Based on the processing of the measurements in the hurricanes Irma 2017/09/07 and Maria 2017/09/21 and 2017/09/23 at a time close to the time of acquisition of the Sentinel-1 images, the dependencies of the cross-polarized normalized radar cross section on the wind speed and wind friction velocity were obtained and used for constructing the GMFs.

IMPROVEMENT OF EXISTING AND DEVELOPMENT OF FUTURE COPERNICUS LAND MONITORING PRODUCTS – THE ECOLASS PROJECT

Abstract. The Horizon 2020 project ECoLaSS (Evolution of Copernicus Land Services based on Sentinel data) contributes to improving existing and developing next-generation Copernicus Land Monitoring Service (CLMS) products. The High Resolution Layers (HRLs) are currently produced in regular 3-year intervals at 10–20 meter spatial resolution for 39 European countries (EEA 39). Evolving scientific developments and user requirements are continuously analysed in a close stakeholder interaction process with the European Entrusted Entities (EEE), targeting a future pan-European roll-out of new/improved CLMS products and assessing transferability to global applications. Products and methods are being prototypically demonstrated. Representative sites (60,000–90,000 km2) were selected, covering boreal, Mediterranean, steppic, Atlantic, alpine and continental conditions. Improvements comprise yearly updates of enhanced dominant leaf types and tree cover change layers, better-quality permanent grassland classification and use categorisation. Novel products target agriculture products (i.e., crop mask, crop types). Temporal analysis, based on optical (Sentinel-2) and SAR (Sentinel-1) satellite data, makes use of temporal feature descriptors (multiple temporal statistical metrics) derived from spectral bands and indices (e.g., VV/VH ratio and NDVVVH from SAR data and NDWI, NDVI, Brightness and IRECI from optical data). Overall accuracies range from 77–98%. Rigorous benchmarking is applied to assess the prototypes’ operational readiness and technical maturity for integration into the CLMS architecture.

Collaborative inversion heavy metal stress in rice by using two-dimensional spectral feature space based on HJ-1 A HSI and radarsat-2 SAR remote sensing data

Accurate and extensive monitoring of heavy metal pollution levels in rice fields is crucial to agricultural production and food safety.Most previous studies used optical remote sensing data to monitor heavy metal stress in rice in which way the remote sensing features are not diversified and the monitoring accuracy is relatively low. Compared with that optical remote sensing can monitor features like color and cell internal structure, microwave remote sensing can monitor the morphology and geometrical features. The complementary characteristics of multi-source remote sensing data are reflected in two aspects. On the one hand, the high spectral resolution characteristics of hj-1a HIS and the high spatial resolution characteristics of radarsat-2 SAR satellite are used for feature fusion to obtain the image data of high altitude spectral resolution; on the other hand, the two remote sensing data can depict the vegetation characteristics from different angles. In order to fully extract the characteristics of crop stress, the model was constructed by utilizing the complementary characteristics of multi-source remote sensing data. This paper synergized optical and microwave remote sensing to construct the crop heavy metal stress monitoring model. To this end, certain rice-growing areas polluted by heavy metal in Suzhou city were selected as the experimental areas, where ASD spectrum data, biochemical parameters and heavy metal content data of rice were collected during critical growth periods on one hand, HJ-1 A HIS and Radarsat-2 SAR satellite data were obtained almost simultaneously on the other hand. Based on heavy metal stress-responsive mechanism of rice, NVI (R598, R508) and SVI(HV,VH,HH,VV), spectral indexes sensitive to heavy metal stress, were extracted from the optical and radar data respectively to construct a two-dimensional feature space, based on which an optical-and-radar-remote-sensing-combined model for monitoring heavy metal stress in rice was constructed. In this paper, the study and main conclusions are as follows:(1) It used spectral characteristics analysis combined with statistic methods or random forest algorithm to build the canopy chlorophyll index NVI, which is sensitive to chlorophyll content changes of rice under heavy metal stress, and a heavy metal stress level inversion model was built based on hyperspectral HSI data accordingly. Also, it used statistic method to build the microwave index SVI, which is sensitive to biomass changes of rice under heavy metal stress, and a heavy metal stress level inversion model was built based on microwave SAR images as a result. (2) A combination of biochemical and morphological parameters, both responding to heavy metal stress, was synergized with NVI and SVI, sensitive to chlorophyll and biomass changes respectively, to build a two-dimensional feature space. Heavy metal stress levels were classified in this space. Thus, a synergetic model to retrieve heavy metal stress based on multi-source remote sensing data was developed. In this paper, the innovation lies in a model constructed by synergizing optical and radar remote sensing data to monitor heavy metal stress in rice based on a multi-dimensional spectral feature space and this model can be applied to monitoring multiple environmental stresses in crops.

In-season early mapping of rice area and flooding dynamics from optical and SAR satellite data

ABSTRACT Rice mapping products were derived from Sentinel-1A and Landsat-8 OLI multi-temporal imagery over Northern Italy at the early stages of the 2015 growing season. A rule-based algorithm was applied to synthetic statistical metrics (TSDs-Temporal Spectra Descriptors) computed from temporal datasets of optical spectral indices and SAR backscattering coefficient. Temporal series are available up to the tillering/full canopy cover stage which is identified as the optimum timing for delivering in-season information on rice area (i.e. mid July). The approach relies on a-priori knowledge on crop dynamics to adapt time horizons for TSD computation and thresholds to local conditions. Output products consist of maps of rice cultivated areas, rice seeding techniques (dry and flooded rice) and flooding practices. Validation showed rice mapping overall accuracy to be 87.8% with commission and omission errors of 3.5% and 24.7%, respectively. Mapping of rice seeding technique showed good agreement with farmer declarations aggregated at the municipality scale (dry rice r 2 = 0.71 and flooded rice r 2 = 0.91). Finally, flood maps have an overall accuracy above 70%. Geo-products on rice areas and flooding occurrence are relevant information for water management at regional scale especially during summer in presence of multiple crops and water shortage.

高分辨率星载SAR影像在岛礁测图中的应用研究

高分辨率星载SAR影像在岛礁测图中的应用研究

Spatial Distribution of Oil Spills at the Sea Surface in the Southeastern Baltic Sea According to Satellite Sar Data

Abstract Statistical analysis of the spatial distribution of oil spills detected using Synthetic Aperture Radars (SAR) in 2004-2015 at the sea surface in the Southeastern Baltic Sea was conducted. Number, area, and shape of oil pollution division between Exclusive Economic Zones (EEZ) of Poland, Russia, and Lithuania were estimated for the first time. The most polluted area of the Southeastern Baltic Sea is Russian EEZ, where 55% of the total amount of detected oil spills, and 52% of the total area of oil pollution were located. The average area of an oil spill detected within Territorial Sea (TS) and EEZ was estimated. Tail-shaped spills associated with oil discharge from the moving vessels are prevailed, and their amount and area within TS and EEZ were estimated.

Monitoring of long-term land subsidence from 2003 to 2017 in coastal area of Semarang, Indonesia by SBAS DInSAR analyses using Envisat-ASAR, ALOS-PALSAR, and Sentinel-1A SAR data

Land subsidence is a critical issue that large cities located in coastal areas, such as Semarang, Indonesia, must address. The monitoring of land subsidence is vital for predicting and mitigating the disasters that such subsidence may cause. Therefore, an economical and effective monitoring method, which can continuously provide accurate measurements over extensive areas, is highly required. Differential Interferometry Synthetic Aperture Radar (DInSAR) has the potential to be a powerful technique that can meet the above demands. Actually, DInSAR has been applied to monitor the subsidence in Semarang, but it was for a limited period before 2012.In order to clarify the transition of the long-term subsidence behavior in Semarang, the Small Baseline Subset (SBAS) method, which is one type of time-series DInSAR, is employed in this research. The sets of data of Envisat-ASAR (2003–2007), ALOS-PALSAR (2007–2011), and Sentinel-1A (2015–2017) are employed for the analyses. Then, the validity of the SBAS results is discussed from the viewpoints of both spatial distribution and temporal transition using GPS displacement measurement results and the geological conditions of the ground.On the other hand, as the lifespan of SAR satellites is commonly designed to be around 5–7 years, an appropriate method, which can connect the subsidence provided independently by the unlinked time-series data sets of the three different SAR satellite data, is required. This study uses the Hyperbolic Method (HM) to connect the above unlinked SBAS results. The HM is often used to fit the monitored subsidence in practice as a geotechnical engineering tool. Using this method, 14 years of the temporal behavior of the subsidence in Semarang is evaluated.It is found that the transition of the subsidence is different depending on the location, and that the subsidence rate is still increasing in the north and northeast parts of the coastal area. This study shows that SBAS DInSAR can be a useful tool for long-term continuous subsidence monitoring.

ESTIMATION OF BIOPHYSICAL PARAMETERS OF WHEAT CROP THROUGH MODIFIED WATER CLOUD MODEL USING SATELLITE DATA

Abstract. A modified water cloud model (WCM) was used to estimate the biophysical parameters of wheat crop using Sentinel-1A and Landsat-8 satellite images. The approach of combining the potential of SAR and optical data provided a new technique for the estimation of biophysical parameters of wheat crop. The biophysical parameters estimation was done using non-linear least squares optimization technique by minimizing the cost function between the backscattering coefficients (σ0) computed from the Sentinel-1A image and simulated by the modified WCM followed by look up table algorithm(LUT). The modified WCM integrates the full account of backscattering response on vegetation and bare soil by adding vegetation fraction. The modified WCM was found more sensitive than the original WCM because of incorporation of vegetation fraction (fveg) derived from the Landsat-8 satellite data. The estimated values of leaf area index (LAI) by modified WCM at VV polarization shows good correlation (R2 = 83.08 % and RMSE = 0.502 m2/m2) with the observed values. Whereas, leaf water area index (LWAI) shows comparatively poor correspondence (R2 = 76 % and RMSE = 0.560 m2/m2) with the observed data in comparison to LAI estimation at VV polarization. The performance indices show that the modified WCM was found more accurate for the estimation of wheat crop parameters during the whole growth season in Varanasi district, India. Thus, the modified WCM shows significant potential for the accurate estimation of LAI and LWAI of wheat crop on incorporating both SAR and optical satellite data.

SHORT PERIOD CHANGE DETECTION OF SPERCHIOS LOWER DELTA AREA USING SPACE RADAR IMAGES

Being highly dynamic by nature, due to their changing hydrological regime and to the encroachment of urbanization, industrialization and changing patterns in agriculture, reliable and timely information of coastal areas is a prerequisite for their effective management. The aim of this paper is to assess the use of ERS-2 SAR satellite data to detect short period changes in the case of the R. Sperchios coastal area that is located at the eastern part of the Maliakos Gulf (near the middle of the east coast of the Greek mainland). A Landsat 7 (ΕΤΜ+) image served as a reference for the interpretation of the ERS images. In order to highlight and detect the changes occurred in the study area two methods were applied. The first method is based on the creation of a Temporal Differentiate Image, consisted of the three ERS-2 images (Figure 1). The second method concerns the implementation of Principal Component Transform (PCT) on the three multitemporal scenes. The final images derived from the two different methods were compared and evaluated. Both methods didn't show any significant change along the coastline. PCT method illustrates more clearly the seasonal changes of crops in the lower delta area. Eventually, radar technology gave the opportunity to discriminate shallow areas, which does not appear in satellite optical data. Concurrently, the effect of wind direction was investigated.

Long-Term Peatland Condition Assessment via Surface Motion Monitoring Using the ISBAS DInSAR Technique over the Flow Country, Scotland

Satellite Earth Observation (EO) is often used as a cost-effective method to report on the condition of remote and inaccessible peatland areas. Current EO techniques are primarily limited to reporting on the vegetation classes and properties of the immediate peat surface using optical data, which can be used to infer peatland condition. Another useful indicator of peatland condition is that of surface motion, which has the potential to report on mass accumulation and loss of peat. Interferometic SAR (InSAR) techniques can provide this using data from space. However, the most common InSAR techniques for information extraction, such as Persistent Scatterers’ Interferometry (PSI), have seen limited application over peat as they are primarily tuned to work in areas of high coherence (i.e., on hard, non-vegetated surfaces only). A new InSAR technique, called the Intermittent Small BAseline Subset (ISBAS) method, has been recently developed to provide measurements over vegetated areas from SAR data acquired by satellite sensors. This paper examines the feasibility of the ISBAS technique for monitoring long-term surface motion over peatland areas of the Flow Country, in the northeast of Scotland. In particular, the surface motions estimated are compared with ground data over a small forested area (namely the Bad a Cheo forest Reserve). Two sets of satellite SAR data are used: ERS C-band images, covering the period 1992–2000, and Sentinel-1 C-band images, covering the period 2015–2016. We show that the ISBAS measurements are able to identify surface motion over peatland areas, where subsidence is a consequence of known land cover/land use. In particular, the ISBAS products agree with the trend of surface motion, but there are uncertainties with their magnitude and direction (vertical). It is concluded that there is a potential for the ISBAS method to be able to report on trends in subsidence and uplift over peatland areas, and this paper suggests avenues for further investigation, but this requires a well-resourced validation campaign.

PRELIMINARY GAOFEN-3 INSAR DEM ACCURACY ANALYSIS

Abstract. GF-3 satellite, the first C band and full-polarization SAR satellite of China with spatial resolution of 1 m, was successfully launched in August 2016. We analyze the error sources of GF-3 satellite in this paper, and provide the interferometric calibration model based on range function, Doppler shift equation and interferometric phase function, and interferometric parameters calibrated using the three-dimensional coordinates of ground control points. Then, we conduct the experimental two pairs of images in fine stripmap I mode covering Songshan of Henan Province and Tangshan of Hebei Province, respectively. The DEM data are assessed using SRTM DEM, ICESat-GLAS points, and ground control points database obtained using ZY-3 satellite to validate the accuracy of DEM elevation. The experimental results show that the accuracy of DEM extracted from GF-3 satellite SAR data can meet the requirements of topographic mapping in mountain and alpine regions at the scale of 1 : 50000 in China. Besides, it proves that GF-3 satellite has the potential of interferometry.

Using SAR satellite data time series for regional glacier mapping

Abstract. With dense SAR satellite data time series it is possible to map surface and subsurface glacier properties that vary in time. On Sentinel-1A and RADARSAT-2 backscatter time series images over mainland Norway and Svalbard, we outline how to map glaciers using descriptive methods. We present five application scenarios. The first shows potential for tracking transient snow lines with SAR backscatter time series and correlates with both optical satellite images (Sentinel-2A and Landsat 8) and equilibrium line altitudes derived from in situ surface mass balance data. In the second application scenario, time series representation of glacier facies corresponding to SAR glacier zones shows potential for a more accurate delineation of the zones and how they change in time. The third application scenario investigates the firn evolution using dense SAR backscatter time series together with a coupled energy balance and multilayer firn model. We find strong correlation between backscatter signals with both the modeled firn air content and modeled wetness in the firn. In the fourth application scenario, we highlight how winter rain events can be detected in SAR time series, revealing important information about the area extent of internal accumulation. In the last application scenario, averaged summer SAR images were found to have potential in assisting the process of mapping glaciers outlines, especially in the presence of seasonal snow. Altogether we present examples of how to map glaciers and to further understand glaciological processes using the existing and future massive amount of multi-sensor time series data.

Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico

BackgroundInformation on the spatial distribution of aboveground biomass (AGB) over large areas is needed for understanding and managing processes involved in the carbon cycle and supporting international policies for climate change mitigation and adaption. Furthermore, these products provide important baseline data for the development of sustainable management strategies to local stakeholders. The use of remote sensing data can provide spatially explicit information of AGB from local to global scales. In this study, we mapped national Mexican forest AGB using satellite remote sensing data and a machine learning approach. We modelled AGB using two scenarios: (1) extensive national forest inventory (NFI), and (2) airborne Light Detection and Ranging (LiDAR) as reference data. Finally, we propagated uncertainties from field measurements to LiDAR-derived AGB and to the national wall-to-wall forest AGB map.ResultsThe estimated AGB maps (NFI- and LiDAR-calibrated) showed similar goodness-of-fit statistics (R2, Root Mean Square Error (RMSE)) at three different scales compared to the independent validation data set. We observed different spatial patterns of AGB in tropical dense forests, where no or limited number of NFI data were available, with higher AGB values in the LiDAR-calibrated map. We estimated much higher uncertainties in the AGB maps based on two-stage up-scaling method (i.e., from field measurements to LiDAR and from LiDAR-based estimates to satellite imagery) compared to the traditional field to satellite up-scaling. By removing LiDAR-based AGB pixels with high uncertainties, it was possible to estimate national forest AGB with similar uncertainties as calibrated with NFI data only.ConclusionsSince LiDAR data can be acquired much faster and for much larger areas compared to field inventory data, LiDAR is attractive for repetitive large scale AGB mapping. In this study, we showed that two-stage up-scaling methods for AGB estimation over large areas need to be analyzed and validated with great care. The uncertainties in the LiDAR-estimated AGB propagate further in the wall-to-wall map and can be up to 150%. Thus, when a two-stage up-scaling method is applied, it is crucial to characterize the uncertainties at all stages in order to generate robust results. Considering the findings mentioned above LiDAR can be used as an extension to NFI for example for areas that are difficult or not possible to access.

Multi-sensor forest vegetation height mapping methods for Tanzania

ABSTRACTThis paper proposes a new method for mapping of forest cover in Tanzania, in the form of yearly estimates of average vegetation height from time-series of Landsat and ALOS PALSAR satellite images. By using airborne laser scanning data and Landsat-8 data from 2014, a regression between average vegetation height and the specific leaf area vegetation index is established. By using all available Landsat acquisitions of the same area within 1 year, and producing a yearly estimate of vegetation height, the estimation error variance is reduced. The variance is further reduced by Kalman filtering the sequence of yearly estimates. A multi-sensor version of the method comprises application of the radar backscatter when L-band SAR data is available. To conclude, we have demonstrated that estimation of mean vegetation height is possible from dense time series of optical and SAR satellite data. Change detection was able to detect areas with total loss of biomass.

Improved Risk Assessment of Tsunami Inundation Based on Geospatial and Local Knowledge for Sustainable Development

Tsunami events have extensive implications for sustainable development, due to the social, economic, and environmental impacts they can cause. Take into account the competence of geospatial and local knowledge; these sources are integrated to assess tsunami risk assessment by utilizing satellite imageries. The study area of Talcahuano experienced severe damages due to the 2010 Chile earthquake and tsunami. The geospatial information of the damaged areas was identified by processing the surface reflectance values between pre- and post-disaster images of the satellite optical and SAR data. The DEM data was also employed to delineate areas according to the inundation height data. Local information of tsunami inundation was generated through a DIG participatory mapping. Both types of inundation maps showed good agreement with the results of field surveys. Furthermore, a map of the potential tsunamiinundation zone was produced to aid integrated risk assessments in the area. The map showed areas with high, medium, and low inundation level adopted from the satellite images and the DIG map. Keywords: Tsunami Inundati Satellite Imageries, Optical and SAR Sensor, DIG, Disaster Risk Reduction

Monitoring of Crustal Movements Using Satellite SAR Data and Observing Volcanic Terrain Using Airborne SAR

In 2018, A large-scale earthquake occurred directly underneath Iburi area in Hokkaido. Furthermore, several volcanos erupted in Japan with volcanic terrain changed. In this report, we introduce disaster response in 2018 by Geospatial Information Authority of Japan (GSI) using satellite SAR data for detecting crustal movements and airborne SAR data for observing volcanic terrain changes. GSI released these results of analysis via website. Also GSI provided them to various institutes and local governments for their disaster response and recovery operations.

Estimation of Sunflower Yield Using a Simplified Agrometeorological Model Controlled by Optical and SAR Satellite Data

This paper aims at estimating the yield of sunflower at field scale by testing different ways to control an agrometeorological model, using the leaf area index (LAI) and/or the dry mass (DM) of the crop. The LAI is derived from multitemporal satellite images acquired in the visible, near-infrared, and microwave wavelengths by five sensors (Formosat-2, Spot-4/5, TerraSAR-X, Radarsat-2), over a region located in southwestern France, while the DM is measured in situ . Results show that the temporal dynamics of LAI and DM can be accurately simulated over the five studied working farms ( R ²DM > 0.85 and R ² LAI > 0.94 with a relative root-mean-square error (RMSE) R ² > 0.85) over 140 ha, with low errors ranging from 0.20 to 0.54 t⋅ha−1 by using both the LAI derived from microwave and optical data ( ${\rm{LAI}}_{{\rm{MTVI2}}}$ and ${\rm{LAI}}_{{\rm{\sigma ^\circ C-VV}}}$ ), in order to reduce the overestimations related to the excessive development of the LAI during the first phenological stages, or to the presence of dew on the vegetation, respectively, affecting the LAI derived from the MTVI2 or σ°C-VV.

Contributions of Actual and Simulated Satellite SAR Data for Substrate Type Differentiation and Shoreline Mapping in the Canadian Arctic

Detailed information on the land cover types present and the horizontal position of the land–water interface is needed for sensitive coastal ecosystems throughout the Arctic, both to establish baselines against which the impacts of climate change can be assessed and to inform response operations in the event of environmental emergencies such as oil spills. Previous work has demonstrated potential for accurate classification via fusion of optical and SAR data, though what contribution either makes to model accuracy is not well established, nor is it clear what shorelines can be classified using optical or SAR data alone. In this research, we evaluate the relative value of quad pol RADARSAT-2 and Landsat 5 data for shoreline mapping by individually excluding both datasets from Random Forest models used to classify images acquired over Nunavut, Canada. In anticipation of the RADARSAT Constellation Mission (RCM), we also simulate and evaluate dual and compact polarimetric imagery for shoreline mapping. Results show that SAR data is needed for accurate discrimination of substrates as user’s and producer’s accuracies were 5–24% higher for models constructed with quad pol RADARSAT-2 and DEM data than models constructed with Landsat 5 and DEM data. Models based on simulated RCM and DEM data achieved significantly lower overall accuracies (71–77%) than models based on quad pol RADARSAT-2 and DEM data (80%), with Wetland and Tundra being most adversely affected. When classified together with Landsat 5 and DEM data, however, model accuracy was less affected by the SAR data type, with multiple polarizations and modes achieving independent overall accuracies within a range acceptable for operational mapping, at 89–91%. RCM is expected to contribute positively to ongoing efforts to monitor change and improve emergency preparedness throughout the Arctic.