RADARSAT-2 Research Articles

The Optimized Small Incidence Angle Setting of a Composite Bragg Scattering Model and its Application to Sea Surface Wind Speed Retrieval

The normalized radar cross section (NRCS) of microwave backscattering from a rough sea surface can be described by a composite Bragg scattering (CBS) model, which is a combination of a two-scale backscatter model and a geometric optics model. For small local radar incidence angles that are smaller than a given angle setting, the two-scale backscattering mechanism of the sea surface is replaced by a geometric optics solution for specular reflection. In this study, an optimized setting of 18 degrees for the small incidence angle is determined by comparing the NRCSs between 52 RADARSAT-2 (R2) synthetic aperture radar (SAR) images and the CBS model with simultaneous sea surface wind derived by meteorological mooring buoys located in the northern South China Sea. By using the CBS model with the optimized incidence angle setting of 18° and the nearly simultaneous wind directions of the Advanced Scatterometer (ASCAT) as the input, sea surface wind speeds are retrieved from 46 R2 SAR images at C-band and from 2 COSMO-SkyMed (CSK) SAR images at X-band for the application study. The average root mean square error of the C-band R2 SAR retrieval results validated against nearly simultaneous measurements of ASCAT is 1.5 m/s, while the same value is 1.6 m/s for the X-band CSK SAR image. The results also indicate that the optimized small incidence angle setting of the CBS model found in this study is relatively reliable under sea surface wind speed conditions less than 14.0 m/s.

Combining Machine Learning and Compact Polarimetry for Estimating Soil Moisture from C-Band SAR Data

This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture content (SMC) from synthetic aperture radar (SAR) acquisitions at C-band. The study was conducted on two agricultural areas in Canada, for which a series of RADARSAT-2 (RS2) images were available along with direct measurements of SMC from in situ stations. The analysis confirmed the sensitivity of RS2 backscattering (σ°) to SMC. The comparison of SMC with the compact polarimetry (CP) parameters, computed from the RS2 acquisitions by the CP data simulator, pointed out that some CP parameters had a sensitivity to SMC equal or better than σ°, with correlation coefficients up to R ≃ 0.4. Based on these results, the potential of machine learning (ML) for SMC retrieval was exploited by implementing and testing on the available data an artificial neural network (ANN) algorithm. The algorithm was implemented using several combinations of σ° and CP parameters. Validation results of the algorithm with in situ observations confirmed the promising capabilities of the ML techniques for SMC monitoring. Furthermore, results pointed out the potential of CP in improving the SMC retrieval accuracy, especially when used in combination with linearly polarized σ°. Depending on the considered input combination, the ANN algorithm was able to estimate SMC with Root Mean Square Error (RMSE) between 3% and 7% of SMC and R between 0.7 and 0.9.

A Geophysical Model Function for Wind Speed Retrieval From C-Band HH-Polarized Synthetic Aperture Radar

Synthetic aperture radar (SAR) imagery is routinely acquired at HH-polarization in high-latitude areas for measuring surface wind over the ocean. However, in the contrary of VV-polarization, there is no HH-polarization geophysical model function (GMF) exists to directly retrieve wind speed from SAR images. In general, HH-polarized normalized radar cross section (NRCS) is thus converted into VV-polarization and then conventional CMOD functions are used with auxiliary wind direction information for wind speed retrieval. In this letter, we propose a new GMF for SAR ocean surface wind speed retrieval, called CMODH, which relates the C-band NRCS acquired at HH-polarization over the ocean, to the 10-m height wind speed, incident angle, and relative wind direction. We first use more than 220 000 ENVISAT ASAR radar backscatter measurements collocated with ASCAT winds to derive the CMODH coefficients. Subsequently, 1459 RADARSAT-2 (RS-2) and 428 Sentinel-1A/B (Sl-1A/B) HH-polarized SAR acquisitions under different wind speeds are matched to in situ buoy observations to validate CMODH. The statistical comparisons between SAR-observed and simulated NRCS show a bias of −0.07 dB and a root-mean-square error of 1.62 dB for RS-2, and −0.01 dB and 2.48 dB for S1-1A/B. These results suggest that the proposed CMODH has the potential to directly retrieve ocean surface wind speeds using C-band SAR images acquired at HH-polarization, with no need for NRCS transformation by using various empirical and theoretical polarization ratio models.

Synergistic RADARSAT-2 and Sentinel-1 SAR Images for Ocean Feature Analysis

Using a case study approach, the utility of synergistic RADARSAT-2 (R2) and Sentinel-1 (S1) synthetic aperture radar (SAR) imagery is demonstrated for ocean feature signature analysis in the vicinity of the Gulf Stream. The R2 and S1 images considered are either spatially adjacent or spatially overlapping, and were quasi-simultaneously collected (i.e., within minutes of each other). Spatially adjacent R2 and S1 imagery allows ocean feature signatures to be delineated over large spatial areas, while spatially overlapping R2 and S1 imagery collected within short time intervals provides independent ‘looks’ at the same ocean features. This permits determination of the surface displacement of features, potentially leading to improved classification of the origin of ocean feature signatures (quasi-stationary features are likely related to sea surface temperature fronts, while mobile features are likely related to atmospheric conditions). Further, we demonstrate how the use of S1 Level-2 products (i.e. radial velocity datasets) can be leveraged as contextual data to improve the interpretation and classification of ocean feature signatures extracted from R2 imagery. Despite the straight-forward approach taken here, this work demonstrates that there are practical, real-world applications that would benefit from exploiting these on-going imaging opportunities in operational environments.

The Utility of Sentinel-1 Data for Ocean Surface Feature Analysis in the Vicinity of the Gulf Stream

ABSTRACTEuropean Space Agency Sentinel-1 (S1) synthetic aperture radar (SAR) datasets are used to assess their suitability for ocean surface feature extraction in the vicinity of the Gulf Stream. A SAR ocean feature detection tool originally developed to extract and classify brightness fronts in RADARSAT-2 (R2) SAR imagery has been updated to extract and classify brightness fronts from S1 ground range-detected products. Results indicate that the features extracted from S1 datasets are largely consistent with those derived from R2. However, more features are extracted from S1 data and, in limited examples, there are differences in how features are classified. In addition, S1 radial velocity (RVL) products were compared with simulated RVL products created from modeled ocean current data. This comparison shows that the orientation and magnitude of the Gulf Stream in S1 RVL products is generally consistent with modeled results, but does indicate that some regions of the Gulf Stream (meanders and ocean eddies) are likely to present challenges for automatic extraction algorithms. Taken together, this research shows that S1 data have utility for ocean surface feature extraction and provide an additional dataset that can be exploited to expand ocean feature analysis over Canadian waters.

Estimation of Melt Pond Fractions on First Year Sea Ice Using Compact Polarization SAR

AbstractMelt ponds are a common feature on Arctic sea ice. They are linked to the sea ice surface albedo and transmittance of energy to the ocean from the atmosphere and thus constitute an important process to parameterize in Arctic climate models and simulations. This paper presents a first attempt to retrieve the melt pond fraction from hybrid‐polarized compact polarization (CP) SAR imagery, which has wider swath and shorter revisit time than the quad‐polarization systems, e.g., from RADARSAT‐2 (RS‐2). The co‐polarization (co‐pol) ratio has been verified to provide estimates of melt pond fractions. However, it is a challenge to link CP parameters and the co‐pol ratio. The theoretical possibility is presented, for making this linkage with the CP parameter C22/C11 (the ratio between the elements of the coherence matrix of CP SAR) for melt pond detection and monitoring with the tilted‐Bragg scattering model for the ocean surface. The empirical transformed formulation, denoted as the “compact polarization and quad‐pol” (“CPQP”) model, is proposed, based on 2062 RS‐2 quad‐pol SAR images, collocated with in situ measurements. We compared the retrieved melt pond fraction with CP parameters simulated from quad‐pol SAR data with results retrieved from the co‐pol ratio from quad‐pol SAR observations acquired during the Arctic‐Ice (Arctic‐Ice Covered Ecosystem in a Rapidly Changing Environment) field project. The results are shown to be comparable for observed melt pond measurements in spatial and temporal distributions. Thus, the utility of CP mode SAR for melt pond fraction estimation on first year level ice is presented.

Bridging the gap between cyclone wind and wave by C‐band SAR measurements

AbstractActive microwave remote sensing of hurricane‐strength wind is a challenging task due to the saturation of col‐polarization backscattering signal under such condition. Here we take advantage of the fact that wind sea wave growth does not saturate at high wind and the intrinsic relationship among wind‐wave triplets (sea surface wind speed, significant wave height, and peak wave period) within a tropical storm to derive the wind speed. Three Sentinel‐1 (S‐1) and nine RADARSAT‐2 (R‐2) C‐band synthetic aperture radar (SAR) images acquired between 20 and 40 m/s winds are collected in this study. The S‐1 and R‐2 SAR‐derived winds are compared with those measured by coincident National Oceanic and Atmospheric Administration Stepped‐Frequency Microwave Radiometer (SFMR) and simulated by Symmetric Hurricane Estimates for Wind (SHEW) model. Validations against SFMR winds for S‐1 show Root‐Mean‐Square Error (RMSE) of 1.7 m/s with a 0.2 m/s bias at the left side of cyclone centers and RMSE of 2.9 m/s RMSE with a 0.56 bias at the backside of cyclone centers. R‐2 SAR‐derived winds against SHEW model results show a RMSE of 2.4 m/s with a 0.3 m/s bias and 2.6 m/s with a 0.35 m/s bias at the right side and the left side of cyclone centers, while the RMSE is 3.9 m/s with a 0.1 m/s bias at the backside of cyclone centers. The wave‐information‐based wind retrieval method works well at the left side and right side, but less accurately at the backside of a tropical storm when wind wave and swell are mixed.

Accurate crop-type classification using multi-temporal optical and multi-polarization SAR data in an object-based image analysis framework

ABSTRACTAccurate crop-type classification is a challenging task due, primarily, to the high within-class spectral variations of individual crops during the growing season (phenological development) and, second, to the high between-class spectral similarity of crop types. Utilizing within-season multi-temporal optical and multi-polarization synthetic aperture radar (SAR) data, this study introduces a combined object- and pixel-based image classification methodology for accurate crop-type classification. Particularly, the study investigates the improvement of crop-type classification by using the least number of multi-temporal RapidEye (RE) images and multi-polarization Radarsat-2 (RS-2) data utilized in an object- and pixel-based image analysis framework. The method was tested on a study area in Manitoba, Canada, using three different classifiers including the standard Maximum Likelihood (ML), Decision Tree (DT), and Random Forest (RF) classifiers. Using only two RE images of July and August, the proposed method results in overall accuracies (OAs) of about 95%, 78%, and 93% for the ML, DT, and RF classifiers, respectively. Moreover, the use of only two quad-pol images of RS-2 of June and September resulted in OAs of 92%, 75%, and 90% for the ML, DT, and RF classifiers, respectively. The best classification results were achieved by the synergistic use of two RE and two RS-2 images. In this case, the overall classification accuracies were 97% for both ML and RF classifiers. In addition, the average producer’s accuracies of 95% and 96% were achieved by the ML and RF classifiers, respectively, whereas the average user accuracy was 94% for both classifiers. The results indicated promising potentials for rapid and cost-effective local-scale crop-type classification using a limited number of high-resolution optical and multi-polarization SAR images. Very accurate classification results can be considered as a replacement for sampling the agricultural fields at the local scale. The result of this very accurate classification at discrete locations (approximately 25 × 25 km frames) can be applied in a separate procedure to increase the accuracy of crop area estimation at the regional to provincial scale by linking these local very accurate spatially discrete results to national wall-to-wall continuous crop classification maps.

GA-SVM Algorithm for Improving Land-Cover Classification Using SAR and Optical Remote Sensing Data

Multisource remote sensing data have been widely used to improve land-cover classifications. The combination of synthetic aperture radar (SAR) and optical imagery can detect different land-cover types, and the use of genetic algorithms (GAs) and support vector machines (SVMs) can lead to improved classifications. Moreover, SVM kernel parameters and feature selection affect the classification accuracy. Thus, a GA was implemented for feature selection and parameter optimization. In this letter, a GA-SVM algorithm was proposed as a method of classifying multifrequency RADARSAT-2 (RS2) SAR images and Thaichote (THEOS) multispectral images. The results of the GA-SVM algorithm were compared with those of the grid search algorithm, a traditional method of parameter searching. The results showed that the GA-SVM algorithm outperformed the grid search approach and provided higher classification accuracy using fewer input features. The images obtained by fusing RS2 data and THEOS data provided high classification accuracy at over 95%. The results showed improved classification accuracy and demonstrated the advantages of using the GA-SVM algorithm, which provided the best accuracy using fewer features.

Nearest neighbour analysis applied to synthetic aperture radar images for the description of urban land cover and land use

ABSTRACTThis article presents an analysis method for the creation of an image variable that represents built-up land cover and land use within the urban fabric. The method is inspired by the nearest neighbour analysis and the data are from the synthetic aperture radar systems COSMO-SkyMed (CSK) and Radarsat-2 (RS2). Point features were identified from the extreme high backscattering values for each image and the spatial pattern extracted to represent the proportion of built-up land cover as clustered, random, or dispersed. The coefficient of association between the continuous nearest neighbour ratio image and the land-cover percentage cover in four classes are −0.9 and −0.8 with the CSK and the RS2 images, respectively. Considering a two-class land-cover scheme, the coefficient of association between variables approaches −0.9 for both images. Clustered features highlight individual buildings that are mixed in various neighbouring land-cover and land-use types. Residential land use is particularly well outlined using the CSK image, while large institutional, commercial, and light industry buildings are enhanced through the RS2 cross-polarization nearest neighbour ratio images.

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Abstract. Synthetic Aperture Radar (SAR) data from RADARSAT-2 (RS2) in dual-polarization mode provide additional information for discriminating sea ice and open water compared to single-polarization data. We have developed an automatic algorithm based on dual-polarized RS2 SAR images to distinguish open water (rough and calm) and sea ice. Several technical issues inherent in RS2 data were solved in the pre-processing stage, including thermal noise reduction in HV polarization and correction of angular backscatter dependency in HH polarization. Texture features were explored and used in addition to supervised image classification based on the support vector machines (SVM) approach. The study was conducted in the ice-covered area between Greenland and Franz Josef Land. The algorithm has been trained using 24 RS2 scenes acquired in winter months in 2011 and 2012, and the results were validated against manually derived ice charts of the Norwegian Meteorological Institute. The algorithm was applied on a total of 2705 RS2 scenes obtained from 2013 to 2015, and the validation results showed that the average classification accuracy was 91 ± 4 %.

An Inter-Comparison of Techniques for Determining Velocities of Maritime Arctic Glaciers, Svalbard, Using Radarsat-2 Wide Fine Mode Data

Glacier dynamics play an important role in the mass balance of many glaciers, ice caps and ice sheets. In this study we exploit Radarsat-2 (RS-2) Wide Fine (WF) data to determine the surface speed of Svalbard glaciers in the winters of 2012/2013 and 2013/2014 using Synthetic Aperture RADAR (SAR) offset and speckle tracking. The RS-2 WF mode combines the advantages of the large spatial coverage of the Wide mode (150 × 150 km) and the high pixel resolution (9 m) of the Fine mode and thus has a major potential for glacier velocity monitoring from space through offset and speckle tracking. Faster flowing glaciers (1.95 m·d−1–2.55 m·d−1) that are studied in detail are Nathorstbreen, Kronebreen, Kongsbreen and Monacobreen. Using our Radarsat-2 WF dataset, we compare the performance of two SAR tracking algorithms, namely the GAMMA Remote Sensing Software and a custom written MATLAB script (GRAY method) that has primarily been used in the Canadian Arctic. Both algorithms provide comparable results, especially for the faster flowing glaciers and the termini of slower tidewater glaciers. A comparison of the WF data to RS-2 Ultrafine and Wide mode data reveals the superiority of RS-2 WF data over the Wide mode data.

Estimation of Snow Cover Area, Snow Physical Properties and Glacier Classification in Parts of Western Himalayas Using C-Band SAR Data

Snow physical properties, snow cover and glacier facies are important parameters which are used to quantify snowpack characteristics, glacier mass balance and seasonal snow and glacier melt. This study has been done using C-band synthetic aperture radar (SAR) data of Indian radar imaging satellite, radar imaging satellite-1 (RISAT)-1, to estimate the seasonal snow cover and retrieve snow physical properties (snow wetness and snow density), and glacier radar zones or facies classification in parts of North West Himalaya (NWH), India. Additional SAR data used are of Radarsat-2 (RS-2) satellite, which was used for glacier facies classification of Smudra Tapu glacier in Himachal Pradesh. RISAT-1 based snow cover area (SCA) mapping, snow wetness and snow density retrieval and glacier facies classification have been done for the first time in NWH region. SAR-based inversion models were used for finding out wet and dry snow dielectric constant, dry and wet SCA, snow wetness and snow density. RISAT-1 medium resolution scan-SAR mode (MRS) in HV polarization was used for first time in NWH for deriving time series of SCA maps in Beas and Bhagirathi river basins for years 2013–2014. The SAR-based inversion models were implemented separately for RISAT-1 quad pol. FRS2, for wet snow and dry snow permittivity retrieval. Masks for layover and shadow were considered in estimating final snow parameters. The overall accuracy in terms of R2 value comes out to be 0.74 for snow wetness and 0.72 for snow density based on the limited ground truth data for subset area of Manali sub-basin of Beas River up to Manali for winter of 2014. Accuracy for SCA was estimated to be 95 % when compared with optical remote sensing based SCA maps with error of ±10 %. The time series data of RISAT-1 MRS and hybrid data in RH/RV mode based decompositions were also used for glacier radar zones classification for Gangotri and Samudra Tapu glaciers. The various glaciers radar zones or facies such as debris covered glacier ice, clean or bare glacier ice radar zone, percolation/refreeze radar zone and wet snow, ice wall etc., were identified. The accuracy of classified maps was estimated using ground truth data collected during 2013 and 2014 glacier field work to Samudra Tapu and Gangotri glaciers and overall accuracy was found to be in range of 82–90 %. This information of various glacier radar zones can be utilized in marking firn line of glaciers, which can be helpful for glacier mass balance studies.

Study of the polarimetric characteristics of mud flats in an intertidal zone using C- and X-band spaceborne SAR data

Abstract The polarimetric characteristics of a mud flat area in the intertidal zone near Jiangsu, China, are analyzed under different environmental conditions using X-band and C-band spaceborne synthetic aperture radar (SAR) data from the TerraSAR-X (TSX) and Radarsat-2 (R2) satellites. The dominant scattering mechanisms of different objects, including mud flats and aquaculture facilities in the intertidal zone, are studied based on the polarimetric target decomposition of fully polarimetric R2 data. The polarimetric characteristics of different objects in the intertidal zone are quantitatively compared using the depolarization parameters derived from the R2 data, which indicate that tides have different effects on the dominant scattering mechanisms of the mud flats and aquaculture farms. Furthermore, comparisons of the C-band and X-band polarimetric characteristics of the mud flats and aquaculture farms in the intertidal zone are analyzed by combining the TSX and R2 data. The results show that surface scattering is the dominant mechanism of the mud flats, whereas different dominant mechanisms occur in the aquaculture areas present during different tides. Interestingly, strikingly similar polarimetric characteristics of mud flats and aquaculture in X-band and C-band SAR are observed, and these novel results were achieved through a depolarization analysis based on the TSX and R2 data in synergy.

Compact Polarimetry in Support of Lake Ice Breakup Monitoring: Anticipating the RADARSAT Constellation Mission

. Canada's RADARSAT Constellation Mission (RCM) will have the capacity to acquire C-band compact polarimetry (CP) data in all imaging modes over swaths up to 500 km wide. Our study aimed to assess and develop the utility of RCM CP data for the purpose of lake ice breakup monitoring. The breaking ice and open water information content of RADARSAT-2 (R2) polarimetric data and simulated RCM CP data was compared. Despite relative losses in terms of polarization diversity and radiometric sensitivity, RCM-type CP data were concluded to make a good source of information in support lake ice breakup monitoring. For that reason, a CP-based approach, called CP_LakeIceBC, to classify breaking ice and open water for the purpose of lake ice breakup monitoring was developed. CP_LakeIceBC is driven by incidence angle information, uses five CP backscatter variables (RH, RR, RV/RH, RR/RL, and conformity), and is compatible with high- and medium-resolution RCM-type CP products. Application of CP_LakeIceBC to representative simulated products yielded classification accuracies ranging from 73.2 to 99.1% and 60.8 to 99.5% for breaking ice and open water, respectively.Résumé. La mission de la Constellation RADARSAT (MCR) du Canada aura la capacité d’acquérir des données en polarimétrie compacte (CP) en bande C dans tous les modes d’imagerie, sur des fauchées allant jusqu’à 500 km de large. Notre étude visait à évaluer et développer l’utilité des données en CP de la MCR à des fins de surveillance des débâcles lacustres. Le contenu de l’information sur l’eau libre et la rupture des glaces a été comparé à partir de données polarimétriques de RADARSAT-2 (R2) et des données en CP de la MCR. Malgré les pertes relatives en termes de la diversité de la polarisation et de la sensibilité radiométrique, il a été conclu que les données en CP de type MCR sont une bonne source d’information de soutien pour la surveillance des débâcles lacustres. Pour cette raison, une approche fondée sur la CP, nommée CP_LakeIceBC, a été développée afin de classifier la rupture de la glace et l’eau libre à des fins de surveillance de la débâcle lacustre. CP_LakeIceBC est déterminée par l’information sur l’angle d’incidence, elle utilise cinq variables CP de rétrodiffusion (RH, RR, RV/RH, RR/RL et conformité), et elle est compatible avec les produits de CP de type MCR à haute et à moyenne résolution. L’application de CP_LakeIceBC aux produits simulés représentatifs a donné des précisions de classification pour la rupture de la glace et l’eau libre allant de 73,2 % à 99,1 % et de 60,8 % à 99,5 %, respectivement.

RCM Polarimetric SAR for Enhanced Ship Detection and Classification

. The RADARSAT Constellation Mission (RCM), which involves three small synthetic aperture radar (SAR) satellites flying in a constellation configuration, will be equipped with fully polarimetric (FP) capabilities in addition to single-polarization (HH, HV, VV), conventional (HH-HV, VV-VH, and HH-VV), and hybrid (i.e., compact) dual polarization. In this article, the added value of polarimetric SAR information for enhanced ship detection is demonstrated using polarimetric RADARSAT-2 (RS2) data collected over vessels (validated with Automatic Identification System (AIS) data) in the Strait of Georgia, near Vancouver, Canada. It is shown that the excursion (Δp) of the degree of polarization (DoP) provides a significant increase in ship–sea contrast in comparison with conventional (i.e., scalar) single-channel polarizations (HH, HV, VV) and compact. Δp, which is a measure of the variation of the DoP with transmit antenna polarization, performs better than compact intensities and degree of depolarization (DoD) promoted recently for ship detection. The unique potential of FP for ship classification is also demonstrated using polarimetric Convair-580 SAR data collected off Cape Race, Newfoundland, Canada. An efficient combination of successive wide-swath ScanSAR and FP, which exploits the RCM rapid-revisit capability at high latitudes, is considered for operational ship detection and classification.Résumé. La mission de la Constellation RADARSAT (MCR), qui est constituée de trois petits satellites SAR évoluant en configuration de constellation, sera équipée avec des capacités de polarisation en quadrature (FP) en plus de la polarisation simple (HH, HV, VV), de la polarisation classique (HH-HV, VV-VH, et HH-VV) et de la bipolarisation hybride (c.-à-d., compacte). Dans cet article, la valeur ajoutée des informations polarimétriques SAR pour améliorer la détection de navires est démontrée en utilisant des données polarimétriques RADARSAT-2 (RS2) recueillies sur des navires (validées avec le système d'identification automatique (AIS) de données) dans le détroit de Georgia, près de Vancouver (Canada). Il apparait que la différence (delta p) du degré de polarisation (DOP) fournit une augmentation significative du contraste entre le navire et l'eau par rapport à des polarisations simples classiques (p.ex., scalaire) (HH, HV, VV) et compactes. Le delta p, qui est une mesure de la variation de la polarisation DoP avec l'antenne d'émission, est plus performant que les intensités compactes et le degré de dépolarisation (DoD) promus récemment pour la détection de navires. Le potentiel unique de la FP pour la classification de navires est également démontré en utilisant les données polarimétriques Convair-580 SAR recueillies au large du cap Race à Terre-Neuve (Canada). Une combinaison efficace de la large fauchée ScanSAR consécutive à la FP, qui exploite la capacité de revisite rapide de la MCR dans les hautes latitudes, est considérée pour la détection et la classification opérationnelles de navires.

Cross‐polarization geophysical model function for C‐band radar backscattering from the ocean surface and wind speed retrieval

AbstractThe wind speed sensitivity of cross‐polarization (cross‐pol) radar backscattering cross section (VH) from the ocean surface increases toward high winds. The signal saturation problem of VH, if it exists, occurs at a much higher wind speed compared to the copolarization (copol: VV or HH) sea returns. These properties make VH a better choice over VV or HH for monitoring severe weather. Combined with high spatial resolution of the synthetic aperture radar (SAR), the development of hurricane wind retrieval using VH is advancing rapidly. This paper describes a cross‐pol C‐band radar backscattering geophysical model function (GMF) with incidence angle dependence for the full wind speed range in the available data sets (up to 56 m/s). The GMF is derived from RADARSAT‐2 (R2) dual‐polarization (dual‐pol) ScanSAR modes with 300 and 500 km swaths. The proposed GMF is compared to other published algorithms. The result shows that the simulated VH cross section and the retrieved wind speed with the proposed GMF is in better agreement with measurements. With careful treatment of noise, the VH‐retrieved wind speeds may extend to mild or moderate conditions. The higher fraction of non‐Bragg contribution in VH can be exploited for analysis of surface wave breaking.

Polarimetric Radarsat-2 wetland classification using the Touzi decomposition: case of the Lac Saint-Pierre Ramsar wetland

Wetlands play a key role in regional and global environments and are linked to climate change, water quality, and hydrological and carbon cycles. They also contribute to wildlife habitat and biodiversity and can act as indicators of overall environmental health. Unfortunately, wetlands continue to be under threat. There is an immediate need for improved mapping and monitoring of wetlands to better manage and protect these sensitive areas. Recently, the Touzi decomposition was introduced and proved very promising for wetland characterization using polarimetric airborne (Convair-580) SAR data. The purpose of this study is to assess the Touzi incoherent target-scattering decomposition (ICTD) for wetland classification using polarimetric Radarsat-2 (RS2) data collected over the RAMSAR wetland site in Lac Saint-Pierre, Canada. In particular, the sensitivity of the ICTD parameters to seasonal evolution of marsh and swamp scattering is discussed and demonstrated. The intent is to show that the dominant scattering type magnitude (αs1) and phase (Φs1), and the dominant (η1) and lowest scattering eigenvalues (η3), lead to an effective characterization of the various backscattering mechanisms of the wetland plant species. The ICTD parameters form the basis of a new hierarchical classification that is efficient for wetland classification. The use of multitemporal information obtained from multidate RS2 acquisitions between April and September allows an accurate wetland classification. The RS2 polarimetric classification is then compared with a supervised maximum-likelihood classification using a pair of Landsat-5 images.

Target Detection on the Ocean With the Relative Phase of Compact Polarimetry SAR

This paper discusses the potential for automatic ocean surveillance using compact linear polarization (CL-pol) synthetic aperture radar (SAR), with large area coverage. Here, the target is a wind farm in the North Sea. The relative phase, as derived from CL-pol SAR, is employed for detection of the wind turbines, apart from the wind turbines' wakes, based on fine-mode quad-polarization (quad-pol) RADARSAT-2 (RS-2) images. The relative phase of CL-pol measurements improves the contrast between the wind turbines and their wakes, because it has opposite signs for these two entities. Moreover, there is almost no variation in the relative phase with respect to wind speed or incidence angle. The results are verified by high-sea-state cases, up to 8.7-m significant wave height and 24.3-m/s wind speed, and also 641 quad-pol RS-2 SAR images collocated with 52 National Data Buoy Center buoys at different incidence angles and sea states. Thus, the relative phase of CL-pol SAR provides new light into the problem of operational autodetection of man-made targets, under high-sea-state conditions, over large areas.

Snow Cover Area Mapping Using Synthetic Aperture Radar in Manali Watershed of Beas River in the Northwest Himalayas

The current study has used Synthetic Aperture Radar (SAR) satellite data to estimate the Snow Cover Area (SCA) in Manali watershed of Beas River in Northwest Himalayas of Himachal Pradesh, India. SAR data used in this study is of Radarsat-2 (RS2) and Environmental Satellite (ENVISAT), Advanced Synthetic Aperture Radar (ASAR). The SAR preprocessing was done with SAR image processing tools for converting raw SAR images into calibrated geo-coded backscatter images. Maps for forest, built area, layover and shadow were created and used for masking snow cover in these areas. The backscattering ratio of wet snow to reference image threshold method with value range from −2 to −3 db was used to estimate wet SCA for study area. In this technique, if the threshold is too high (≥-2 db) wet SCA is overestimated and if it is too low (≤-3db), this method underestimates the SCA. The wet SCA is under/over estimated (+6 % to−8 % on average) in late spring season due to the inherent terrain and SAR imaging effects of layover/foreshortening and shadow and also due to the masking of forest areas. Overall, the SCA derived from SAR data matches well when compared with total SCA derived from cloud free optical remote sensing data products, especially during wet season.