This study examines the impact of wave breaking (WB) and Bragg scattering on the normalized radar cross section (NRCS) to reveal radar scattering features during high wind conditions. This is conducted by decomposing C-band quasi-synchronous wide-swath quad-polarization synthetic aperture radar (SAR) observations acquired from the RADARSAT Constellation Mission (RCM) and RADARSAT-2 (RS-2). The analysis results clearly demonstrate that the polarization difference (PD) associated with Bragg scattering saturates at high wind speeds, while still maintaining azimuthal modulation. Notably, the radar returns from breaking waves at cross-polarization (HV or VH) exhibit higher sensitivity to wind speeds but lower sensitivity to wind direction, compared to co-polarization (HH or VV). Moreover, our analyses show that WB contributes 40%, 80%, and 90% of VV-, HH-, and HV-polarized NRCS, respectively. This study highlights the unique capabilities provided by collocated RCM and RS-2 observations for investigating radar scattering features under high wind conditions. Results of this study can be further used to develop empirical models for estimating co- and cross-polarization radar backscatters induced by WB under high wind speeds.

This article introduces an innovative deep-learning approach for retrieving tropical cyclone (TC) rainfall information from C-band Sentinel-1 synthetic aperture radar (SAR) imagery. We collected 17 SAR images under TC conditions from 2016 to 2021 and matched them with synchronous observational Next Generation Weather Radar (NEXRAD) Level-III data, forming a dataset of 302689 data pairs for model development. The model inputs include SAR-measured physical parameters in normalized radar cross section (NRCS), texture features represented by the gray-level co-occurrence matrix (GLCM), and statistical parameters of VV-polarized NRCS. A deep-learning-based TC rain rate retrieval (TC3R) model, combining a convolutional network and a fully connected (FC) network, was developed to retrieve quantitative TC rainfall information effectively. The test results demonstrate that the TC3R model can offer reasonable and stable quantitative rainfall estimation, particularly effectively detecting areas with medium-to-heavy rainfall events (2.5–40 mm/h) in SAR images where the NRCS is significantly affected by rain. Furthermore, to offer valuable insights into the performance of the TC3R model, we analyzed results across TC events of different intensities as case studies. Our results show high structural similarity (SSIM) in rainfall patterns between SAR and NEXRAD across all cases, consistently achieving SSIM values above 0.67. Moreover, in areas where SAR signals are notably affected by rainfall, the SSIM index even exceeds 0.80. Finally, our model’s performance was evaluated by comparing its results with the independent global precipitation measurement (GPM) data, demonstrating effective rainfall prediction, particularly for the primary spiral rain band, in the two cases analyzed.

We present the characteristics of the sea surface height (SSH) response to tropical cyclones (TCs), based on a statistical analysis of long time series of satellite altimeter data. SSH anomalies on both along-and cross-track of TC are found to be dependent on storm intensity and translation velocity. Larger sea surface troughs correspond to the stronger and slower moving TCs. The most striking trough features are located in the vicinity of TC centers. The amplitudes of wedge-shaped troughs decay in the direction normal to the storm tracks. In extreme weather conditions, these troughs are primarily caused by the vertical displacement of isopycnals in the thermocline. For very strong and slow-moving TCs, the magnitude of the sea surface depressions can reach -21 cm. The spatial distribution of SSH anomalies tends to be asymmetric for faster moving TCs, comparing both sides of the TC tracks, becoming more symmetric for slower storms. The conventional altimeter has large gaps between tracks, especially in the tropics, which leads to the absence of SSH anomaly observations. To address this inherent limitation, we apply a semi-empirical model to estimate SSH anomalies, using TC and ocean stratification parameters. When applying the maximum wind speed and radius of the maximum wind derived from high-resolution synthetic aperture radar (SAR) gridded surface wind fields, the semi-empirical model estimates are in good overall agreement with collocated altimeter observations. These results suggest that indirect calculations of SSH anomalies may serve as an important complement for TC inner-core areas, when altimeter observations are not available.

We present an approach for automatic classification of Arctic sea ice and open water from spaceborne C-band RADARSAT-2 fully polarimetric (HH, HV, VH, VV) synthetic aperture radar (SAR) imagery, based on the random forest (RF) model. The HH- and HV-polarized radar backscatter, incidence angle and optimal polarimetric features are inputs of the RF model.First, we use a physics-based unsupervised scheme to generate well-annotated sea ice and open water samples. This scheme can alleviate labeling errors arising from visual interpretation or temporal-spatial mismatch between SAR images and operational ice charts.Subsequently, we estimate a set of characteristic polarimetric parameters and select the most representative features, using the recursive feature elimination method.Finally, the RF model is trained and validated using more than one million labelled samples. Statistical validation results show that the overall sea ice and water classification accuracy is 99.94% and the Kappa coefficient is 0.999. We find that ice–water discrimination accuracy can be improved by about 4%–10%, when optimal polarimetric features are involved in the RF model input. Moreover, thepolarization difference(PD, VV–HH) is found to be the foremost polarimetric parameter for distinguishing sea ice from open water. The proposed approach has capability of yielding satisfactory ice–water classification over the complicated marginal ice zone. High-resolution ice–water classification maps clearly show that sea ice leads and their surroundings are also well separated.

Although cross-polarized synthetic aperture radar (SAR) images play a crucial role in wind speed retrieval under extreme weather conditions, the retrieved wind speeds are susceptible to thermal noise. However, there is still a lack of research on how thermal noise affects the accuracy of wind speed retrieval. To address this issue, this paper proposes a strategy to quantitatively examine the impact of thermal noise on wind speed retrieval, using 910 Sentinel-1 cross-polarized SAR images acquired in Extra-Wide Swath (EW) mode. The thermal noise and wind speeds of these images range from -35 dB to -22.5 dB and from 5 m/s to 70 m/s, respectively. By considering the wind speed retrieved from dual-polarized SAR as a reference, the study reveals that higher levels of thermal noise result in increased uncertainty in the retrieved wind speeds using cross-polarized SAR images. Additionally, the error of wind speed retrieval from cross-polarized SAR rapidly decreases as wind speed increases, eventually converging to a stable level. Notably, as thermal noise levels decrease to less than -30 dB for wind speeds between 5 m/s and 25 m/s, the root-mean-square error (RMSE) associated with wind speed retrieval through cross-polarized SAR imagery experiences a rapid decline, with a 94% reduction in RMSE, which then stabilizes. This implies that when the thermal noise level falls below -30 dB, wind speed retrieval can be directly conducted using cross-polarized imagery for wind speeds exceeding those typical of a tropical storm, yielding comparable results to dual-polarized imagery. The findings of this study provide valuable insights for advancing wind speed retrieval algorithms and hold significant reference value for the design of the next-generation radar instruments that will incorporate the cross-polarized channel.

This is the first presentation of quasi-synchronous spaceborne synthetic aperture radar (SAR) high-resolution images acquired from C-band Radar Constellation Mission (RCM) and RADARSAT-2 consisting of quad-polarization (HH + HV + VH + VV) wide-swath observations of Hurricane Epsilon. These measurements clearly show that the denoised HV- and VH-polarized normalized radar cross sections (NRCSs) have great consistency. NRCS values at HV- and VH-polarizations are more sensitive to wind speeds and less sensitive to incidence angles or wind directions than those at HH and VV for hurricane force winds. For large incidence angles and high wind speeds, the sensitivity of HH-polarized NRCS to wind speed is higher than that of VV. HH- and VV-polarized NRCSs gradually lose wind direction dependency at high winds. It is notable that the time interval between the two SAR acquisitions is only 3 min. This allows for a direct comparison of HV- and VH-polarized images to investigate the variations in high-resolution backscattering within the hurricane vortex, thereby revealing the most dynamical areas. An asymmetric dynamic is observed around the eye of Hurricane Epsilon, based on positive and negative differences (VH–HV) in the western and eastern parts of the eye. The impacts of rain on quad-polarized NRCS are also examined using collocated rain rates from the Global Precipitation Mission (GPM) and wind speeds from the Soil Moisture Active Passive (SMAP). Significant rain-induced NRCS attenuations are about 1.7 dB for HH and VV, and 2.2 dB for HV and VH, when the rain rate is 20 mm/h. These attenuations are associated with rain-induced turbulence and atmospheric absorption. This work shows that the collocated RCM and RADARSAT-2 hurricane observations provide a unique analysis of synoptic and joint C-band measurements of the ocean surface in quad-polarization; this is noteworthy in view of preparations for the next generation of dual-polarization scatterometer (SCA) onboard second-generation meteorological operational satellite program (MetOp-SG).

We propose a methodology to estimate the tropical cyclone (TC) center location associated with the additional TC parameters of the radius of maximum wind (RMW) and intensity, purely from ocean winds observed by the Soil Moisture Active Passive (SMAP) radiometer. This method assumes a symmetric vortex. To demonstrate the method, we analyze 28 SMAP wind fields collected during 11 TCs. Verification of the estimated hurricane centers and wind distributions along the radius is compared with measurements provided by the airborne stepped-frequency microwave radiometer (SFMR) collected during its flying through hurricane cores and to sea surface wind fields derived from spaceborne synthetic aperture radar data. The significance of this simple method is that TC centers, intensities, and RMW can be estimated purely from SMAP wind products, despite SMAP’s low spatial resolution. Comparing these 28 model results to the aircraft measurement (f-deck) and the best track (BT) data, we show that, for the latitudes and longitudes of the TC centers, the standard deviations are 0.28° and 0.29°, respectively, both with a correlation of 1.00. For the detected intensity, the standard deviation is 8.11 m/s, and the correlation is 0.84. We note that the TC intensity detected by this method can be even stronger than the maximum winds observed by the relatively low-resolution SMAP observations.

The oil-water mixture ratio for oil spills on the ocean surface is an important parameter for volume estimation of oil spills, response strategy for the oil spills, cleanup operations, and remediation planning for the impacts on wildlife. The hybrid-polarized (HP) mode compact polarization (CP) Synthetic Aperture Radar (SAR) is a new SAR mode. Such new SARs have been carried by several earth observation systems. We present a methodology to retrieve the oil-water mixture ratio at the ocean surface using CP SAR imagery. The Uninhabited Aerial Vehicle SAR (UA VSAR) L band observations collected on June 23, 2010 over the site of the Deep Water Horizon drilling rig will be used. The CP data is simulated from the quad-pol UA VSAR observations.

A new HH-polarized geophysical model function (GMF), called CMODH, has been proposed recently for C-band synthetic aperture radar (SAR) ocean surface wind speed retrieval. CMODH has the potential to be directly used to retrieve wind speed from HH-polarized SAR images without converting the normalized radar cross section (NRCS) from HH- into VV-polarization using various empirical and theoretical polarization ratio (PR) models. However, the capability of CMODH for wind speed retrieval has not been comprehensively validated. In this letter, as a case study, two C-band HH-polarized SAR images acquired in RADARSAT-2 (RS-2) quad-polarization and Sentinel-1A (S1-1A) dual-polarization modes are first used to examine the CMODH performance. The wind speeds estimated using CMODH are shown to be consistent with buoy winds. A statistical comparison is then carried out to further validate CMODH using collocated 1457 C-band RS-2 and 284 S1-1A/B HH-polarized SAR images and 110 in situ buoys. The results show that the CMODH-retrieved wind speeds are in good agreement with buoy measurements, with a bias of 0.07 and 0.49 m/s and a root mean square error (RMSE) of 1.66 and 2.05 m/s for RS-2 and S1-1A/B, respectively. Compared to hybrid models, such as CMOD5.N and various PR models, CMODH achieves the smallest RMSE for wind speed retrieval. Moreover, for the first time, it is shown that the wind speed retrieval capability of CMODH is better than that of CMOD5.N for incidence angles between 30° and 49° and wind speeds between 10 and 20 m/s, which provides a new prospective on wind retrieval improvement using a combination of VV- and HH-polarized SAR observations.

River runoff on the Louisiana Coast produces shallow, low-salinity lenses. Due to the presence of a leaking oil platform (Taylor Energy), the fine structure of these lenses is visible in synthetic aperture radar (SAR) imagery. In this paper, we report results of the comprehensive study including high-resolution remote sensing and in-situ observations of low-salinity lenses on the Louisiana Coast as well as modeling the lens using a computational fluid dynamics (CFD) model. Our results indicate that these transient near-surface lenses create significant horizontal density gradients and spread as gravity currents, influencing the propagation of oil slicks.

Under extreme weather conditions, the imprints of kilometer-scale marine atmospheric boundary layer roll vortices on the ocean surface are clearly visible in synthetic aperture radar (SAR) images of storms. Therefore, information about wind direction in storms can be obtained by analyzing SAR image features caused by boundary layer rolls. VH-polarized SAR imagery captures the structural features of storms well and shows prominent image gradients along the radial directions of the storm. The signal-to-noise ratios of VH-polarized images are small in low wind speed areas, but they are large in the same regions of VV-polarized images. Also, the capability of retrieving the atmospheric rolls orientation in VV-polarization is found to be sensitive to incidence angle, with better performances for larger incidence angles. Thus, there is the potential to retrieve the storm's wind directions using a combination of the VH- and VV-polarized SAR observations. In this article, we use the local gradient method to estimate tropical cyclone (TC) wind directions from C-band RADARSAT-2 and Sentinel-1A dual-polarization (VV + VH) SAR imagery. As a case study, wind directions with a spatial resolution of 25 km are derived by using both wide-swath VV- and VH-polarized SAR imagery over two hurricanes (Earl and Bertha) and one Typhoon (Meranti). We compare wind directions derived from ten dual-polarization SAR images with collocated wind directions from buoys, Global Positioning System (GPS) dropsondes, scatterometer, and radiometer. Statistical comparisons show that the wind direction bias and root-mean-square error are, respectively, -0.54° and 14.78° for VV-polarization, 0.38° and 14.25° for VH-polarization, 0.20° and 13.30° for VV- and VH-polarization, suggesting dual-polarization SAR is more suitable for the estimation of TC wind directions than VV- or VH-polarization SAR.

Co- and cross-polarized geophysical model function (GMFs) both have limitations to retrieve ocean surface wind speeds. The former underestimates high winds and the latter overstimates low winds. In this study, we both use co- and cross-polarized SAR observations to retrieve hurricane wind speeds and directions. The local gradient method is first used to estimate hurricane wind directions from C-band RADARSAT-2 and Sentinel-1A dual-polarization SAR imagery. We compare wind directions derived from 10 dual-polarization SAR images with collocated wind directions from buoys, GPS dropsondes, scatterometer, radiometer, and Hurricane Research Division Real-time Hurricane Wind Analysis System (H*Wind) data. Statistical comparisons show that the wind direction bias and root-mean-square error are 3.22° and 21.53°, respectively. Subsequently, as a case study for Hurricane Maria, the retrieved wind directions, along with co- and cross-polarized GMFs are used to derive wind speeds. The retrieved wind speeds are validated with collocated Stepped-Frequency Microwave Radiometer (SFMR) measurements. The results show that the combination of co- and cross-polarized GMFs has capability of obtaining more reasonable hurricane wind speeds and directions than by only using co- or cross-pol GMF.

In the study, the method related with ocean wave observation from the new mode polarized Synthetic Aperture Radar (SAR), the Hybrid Polarization (HP) compact polarization (CP) SAR is proposed. The diagonal elements of covariance matrix of HP, the ratio of diagonal elements of covariance matrix of HP SAR and the optimal methods are employed to retrieve ocean wave information without complex modulate transfer functions. The preliminary results from the proposed method are comparable with the results from quad-pol observations. More data is needed to verify the results.

To investigate the capability of ocean wind monitoring by the C-band compact-polarimetry (CP) Synthetic Aperture Radar (SAR), we develop a theoretical model based on the fundamental mechanisms of interactions between sea surface wind-waves and radar microwave. We simulate the dependencies of the NRCSs (normalized radar cross-sections) on wind speeds and incidence angles, for up-wind (wind direction is 0o) and cross-wind (wind direction is 90o) conditions. Analysis of the model results lead to two ocean wind retrieval methods for RV-polarization and RH-polarization (pol) SAR (Synthetic Aperture Radar) data, respectively. The RV-polarized method is proposed based on the CMOD framework and a sensitivity analysis, while the RH-polarized method and model are based on application of a quadratic function. Both the theoretical model and wind retrieval methods suggest that the RV-pol should be more suitable for ocean wind especially hurricane monitoring than the RH-pol method. The database used in this study includes wind vectors observed by in situ buoys of the National Data Buoy Center (NDBC) and simulated C-band CP radar signals, created by a simulator based on imputing C-band RADARSAT-2 SAR images.

The radar backscatter from the sea surface plays an important role in ocean observation. Many theoretical models have been developed to accurately estimate the scattering coefficient over the past few decades. A comparison between simulations of different scattering models with three roughness spectra and field measurements in Ku, C, and L bands and several empirical functions is obtained, demonstrating the behavior of normalized radar cross section (NRCS) for different incidence angles, wind speeds, azimuth angles, and frequencies. The verification of RADARSAT-2 measurements to these combinations suggests that NRCS values can be best reproduced in VV polarization by the CB model when the E spectrum is imported, and best in HH and VH polarizations by the SSA2 model with the H2 and E spectra, with a root mean square error (RMSE) of 1.62, 1.57 and 2.10 dB, respectively.

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.

We report an investigation of ocean-surface wind speed retrieval from C-band RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) images using a new channel of coright-circular polarization (RR-pol) in compact polarimetry (CP) option. The analysis of simulated RCM quad-polarized CP SAR data and collocated in situ buoy measurements suggests that the RR-pol is much less sensitive to wind directions than the other three polarizations in the CP option. A method is proposed for the RR-pol radar signal as a function of wind speed and incidence angle. We demonstrate that C-band RR-pol has the potential for ocean high wind retrieval, especially for cyclone studies.

Certain marine bacteria found in the near-surface layer of the ocean are expected to play an important role in the production and decay of surface active materials. Identifying a connection between marine bacteria and the production of natural surfactants may provide a better understanding of the global picture of biophysical processes at the boundary between the ocean and atmosphere, air-sea exchange of gases, and production of climate-active marine aerosols. Kurata et al. (2016) and Hamilton et al. (2015) have developed measurement methodology combining DNA sampling of sea surface microlayer with SAR satellite technology. Following Franklin et al. (2005) and Cunliffe et al. (2011), these authors used polycarbonate membrane filters in order to minimize potential contamination that may occur with other sampling techniques. A hydrophilic polycarbonate filter, attached to the sea surface by capillary forces, collected bacteria effectively from a 35-42 μm surface layer. A fly fishing technique was used in Kurata et al. (2016) and Hamilton et al. (2015) to ensure that the filter sat on the sea surface for a few seconds (away from the vessel and its wake in order to avoid these sources of disturbance to measurements of the microlayer). Samples from the water column at approximately 0.2 m depth were taken with a peristaltic pump for comparison with the sea surface results.

Three RADARSAT-2 synthetic aperture radar (SAR) images in dual-polarization mode were acquired successively over Typhoon Soudelor in early August, 2015. In this work, the center locations of the typhoon were determined from SAR images using an automatic eye detection method. The derived typhoon centers are very close to that from the tropical cyclone Best Track dataset, which allows the study of the typhoon eye evolution during its movement. The change of the sea surface wind structure was also investigated based on the sea surface wind speed retrieved from the cross-polarized SAR images with a Cross-Polarization Ocean (C-2PO) model.

Compact polarimetric (CP) synthetic aperture radar (SAR) can provide ocean surface observations with large-coverage swath and abundant polarimetric scattering information. These distinctive characteristics make CP SAR a potential tool for operational monitoring oil slicks and oil platforms, overcoming the shortcomings of small spatial coverage by the traditional quad-polarization (quad-pol) SAR. In this paper, we use the RADARSAT-2 C-band quad-pol SAR data to generate CP covariance matrix elements and subsequently construct pseudoquad-pol backscatter coefficients, using two CP reconstruction algorithms to evaluate CP SAR's applications in detection of oil slicks and oil platforms. The reconstructed co- and cross-polarization data show good agreement with original radar observations acquired at different incidence angles and wind speeds. Furthermore, we develop an unsupervised classification method using the relative phase, a logical scalar threshold that separates odd and multiple scattering events, as an indicator to discriminate oil slicks and platforms from clean ocean waters. The relative phase is positive for clean ocean surfaces where odd scattering is dominant, but negative for oil platforms and oil slick-covered areas associated with multiple scattering. The detections of oil spills and oil platforms are validated against known oil platform geographic positions and optical aircraft surveys of the oil slicks. The proposed method provides a promising technique to detect oil slicks and oil platforms from CP imaging mode SAR data, i.e., as may be acquired by the RISAT-1, ALOS-2, and the future RADARSAT Constellation Mission.

A hybrid backscattering model is built to provide a consistent description for C-band VH- and VV-polarized normalized radar cross sections (NRCSs). Ocean surface coand cross-polarized NRCS are both treated as a sum of Bragg and non-Bragg scattering components. To better understand the synthetic aperture radar (SAR) observed NRCS signals under high-wind conditions, five C-band RADARSAT-2 dual-polarization SAR hurricane images and the collocated wind vectors measured by the airborne stepped-frequency microwave radiometer (SFMR) are collected. Based on the match-up data, we add a non-Bragg term in the composite Bragg theory to explain the discrepancy between the measurements in the cross-polarization channel and the existing theory results. The non-Bragg scattering to Bragg scattering ratio (Br) is found to be a constant. We build the hybrid backscattering model with Br and establish a relationship between the cross-polarization NRCS and the radar incidence angle under different wind conditions. The NRCS dependence on incidence angle is simulated by the hybrid backscattering model. Finally, a C-band Cross-Polarization Coupled-Parameters Ocean (C-3PO) model is developed to retrieve hurricane winds using VH-polarized ScanSAR by including the radar incidence angle. The collocated SAR and SFMR data sets are separated into two parts: data set-A, for hybrid backscattering model derivation and C-3PO model coefficients tuning, and data set-B, for hurricane wind validation. C-3PO model validation results show that the model is suitable for ocean surface wind mapping from RADARSAT-2 cross-polarization ScanSAR images. The retrieval has a rootmean-square error less than 3 m/s for wind speed up to 40 m/s.

Over the last decades, data from spaceborne synthetic aperture radar (SAR) have been used in hurricane research. However, some issues remain. When wind is at hurricane strength, the wind speed retrievals from single-polarization SAR may have errors, because the backscatter signal may experience saturation and become double valued. By comparison, wind direction retrievals from cross-polarization SAR are not possible until now. In this paper, we develop a 2-D model, the symmetric hurricane estimates for wind (SHEW) model, and combine it with the modified inflow angle model to detect hurricane morphology and estimate the wind vector field imaged by cross-polarization SAR. By fitting SHEW to the SAR derived hurricane wind speed, we find the initial closest elliptical-symmetrical wind speed fields, hurricane center location, major and minor axes, the azimuthal (orientation) angle relative to the reference ellipse, and maximum wind speed. This set of hurricane morphology parameters, along with the speed of hurricane motion, are input to the inflow angle model, modified with an ellipse-shaped eye, to derive the hurricane wind direction. A total of 14 RADARSAT-2 ScanSAR images are employed to tune the combined model. Two SAR images acquired over Hurricane Arthur (2014) and Hurricane Earl (2010) are used to validate this model. Comparisons between the modeled surface wind vector and measurements from airborne stepped-frequency microwave radiometer and dropwindsondes show excellent agreement. The proposed method works well in areas with significant radar attenuation by precipitation.

Sea ice is a dominant indicator of climate change with rapid sea ice loss observed in Arctic Ocean in recent years. Spaceborne Synthetic Aperture Radar (SAR) sensors are ideal for ice monitoring because of their all-weather operation and sensitivity to surface roughness, volume structure, dielectric properties and viewing geometry. Quad-pol SAR has unique capabilities which play a key role in geophysical remote sensing, able to measure the scattering matrix, providing both intensity and phase information that can lead to classification of the scattering mechanisms of the sea ice. However, there is a cost to quad-pol SAR in terms of doubled average transmitted power, swath width and limited range of admissible incidence angles; for example, the quad-pol images are about 25 km width, which limits the monitoring of ice.

Five possible mechanisms for the rain effects on the spaceborne C-band SAR observations are considered: 1) attenuation and 2) volume backscattering for the microwave transfer in atmosphere; as well as 3) rain - induced damping to the wind waves and 4) rain - generated ring waves on the ocean surface, and 5) diffraction on the sharp edges of rain products. A composite radar scattering model composed of the atmosphere radiative transfer model and the ocean surface Bragg wave theory is employed to analyze the impact of rain on the normalized radar-backscatter cross-section (NRCS) measured in the VV- and cross-polarized C-band Synthetic Aperture Radar (SAR) channels. Our composite model was validated with the matchup of observations of wind speed and rain rate from the SFMR data as well as the NRCSs and incidence angles of C-band VV-polarization SAR data over two hurricanes. Comparisons between the observed NRCSs and the atmosphere part of our model imply that the most important mechanism for the rain effect on the C-band VV polarized SAR hurricane observations is through the influence of waves on the ocean surface. Moreover, the non-Bragg scattering is important for the cross-polarized NRCS simulations.

Compact polarimetric (CP) synthetic aperture radar (SAR) is a potential tool for operationally monitoring oil slicks and oil platforms because its large-coverage swath and abundant polarimetric scattering information. In this study, we use C-band RADARSAT-2 quad-polarization (quad-pol) SAR data to simulate CP covariance matrix elements and then construct pseudo quad-pol scattering coefficients by utilizing two different CP reconstruction algorithms. We develop an unsupervised classification method to discriminate oil slicks and platforms from clean ocean waters, using the relative phase, a logical scalar threshold that separates odd and even scattering events. The relative phases are estimated with the reconstructed co- and cross-polarization backscatters, which are positive in clean ocean surfaces where odd scattering is dominant but are negative for oil platforms and oil slick-covered areas associated with even scattering.

The Bragg scattering theory does not explain the cross-polarized (VH or HV) normalized radar-backscatter cross-sections (NRCSs) from the ocean surface very well. The difference lies in the non-Bragg scatterings mechanisms including specular reflection and quasi-specular reflection or diffraction on the sharp edges of wave breaking. The specular reflection is a function of radar incidence angle and is negligible for the intermediate incidence angle. The quasi-specular reflection and diffraction on the sharp edges of wave breaking is a function of incidence angle, wind speed and direction. In this study, we adopt the C-band Cross-polarization Ocean (C-2PO) model as the total backscatter from the ocean surface, and assume the non-Bragg scattering as the differences between NRCSs simulated by C-2PO model and composite Bragg model. And a non-Bragg scattering ratio is suggested. With the collocated datasets of wind speeds, wind directions, measured by airborne SFMR and NRCSs from SAR, the suggested non-Bragg scattering ratio is validated.

Microwave backscattering from the ocean surface is closely related to the wind-generated ocean surface roughness. This property is used for obtaining global ocean surface vector winds. The deployed scatterometers so far do not use the cross-polarized sea return (VH, representing either vertical transmit horizontal receive or horizontal transmit vertical receive) because of its weak signal level. The copolarized returns (VV or HH), however, may saturate in high wind speeds especially for low incidence angles.

We present a ocean surface waves and winds simultaneous retrieval method for the RADARSAT-2 fully polarimetric synthetic aperture radar (FP-SAR) imaging mode. This algorithm integrates FP-SAR wind vector retrieval model with nonlinear mapping relations between ocean wave spectrum and SAR image spectrum, to synergistically retrieve wind field and wave directional spectrum. The method does not require a priori information on the sea state. The proposed method can simultaneously acquire ocean surface wave and wind information.

Several papers highlighting the monotonic increase (with respect to wind speed) of the cross polarization radar backscatter (σ0VHor VH for shorthand in the text) from the ocean surface have been published recently (Hwang et al, 2010a, b; Vachon and Wolfe, 2011; Zhang et al., 2011). The result has generated some interest in exploring hurricane wind retrieval using VH and to incorporate VH in the next generation scatterometer designs. In the subsequent development of algorithms (e.g., Zhang el al, 2011, 2012, 2014 Zhang and Perrie, 2012; van Zadelhoff et al. 2013 Belmonte Rivas et al, 2014), it is assumed that VH varies mainly with wind speed (U10) and independent on the azimuthal angle (φ). Its dependence on incidence angle (θ) is either ignored [Zhang et al, 2011, 2012, 2014 Zhang and Perrie, 2012] or accounted for only in low to strong wind range (U10<;21 m/s) [van Zadelhoff et al., 2013, 1014].

Over the last several years, considerable efforts have been devoted to investigate hurricane surface winds using SAR observations [1]-[6]. Compared to optical satellite sensors, synthetic aperture radar (SAR) has advantages for observation of ocean surface winds, with high resolution and large spatial coverage, in almost all-weather conditions. Measurements of the inner-core intensities and tangential wind profiles are generally provided by NOAA aircraft flying though the hurricanes [7]. However, aircraft only can provide single point observations along the flight track. Thus, there can be some variance in the aircraft-measured tangential wind profiles, particularly for those hurricanes with asymmetric wind structures. For example, for a non-symmetric hurricane with an elliptic eye, we can assume that the true maximum wind speed exists somewhere on the major axis direction. Therefore, if the aircraft flies along the minor axis of this hurricane, instead of the major axis, the maximum wind and the radius of maximum wind (RMW) observations are probably not accurate. In this study, we determine the hurricane eye center and its extent, and propose a model to estimate hurricane intensity and structure parameters. A specific objective is to describe the tangential wind profile corresponding to the reintensification phase in the hurricane eyewall replacement cycle (ERC).

The geophysical model function is generally employed to invert the surface wind speed using scatterometer or synthetic aperture radar (SAR) measurements over the ocean. The GMF relates the normalized radar cross-section (NRCS) to the incidence angle and wind vector. The rain effect on NRCS is not considered in the GMF. In raining areas, the NRCS of the ocean surface is altered by rain. Rain contamination introduces errors to wind speed retrieved by scatterometer, particularly at high incidence angles [1]. Moreover, under extreme weather conditions, significant differences exist between the SAR retrieved wind speed and those measured by Stepped-Frequency Microwave Radiometer (SFMR) measurements in hurricane eyewall regions [2], due to the intense rainfall there. The challenges in satellite retrievals of ocean winds related to precipitation effects has been elaborated in [3].

Recent wind retrieval algorithms using crosspolarization (cross-pol) radar sea return (VH) assume that VH is independent on the azimuth angle and mainly varies with the wind speed. Incidence angle dependence is either absent or is only in wind speeds less than 21 m/s. However, azimuth and incidence angle variations are expected since theory and data comparisons show the dominance of surface effects in the scattering mechanisms; thus, both co-polarization and cross-pol cross sections reflect the directional distribution of the ocean surface roughness and wave breaking. Here, the VH dependence on wind velocity is analyzed with special focus on the variations with the incidence and azimuth angles. The results show that, for the typical incidence angle range of radar images used for hurricane wind retrieval (20°-50°), the magnitude of these angular variations is equivalent to a difference of about 10 m/s in the retrieved wind speed for low-to-strong winds (~21 m/s) as well. It is prudent to incorporate the incidence angle dependence and the azimuth angle dependence in the wind retrieval algorithm and in the signal simulation for the design of next-generation scatterometers. The dependence on the wind speed is also examined. It reconfirms that the VH sensitivity increases toward high winds, but signal saturation may occur.

Recent studies show the promising ability of cross-polarization synthetic aperture radar (SAR) for high-wind-speed monitoring. However, no dual-polarization (dual-pol) geophysical model functions (GMFs) have thus far been established. In this paper, we first address the difference between dual-pol and quad-pol VH radar returns with respect to wind speed and show the necessity of having a VH dual-pol GMF for ScanSAR images, for remote sensing of mesoscale wind processes, such as hurricanes. By collocating VH dual-pol RADARSAT-2 SAR measurements with in situ buoy winds, a VH dual-pol GMF was developed after introducing a denoise procedure to increase the signal-to-noise ratio for SAR measurements. Study results suggest that the VH dual-pol radar returns have a linear monotonic piecewise increasing dependence on wind speed, with decreasing scatter in high winds. Successful application of the GMF to high-wind-speed retrieval for hurricane Gustav in 2008 suggests that VH mode SAR images do not exhibit signal saturation or the speed ambiguity problem that affects copolarization SAR images for high-wind-speed retrieval. This result supports the potential future application of VH dual-pol SAR images to hurricane monitoring.

Hurricane tangential wind profiles are routinely observed by aircraft reconnaissance in order to estimate the surface winds. However, these wind profile estimates are occasionally biased because along-track aircraft observations might not determine the nonaxisymmetric hurricane structure characteristics. In this paper, we resolve this problem by calculating the mean wind speed in all radial directions using cross-polarization SAR wide-swath images. Moreover, we propose a one-half modified Rankine vortex (OHMRV) model to describe the hurricane wind profile, particularly for those wind profiles with a wind speed maximum and an inflection point possibly associated with the degeneration of the inner wind maximum in the hurricane reintensification phase. OHMRV characterizes the hurricane wind profile and represents a model that complements the previously established single-modified Rankine vortex model and the double-modified Rankine vortex model. Moreover, the OHMRV-derived wind profiles are used to estimate hurricane intensity and structure parameters, such as the maximum wind speed and the radius of maximum wind. For validation of the method, the estimated ISPs are compared with measurements by the stepped-frequency microwave radiometer on board the National Oceanic and Atmospheric Administration aircraft. These parameters contribute to a description of hurricane inner-core intensity and structure associated with eyewall replacement cycles.

An analysis of the polarimetric scattering properties of oil-covered waters is conducted using the classic Poincaré ellipticity parameter chi (χ) from the Stokes parameters of hybrid polarized mode Synthetic Aperture Radar (SAR). For natural oil seeps, χ has a change in signs, comparing oil-covered waters with the `clean' ocean surface. The χ sign reversal is basic to sign difference oil spill detection methods. However, a problem is that oil spills related to the Deep Water Horizon (DWH) disaster did not exhibit a reversal in χ signs, when `clean' ocean surfaces are compared to the area contaminated by crude oil. Therefore, the scattering mechanism related to the oil seeps is different from that of the DWH oil spill; the former is dominated with even bounce scattering and the latter is dominated by Bragg scattering, similar to that of the clean oil-free ocean surface.

Hybrid-polarimetric SAR (synthetic aperture radar) is a new sensor, with relatively simple architecture, low cost and wide swath, which will be carried by several Earth-observing systems in the near future. Here, we show how the second Stokes parameter of hybrid-polarimetric SAR can be employed to detect oil on the ocean surface, in relation to contributions from different polarizations and dampening effects on backscatter intensity, neglecting the scattering mechanism. The detection methodology is demonstrated to be reliable in two example cases: natural oil seeps from the Gulf of Mexico, and observations from the Deep Water Horizon oil spill disaster in 2010.

We report the recent research progress on SAR winds retrieval using polarized RADARSAT-2 data. The two polarization ratio models were studied and applied for wind speed estimation using SAR image acquired at HH polarization. The wind speeds were also calculated utilizing VV-polarized SAR image and corresponding geophysical model function (GMF), and compared with buoy observations. We developed a cross-polarization ocean backscatter model for high wind speed retrieval. The hurricane wind vectors can be obtained by employing this model and the VV-polarized GMF. Moreover, we also presented a fully polarimetric SAR ocean vector winds retrieval approach. The wind direction ambiguities were removed with polarimetric correlation coefficients between co- and cross-polarized channels.

We presents a new method for retrieving tropical cyclone vector winds using C-band dual-polarization SAR observations. The co-polarized geophysical model function (CMOD5.N) and a cross-polarized wind speed retrieval model for dual-polarization (C-2POD) are employed to construct a cost function. minimization of the cost function allows optimum estimates for the wind speeds and directions. The wind direction ambiguities are removed by a parametric two dimensional sea-surface inflow angle model.

We present a innovative physically-based approach to detect oil platforms by using compact polarimetric (CP) synthetic aperture data. We utilize two RADARSAT-2 fine quad-polarization SAR images of Gulf of Mexico to estimate the CP covariance matrix. The resulting CP data is used to reconstruct pseudo quad-polarization covariance matrix. The Stokes parameters are evaluated using the reconstructed pseudo quad-polarization covariance matrix elements. We further calculate the relative phase with two Stokes parameters. The relative phase is a logical scalar descriptor which can be used to detect the oil platforms. The detected oil platforms are validated with ground truth from NOAA. The proposed method provides a simple and effective mapping technique for oil platform detection.

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.

Based on linear statistical relationships between components of SST gradients and wind stress variations, a high-resolution methodology is presented to retrieve Gulf Stream thermal front features using only variations in pixel-scale features of the SAR-derived wind stress divergence and curl fields, representing a significant improvement in methodology. It is important to remove small-scale features in divergence and curl wind stress images before they are used to construct the thermal front parameter, TF. We also verified the results with another 42 RADARSAT-2 images acquired at dual-polarization (VV, VH) image mode in the Gulf Stream region. Results indicates that the proposed method works well when retrieved wind speed lies between 5 m/s and 12 m/s, because SST-induced wind gradients can modify the vorticity and divergence fields [O'Neill et al. 2010].

This paper discusses the potential for automatic ocean surveillance using compact linear polarization (CL-pol) SAR, with large area coverage, including high sea state conditions. Here, the target is a wind farm in the North Sea. The relative phase 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 its sign is opposite for these two entities. Therefore, the relative phase of CL-pol SAR provides new light on the problem of operational auto-detection of man-made targets, under high sea state conditions, over large areas.

We present an efficient algorithm for retrieving the ocean-surface wind vector from C-band Radar Satellite RADARSAT-2 fully polarimetric synthetic aperture radar (SAR) measurements based upon the copolarized geophysical model function, i.e., CMOD5.N, and the cross-polarized ocean backscatter model, i.e., C-2PO. The analysis of fine quad-polarization mode single-look complex SAR data and collocated in situ moored buoy observations reveals that the polarimetric correlation coefficient between co- and cross-polarization channels has odd symmetry with respect to the wind direction. This characteristic is different from the feature that normalized radar cross sections for quad-polarization have even symmetry regarding the wind direction. We first use the C-2PO model to directly retrieve wind speeds without any external wind-direction and radar-incidence-angle inputs. Subsequently, the retrieved wind speeds, along with incidence angles and CMOD5.N, are employed to invert the wind direction, still with ambiguities. The odd-symmetry property is then applied to remove the wind direction ambiguities. Thus, it is shown that fully polarimetric SAR measurements provide complementary directional information for the ocean-surface wind fields. This method has the potential to improve wind vector retrievals from space.

Presently, wind velocity retrieval uses co-polarized (co-pol) normalized radar cross sections (NRCS) from the ocean surface (σοyy and σ0HH, respectively vertical transmit vertical receive and horizontal transmit horizontal receive). In recent analyses of the RADARSAT-2 (R2) quad polarization (quad-pol) data, it is found that whereas the co-pol backscatter shows a saturation trend in winds exceeding about 16 m/s for incidence angle 6*;~10 m/s) from about linear in moderate winds (<;~7 m/s). Thus, the retrieved wind speed using σ0VH becomes more accurate in higher wind speeds. This is a significant breakthrough for monitoring hazardous wind events such as hurricanes and subtropical storms with wind speeds frequently exceed the ambiguity thresholds of wind retrieval using the co-pol radar backscatter. The spatial coverage of R2 quad-pol data is nominally 25 km by 25 km. For a large area of coverage, the Scan SAR beam modes (300 km for narrow and 500 km for widemode) are more practical, and the cross-pol data are only available in dual-polarization (dual-pol) mode, either HH and HV or VV and VH. Here the result of wind speed retrieval using dual-pol σ0VH is presented.

In this paper, several examples of synthetic aperture radar images of the sea surface from TerraSAR-X and RADARSAT-2 in the Straits of Florida are examined with corresponding in-situ data. The in-situ observations included vertical and horizontal fine thermo-haline structure, sonar transects, current velocity, directional wave characteristics, weather parameters, and Doppler weather radar data. The appearance of natural and artificial features in these images is discussed in relation to the environmental conditions during the overpass and the satellite sensor used for observation.

SAR can penetrate clouds and precipitation and can provide accurate observations of surface features such as fronts and localized coastal marine winds. Often atmosphere-ocean interaction processes evident in SAR imagery are not present from in situ observations, the operational network, weather prediction models, or scatterometer data, especially in complex coastal areas. Timely, high-resolution SAR-derived wind data have a great potential to improve operational marine weather forecasts, particularly in coastal areas. As an outcome of NSWP the marine meteorologists and other users have an interrupted access to high resolution color-coded maps of SAR-derived wind speeds, ocean buoy observations, and surface wind predictions from NWP models. Over the last 18 months, they had access to over 13,000 such images across all Canadian coastal zones. The two-year Canadian National SAR Winds Project (2009–2011) has successfully demonstrated that quasi-operational SAR Wind retrieval system could be an important element of the operational weather analysis and prediction structure.

The wind generates a distribution of small slope waves and sporadic steep breaking events. Such double structure of the sea surface is expected to have a strong impact on the radar scattering from the ocean surface. The signature of the double structure is in the wind speed dependence of radar returns: linear for scattering from gentle waves and cubic for breaking contribution. The composite-surface Bragg resonance (CB) theory describes the former very well. Detection of the breaking contribution remains difficult. Here we show that the depolarized (de-pol) radar return exhibits the typical double structure, its wind speed dependence increases with wind speed from linear to cubic. The increased sensitivity of the de-pol returns in high winds is ideal for hurricane wind retrieval. The strong breaking connection offers an opportunity to measure wave breaking and the associated energy dissipation and area of foam coverage from space, their quantification is important in air-sea interaction and electromagnetic and electro-optical remote sensing.

Our objective is to detect ocean surface features, specifically oceanic thermal fronts, through analysis of SAR (synthetic aperture radar)-derived wind stress fields. Fine-resolution measurements of near-surface wind speeds over the Gulf Stream region of the Northwest Atlantic were made using SAR images collected by RADARSAT-2. Linear statistical relationships between the wind stress curl and divergence to the crosswind and downwind components of the sea surface temperature (SST) gradient field were used to derive a new method for detecting Gulf Stream thermal fronts from Synthetic Aperture Radar (SAR) imagery. In particular, sea surface temperature front features, as suggested by corresponding AVHRR and MODIS images, are evident in both of the wind stress curl and divergence fields.

We propose two new analytical polarization ratio (PR) models based on the RADARSAT-2 Quad-Polarization (HH+VV+HV+VH) observations over the ocean. One is a function of incidence angle only and the other has additional dependence on wind speed. Comparisons are presented with theoretical and empirical PR models from the literature. The new PR model with wind speed and incidence angle dependence is shown to compare best with observed RADARSAT-2 data. An assessment of the PR model with only incidence angle dependence is given using CMOD algorithms and HH-polarized images. Results suggest that this PR model can accurately convert normalized radar cross sections (NRCS) in HH polarization to VV polarization and retrieve wind speeds from RADARSAT-1 or RADARSAT-2 HH polarization images.

Four C-band fully polarimetric synthetic aperture radar (POLSAR) images of ocean waves from the RADARSAT-2 SAR are used to measure ocean slopes and wave spectra. A new technique has been developed to measure wave slopes in the SAR azimuth and range directions. The POLSAR ocean wave parameter measurements were validated with in situ observations from an NOAA National Data buoy Center (NDBC) buoy. The results show that wave parameters measured using the new method are in good agreement with in situ NDBC measurement products.

A new model is proposed to estimate significant wave heights from ERS-1/2 scatterometer data. We find that the relationship between wave parameters and the radar backscattering cross section is similar to that between the radar backscattering cross section and wind. The model for the relation between significant wave height and the radar cross section is obtained by a neural network algorithm. When the average wave period is less than 7s, the root mean square of significant wave height retrieved from ERS-1/2 data is 0.51 m. When the average wave period is more than 7 s, it is 0.72 m.

An retrieval algorithm is developed for wind speed inside hurricanes or typhoons from microwave radiometers, SSM/I and AMSR. We match use GEM numerical weather forecast model outputs for wind speed in comparisons with SSM/I and AMSR data. The algorithm using AMSR data is better than that for SSM/I, because AMSR has two low frequencies radiometers, 6 and 10 GHz, and they are more suitable than the high frequency radiometers to retrieve wind speeds in cases involving rain. The RMS for our algorithm is less than 2 m/s or 10%. The algorithm shows improvement over recent algorithms for retrieving wind speeds inside hurricanes or typhoons.

Based on polarization ratio from ENVISAT ASAR, we compared the wind vectors retrieved from CMOD5 with alpha= 0.6, alpha = 0.5, our polarization ratio,VV HWGMF with our polarization ratio and HH HWGMF with results from QuikSCAT and buoy, respectively. We found that CMOD5 with alpha= 0.5 polarization ratio is best one for retrieving high wind speed from RADARSAT SAR; and error between QuikSCAT and HH HWGMF is the least. The polarization ratio and GMF for high wind need be improved.

In this presentation, the polarization ratios were calculated from AIRSAR polarimetric SAR data and ENVISAT ASAR dual-polarization data; and their empirical CC parameters which depend on incidence angle were obtained. Five C band HH polarization RADARSAT-1 SAR images are used to validate these polarization ratios and we found that the empirical parameter alpha = 0.5 is superior to other possible parameter alpha values.

Radar backscattered signals over the ocean are dampened in extremely high winds, which leads to a wind speed ambiguity problem during the process of wind retrieval from synthetic aperture radar (SAR). This problem was firstly studied by Shen et al. (2007), where we proposed a wind speed ambiguity removal scheme for the two wind speed solutions that may exist for any given normalized radar cross section (NRCS) and wind direction. This approach is based on the operational geophysical model function (GMF) CMOD5. Recently, new C-band GMFs for high wind have been developed, among which, a HH polarized GMF was established for the first time. In this study, the wind speed ambiguity problem will be studied within the context of the available high wind GMFs, which are, CMOD5, CMOD4HW, HWGMF_V and HWGMF_H. For the wind retrieval from HH polarized SAR images, a hybrid empirical polarization ratio is generally adopted. To compare the different behavior of various GMF models, this polarization ratio is used to transform the HH polarized GMF into a VV field. Although the wind speed ambiguity problem is found in most GMFs, the saturation wind speed where radar backscattered signals start to decrease is different for the various GMFs. We show that consideration of the wind speed ambiguity problem is important for high wind retrieval from SAR images, especially for category 5 hurricanes.

In this letter, a new wind-vector algorithm is presented that uses radar backscatter sigma0measurements at two adjacent subscenes of RADARSAT-1 synthetic aperture radar (SAR) images, with each subscene having slightly different geometry. Resultant wind vectors are validated using in situ buoy measurements and compared with wind vectors determined from a hybrid wind-retrieval model using wind directions determined by spectral analysis of wind-induced image streaks and observed by colocated QuikSCAT measurements. The hybrid wind-retrieval model consists of CMOD-IFR2 [applicable to C-band vertical-vertical (VV) polarization] and a C-band copolarization ratio according to Kirchhoff scattering. The new algorithm displays improved skill in wind-vector estimation for RADARSAT-1 SAR data when compared to conventional wind-retrieval methodology. In addition, unlike conventional methods, the present method is applicable to RADARSAT-1 images both with and without visible streaks. However, this method requires ancillary data such as buoy measurements to resolve the ambiguity in retrieved wind direction

During the past decade, the ability to determine detailed information of ocean surface wind from Synthetic Aperture Radar (SAR) has been generally accepted. It is expected that SAR will provide all-day and all-weather wind parameters with high spatial resolution and high accuracy, which would provide valuable data to marine weather forecasters and SAR model designers. However, the methodology for retrieving wind fields from SAR is far from complete. Present procedures require that a prior wind direction must be available in order to get wind speed information from SAR images. Errors in wind direction can result in significant biases in the SAR-retrieved wind speed. The prior wind direction can be acquired from measurements of other instruments (scatterometer, buoys etc.), from meteorological model outputs, or from wind-induced slicks if evident in the SAR images. The latter method may be limited by the image; many SAR images don't have any obvious wind-induced slicks. He et al. [2005] developed a new gradient method model (GM), which can retrieve wind directions from SAR images without trying to infer slicks. However, thus far, this method can only be used in relatively uniform wind field situations. In this paper, two modifications to the GM method have been developed by introducing an optimal analysis method. Wind speed and direction (with ambiguity) can be retrieved directly. In this method, sigma0from two overlapped sub-blocks of the SAR image are used to retrieve wind parameters, which would violate the original GM method, because it cannot be used in highly varied winds, such as hurricanes. The chief advantage of this method is that wind speed can be retrieved without additional reference wind directional information. The wind direction ambiguity can be removed by background geophysical information of the SAR image or by in-situ measurements. Two cases are considered to illustrate this new method. One is a simulated image with relatively uniform wind field, and the other is a Radarsat-1 image which captures the eye of hurricane Isabel before it made landfall in North Carolina in 2003 as a category 2 storm. In both cases, the retrieved wind result is shown to compare well with Quickscat measurements andin-situdata. However, error exists for highly-varied wind field, especially for the retrieved wind direction. Future investigation will focus on this limitation, and also conduct further comparisons.

Here, we present a new method to measure ocean wave slopes in the range direction using dual-polarization SAR image intensity, without need for estimation of the polarization orientation angles. This method is different from those developed by Shuler et al. and Pottier.. In addition, it eliminates the effect of the not well known hydrodynamic modulation on retrieving ocean surface spectra from SAR data Comparisons will be made between ocean wave spectra measured using ENVISAT C band dual-polarization (HH and VV) backscatter data and in situ buoys maintained by NOAA National Data Buoy Center (NDBC). I. INTRODUCTION

Ocean wind speed and wind direction are estimated simultaneously using the normalized radar cross sections /spl sigma//sup 0/ corresponding to two neighboring (25-km) blocks, within a given synthetic aperture radar (SAR) image, having slightly different incidence angles. This method is motivated by the methodology used for scatterometer data. The wind direction ambiguity is removed by using the direction closest to that given by a buoy or some other source of information. We demonstrate this method with 11 ENVISAT Advanced SAR sensor images of the Gulf of Mexico and coastal waters of the North Atlantic. Estimated wind vectors are compared with wind measurements from buoys and scatterometer data. We show that this method can surpass other methods in some cases, even those with insufficient visible wind-induced streaks in the SAR images, to extract wind vectors.

Wind speed and direction are estimated from backscatter /spl sigma//sup 0/ measurements at two adjacent subscenes of RADARSAT-1 synthetic aperture radar (SAR) images, each subscene with slightly different incidence angles. Resultant wind vectors are validated using in situ buoy measurements, and compared with SAR-derived wind vectors obtained by using the wind directions determined from a spectral analysis of SAR images and colocated QuikSCAT measurements, as inputs to a hybrid model wind retrieval model. The implemented hybrid model consists CMOD-IFR2 (applicable to C-band, VV polarization) and the C-band copolarization ratio for Kirchhoff scattering. The new algorithm displays improved skill in wind vector estimation from RADARSAT-1 SAR data.

Conventional horizontal or vertical polarization synthetic aperture radar (SAR) images seldom perform well in detecting azimuthally traveling ocean waves. However, theoretical analyses suggest that linear-polarimetric backscatter measurements may be more sensitive to these waves and, therefore, that a SAR with linear polarization is expected to give a better measurement of azimuthally traveling ocean waves. We derive the polarization-orientation modulation transform function and tilt modulation transform function of the linear-polarimetric SAR. Through numerical simulations based on these formulations, we examine the effects of radar and ocean wave parameters on linear-polarimetric SAR image spectra. We suggest a method to eliminate the 180/spl deg/ directional ambiguity in determining the true wave direction. Our numerical simulations show that the correlation between ocean wave spectra and SAR image spectra is improved for larger radar incidence angles and longer ocean waves. For real aperture radar, we suggest that the polarimetric modulation allows measurement of waves traveling in the azimuth direction. Moreover, as suggested by Schuler et al., this may be the dominant modulation for many SAR aircraft measurements, particularly for moderate sea states.