Geophysical Doppler shifts measured by synthetic aperture radar (SAR) are influenced by the combination of ocean surface winds, waves, and currents. Accurate retrieval of radial surface current velocities therefore requires precise estimation and removal of the wave-induced Doppler contributions. To address this challenge, we developedWaveDop, a hybrid physics-guided and data-driven model based on the eXtreme Gradient Boosting (XGBoost) machine learning algorithm, designed to estimate Doppler shift contributions arising from wind and waves effects. The model was trained on a comprehensive dataset comprising Sentinel-1 SAR Doppler measurements acquired over global coastal regions from January 2015 to October 2022, in conjunction with WAVEWATCH III wave model outputs and drifter-observed surface currents. Our analysis reveals that wave-induced Doppler shifts are strongly modulated by the radar incidence angle, surface wind fields, and wave characteristics, with both wind waves and swell making significant contributions. By accounting for these wave effects, and correcting for non-geophysical artifacts such as antenna electronic mis-pointing, satellite attitude variations, and azimuthal scalloping, we derive radial current velocities from SAR observations. Validation against independent drifter measurements indicates strong agreement, with a correlation coefficient of 0.82, negligible bias, and a root-mean-square error (RMSE) of 0.16 m/s. These results demonstrate thatWaveDopeffectively estimates wave-induced Doppler shifts and enables reliable retrieval of ocean surface current velocities from SAR data.

The surface wave investigation and monitoring (SWIM) instrument, onboard China and France Oceanography Satellite (CFOSAT), is a Ku-band real-aperture radar, with six beams that illuminate the ocean surface at near-nadir incidences ranging from 0° to 10°. In this study, we investigate the effect of rain on the normalized radar cross section (NRCS) measured by SWIM under both tropical cyclone (TC) and non-TC conditions. Rain primarily attenuates the radar backscatter from the ocean surface. Under non-TC conditions, when wind speeds are below 21 m/s, the NRCS reduction is small (4–5 dB) under heavy rain (>15 mm/h) conditions. The impact of rain generally decreases as wind speed increases, especially at the smallest incidence angle. At low wind speeds, the NRCS reduction is also sensitive to the incidence angle. Based on a simplified model and the differing sensitivities of NRCS to surface roughness at two near-nadir incidence angles, we find that rain also influences the surface signal at incidence angles below 10°, in addition to the dominant effect of atmospheric attenuation, which leads to a consistent NRCS reduction across all wind speeds and incidence angles. Specifically, it contributes positively to NRCS at wind speeds above 7 m/s and negatively at lower wind speeds. This behavior is attributed to increased surface roughness from splashes and ring waves at low wind speeds, and to decreased roughness due to wave damping at moderate and high wind speeds. In TC environments, the NRCS tends to saturate under light to moderate rain rates (15 mm/h) conditions, the NRCS continues to decrease as wind speed increases. In both TC and non-TC conditions, beyond atmospheric attenuation, the impact of rain on surface roughness must be considered to fully explain the observed NRCS variations with wind and rain rate.

Remote sensing images from a single sensor restrict in terms of spatial resolution and temporal resolution and they cannot simultaneously achieve high-precision and high-frequency synchronous observations. This paper proposes a spatiotemporal reflectance fusion method using double Bayesian nonparametric inferences for coupled feature space. Overcoming the limitations of existing fusion models, our approach incorporates downsampling, establishes a coupled feature space fusion model, and introduces a Beta-Bernoulli process to learn specific dictionaries. A dictionary atomic indicator ensures precise sparse projection relationships between coupled feature spaces. To address resolution disparities, a two-step Bayesian nonparametric fusion framework is employed. Experimental results demonstrate superior fusion performance, particularly in capturing surface reflectance changes in images depicting phenology or land-cover type changes. This method offers a promising solution to balance spatial and temporal resolutions, enhancing dynamic ecosystem monitoring and phenological parameter inversion.

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.

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.

The Doppler shift obtained from synthetic aperture radar (SAR) measurements comprises the combined contribution to the radial motion of the ocean surface induced by the sea state (wind waves and swell) and underlying surface currents. Hence, to obtain reliable estimates of the ocean surface current, the sea-state-induced Doppler shifts must be accurately estimated and eliminated. In this study, we use a semiempirical dual co-polarization Doppler velocity (DPDop) model, presented in the companion paper, to calculate sea-state-induced Doppler shifts using buoy-measured wind speed, wind direction, and ocean wave spectra. The DPDop model-simulated Doppler shifts are compared with the collocated Sentinel-1B SAR Wave (WV) mode observations at the 24° and 37° incidence angles, showing a bias of -0.24 Hz and a root-mean-square error (RMSE) of 5.55 Hz. This evaluation is also implemented on a simplified DPDop model at the same incidence angles. The model inputs include wind fields from the European Center for Medium-Range Weather Forecasts (ECMWF) and wave characteristic parameters (e.g., significant wave height, mean wave direction, and mean wavenumber) from WAVEWATCH III (WW3). The estimated Doppler shifts are validated using the ascending and descending observations of Sentinel-1B WV over the global ocean. Furthermore, the comparisons show that the bias and RMSE are -0.71 Hz and 9.25 Hz, respectively. Based on accurate wave bias correction, we obtain the radial current speeds of the ocean surface from the Doppler shift measurements. The estimated current speeds are compared with the collocated high-frequency radar measurements, with a bias of -0.04 m/s and an RMSE of 0.15 m/s. These results suggest that the original and simplified DPDop models can be used to estimate sea-state-induced Doppler shifts and, thus, derive accurate surface current retrievals.

This article proposes a Doppler velocity (DV) model based on dual co-polarized (co-pol) decomposition of a normalized radar cross section of an ocean surface on polarized Bragg scattering and nonpolarized (NP) radar returns from breaking wave components. The dual co-pol decomposition provides a quantitative description of resonant and NP scattering, as well as their dependence on the incident angle, azimuth, and wind speed. Subsequently, the contributions of the facet (resonant Bragg waves and breakers) velocities, tilt, and hydrodynamic modulations due to long waves to the resulting DV can be quantified. The tilt modulation contributions to DV are estimated using the measured/empirical tilt modulation transfer function (MTF). The hydrodynamic modulations are mostly dominated by wave breaking and are estimated using a semiempirical model based on in situ measurements. In addition to the VV and HH radar data, which are required for dual co-pol decomposition and tilt MTF estimates, the surface wave spectrum is required in the DV determination for a given radar observation geometry. In this article, qualitative and quantitative consistencies are presented between the model simulations and the empirical CDOP model. In a companion paper, a DV analysis is presented to analyze the Sentinel-1 synthetic aperture radar measurements and collocated in situ measurements of surface wind and wave spectra.

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.

The surface wave investigation and monitoring (SWIM) aboard the China-France Oceanography Satellite (CFOSAT), a pioneer conically scanning wave spectrometer, was successfully launched on October 29, 2018. Its innovative configuration composed of one nadir and five rotating near-nadir beams is designed to simultaneously observe the directional wave spectrum at a global scale. In this study, we systematically implement the spectral analysis of the radar backscattering with the periodogram technique to obtain the fluctuation spectrum for each azimuth direction. The 2-D fluctuation spectrum of the three spectral beams ($\theta = 6^\circ $, 8°, and 10°) combines all the azimuth directions within one entire rotation of 360°. The case study demonstrates that the wave features (peak wavelength and direction) are roughly consistent between the estimated fluctuation spectrum and the collocated WaveWatch III wave slope spectrum. A marked up-to-downwave asymmetry of the fluctuation spectrum with larger spectral level in the upwave direction for all the three spectral beams is observed. A ratio is defined between the fluctuation spectrum within the [0°, 180°] sector relative to the [180°, 360°] sector. Statistics display that this ratio is greater than 1 when it denotes the up-to-downwave ratio and smaller than 1 for the down-to-upwave ratio. This observed spectrum asymmetry is linked to the asymmetric modulation from upwind to downwind. In addition, we employ such finding to help remove the 180° wave direction ambiguity from a practical point of view. Preliminary results of the direction ambiguity removal display a bias of 41.3°, 40.6°, and 36.7° for the beams. The 10° beam shows slightly better performance compared to the other two beams in terms of bias and standard deviation. This shall lay a strong basis for the operational implementation of such algorithm to resolve the direction ambiguity.

Sea surface winds derived from the spaceborne rotating pencil-beam scatterometers, such as the current Chinese Haiyang-2 (HY-2) scatterometers and the Indian SCATSAT-1 scatterometer, are used in a wide range of atmospheric and oceanic applications. One key problem of the pencil-beam scatterometers is the relatively low wind quality in the nadir region, due to the poor azimuth diversity of the nadir-track observations. This article proposes to include the altimeter backscatter measurements in the wind inversion to improve the retrieved wind quality in the nadir region of HY-2B scatterometer (HSCAT). First, empirical model functions are developed to relate the C- and Ku-band altimeter backscatters to the neutral surface winds at 10-m height. Subsequently, the maximum likelihood estimator, which is commonly used to perform wind inversion for satellite scatterometers, is adapted to retrieve winds with simultaneous scatterometer and altimeter measurements. The significance of altimeter measurements in the combined wind inversion is addressed by comparing the cost functions of the combined scatterometer/altimeter wind retrieval and the nominal scatterometer inversion. The experimental results are eventually validated using moored buoy winds and the European Centre for Medium-range Weather Forecasting (ECMWF) model winds, indicating that the inclusion of altimeter backscatters has a positive impact on the wind quality over the HSCAT nadir region. Finally, the potential approaches to further improve the combined scatterometer and altimeter wind inversion are summarized.

The C-band synthetic aperture radar (SAR) observation is one of the most popular sources for high-resolution tropical cyclone (TC) wind speed estimation. The scarcity of high wind speed data with good quality restricts the inversion accuracy of high wind speed. It is a challenge to obtain a high-precision wind speed inversion model with sparse data points. In this paper, the TC wind speed is successfully estimated from the dual-polarization SAR signal. Firstly, the training dataset with a total of 327 data points is formed using the Sentinel-1A EW/IW mode images and their temporal-spatial matched Stepped Frequency Microwave Radiometer (SFMR) measurements. Then, a novel nonparametric TC wind speed estimation model (hereafter NWSE model) is proposed with this dataset by using the Bayesian nonparametric general regression method. The wind speed is interpreted as a function of the cross-polarized normalized radar cross-sections (VH-NRCS) and incident angle for the NWSE model. Moreover, the wind speed obtained from the co-polarized signal is used to improve the accuracy of NWSE model under low wind speed. Finally, the validation results show the excellent overall consistency between the model retrieved wind speed and the collocated SFMR and SMAP measurements. Specifically, considering all the wind speeds, the overall root-mean-square error and absolute bias of NWSE model are 2.85 m/s and 2.26 m/s compared with the SMAP wind speed. When considering the wind speeds larger than 30 m/s, the RMSE and bias of NWSE model are 3.75 m/s and 2.78 m/s, respectively.

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).

In this study, we use satellite and drifting-buoy synergistic observations to investigate the connections between tropical cyclone (TC) surface winds and currents, as well as their spatial distribution characteristics. The observed ageostrophic current speeds in ocean mixed layer (OML) increase linearly with wind speeds. The mean ratio (ageostrophic current speed to the wind speed) is around 2% in the left-front and left-rear quadrants with smaller variability, while it changes between 2% and 4% in the right-front and right-rear quadrants and shows much higher variations. Surface winds and currents both exhibit strong asymmetric features, showing larger wind speed and current velocity on the right side of the TC. The wind directions were approximately aligned with the current directions in the right-front quadrant, while a difference of about 90° was observed in the left-front and left-rear quadrants. The direction discrepency between wind and current in the right-rear quadrant was relatively small.

We present a relatively simple method to estimate tropical cyclone (TC) surface wind structure (34-, 50- and 64-kt wind radii) and intensity (maximum wind speed, MSW) from wind fields acquired from the L-band SMAP radiometer and C-band Sentinel-1A/B and RADARSAT-2 synthetic aperture radar (SAR) between 2015 and 2020. The radiometer and SAR-derived wind radii and MSW are systematically compared with the best-track estimates. The root mean square errors (RMSE) are smaller than the averaged best-track uncertainty estimates for the three wind radii. For TC Lionrock in the Northwest Pacific Ocean, our results show that a combination of the radiometer and SAR wind data acquired within a short time interval has the potential to simultaneously obtain reasonable measurements of the wind radii and intensity parameters.

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.

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.

RADARSAT-2 C-band quad-polarization ocean synthetic aperture radar (SAR) scenes are decomposed into resonant Bragg scattering from regular (no-breaking) surface and scattering from breaking waves. Analysis of the surface current signatures in dual co- and cross-pol SAR images revealed that governing imaging mechanism is modulations of wave breakings which are very sensitive to the presence of current non-uniformities. As found, due to small relaxation scale, short Bragg waves do not "feel" the current. Thus routinely observed current signatures in quad-pol SAR images originate essentially from wave breaking modulations, and modulation of Bragg waves does not matter this issue.

This study provides quantitative estimates of wind waves breaking contribution to dual co- and cross-polarized radar scattering. Extending approaches suggested by [1], [2], 1696 RADARSAT-2 quad-polarized Synthetic Aperture Radar measurements collocated with 65 in situ National Data Buoy Center buoys observations are analyzed. For VV polarization, wave breaking contribution decreases from 60% to 20% with increasing incidence angle, while for HH polarization and cross-polarization wave breaking contribution is about 60%-70% at all incidence angles. Empirical dependencies of wave breaking contributions to co- and cross-pol signals on wind speed, incidence angles and azimuth are then suggested.

We derive an asymmetry parameter using wind speeds acquired from Soil Moisture Active Passive (SMAP) satellite observations between 2015 and 2017 in Northwest Pacific Ocean. This parameter is related to the environmental vertical wind shear and the translation velocity, which is used to investigate typhoon surface wind asymmetry. It is shown that the proposed asymmetry parameter significantly increases with the increasing of the vertical wind shear, but not for the moving speed. We also analyze the relationship between the typhoon asymmetry and the intensity, showing obvious surface wind asymmetry phenomenon often exists in the weak typhoons.

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.

This paper provides improved quantitative estimates of the wind-ruffled roughness contributions to dual co- and cross-polarized radar signals. Expanding previous approaches, 1696 RADARSAT-2 quad-polarized synthetic aperture radar (SAR) measurements, co-located with 65 in situ National Data Buoy Center (NDBC) buoy observations, are analyzed. Considering all wind conditions, the impact of breaking and near-breaking waves on dual co- and cross-polarized radar signals is robustly documented. For VV polarized measurements, the contribution of breaking waves decreased from 60% to 20% with increasing incidence angle, whereas for HH polarization and cross-polarization measurements, it can amount to about 60%-70% for all incidence angles. Building on the large analyzed data set, robust empirical dependencies between breaking waves and their impact on co- and cross-pol signals are then derived, as functions of wind speeds, incidence angles, and azimuth directions.

We use multiple satellite observations and numerical simulation to investigate the upper ocean response to Typhoon Kalmaegi in 2014 in the South China Sea (SCS). In this study, significant sea surface temperature (SST) decreasing is observed, which is caused by typhoon-induced vertical mixing and upwelling. The maximum SST cooling is 2 °C on the right of the typhoon track since inertial currents rotate in the same direction as the surface wind vectors. We also find an interesting phenomenon that salinity decreases ranging between 0.3 and 0.6 psu on the left side of the typhoon track. We use numerical simulations and in-situ observations to further confirm that salinity reduction is caused by heavy rainfall.

The conventional linear modulation transfer function (MTF) was derived using HH-polarized marine radar observations in deepwater conditions. It is possible to constrain this MTF for ocean surface wave spectra retrieval in coastal shallow waters. In this letter, we propose a new MTF with both range and azimuth dependence based on VV-polarized radar measurements acquired from heterogeneous coastal wave fields. This new MTF is determined using a radar-observed image spectrum and in situ buoy-measured wave frequency spectrum. To assess the proposed MTF, we compare the buoy-measured 1-D wavenumber spectrum with those obtained using different MTFs. Compared to the conventional linear MTF, the new MTF-derived wavenumber spectrum is closer to buoy measurements. The retrieved peak and mean wave periods are also validated using concurrent wave buoy measurements. It is shown that the retrieval accuracies of peak and mean wave periods of the new MTF are better than those of the conventional MTF. The bias and root mean square errors of the peak and mean wave periods of the new MTF are 0.52 and 0.95 s and 0.26 and 0.48 s, respectively. This suggests that the proposed new MTF is more appropriate for retrieving integral wave parameters than the conventional linear MTF.

A new method is proposed to retrieve wave height from an X-band marine radar image sequence, without external measurements for reference. The X-band marine radar image sequence is first decomposed by empirical orthogonal function (EOF), and then the sea surface height profile is reconstructed and scaled from the first EOF mode. The radial profiles that are close to the peak wave direction are used to extract the zero-crossing wave periods and relative wave heights. The spectral width parameter is deduced from the histogram of a dimensionless wave period. Based on a joint probability distribution function (pdf) of a dimensionless wave period and wave height, the theoretical pdf of the wave height is derived. A shape parameter is defined for the theoretical pdf and the histogram of the relative wave heights, and then the calibration coefficient is estimated. The method is validated by comparing the significant wave heights retrieved from two different X-band marine radar systems with those measured by buoy; the correlation coefficient, the root-mean-square error, and the bias between them are 0.78, 0.51 m, and -0.19 m for HH polarization, while they are 0.77, 0.51 m, and 0.19 m for VV polarization, respectively. The sources of error of the method are discussed.

During summer 2016, the European Space Agency (ESA) set up the Satellite Hurricane Observations Campaign, a campaign dedicated to hurricane observations with Sentinel-1 synthetic aperture radar (SAR) in both vertical-vertical (VV) and vertical-horizontal (VH) polarizations acquired in wide swath modes. Among the 70 Sentinel-1 passes scheduled by the ESA mission planning team, more than 20 observations over hurricane eyes were acquired and tropical cyclones were captured at different development stages. This enables us to detail the sensitivity difference of VH and VV normalized radar cross section (NRCS) to the response of intense ocean surface winds. As found, the sensitivity of the VH-NRCS computed at 3-km resolution is reported to be more than 3.5 times larger than in VV. Taking opportunity of SAR high resolution, we also show that the decrease in resolution (up to 25 km) does not dramatically change the sensitivity difference between VV and VH polarizations. For wind speeds larger than 25 m/s, a new geophysical model function (MS1A) to interpret cross-polarized signal is proposed. Both channels are then combined to get ocean surface wind vectors. SAR winds are further compared at 40-km resolution against L-band soil moisture active and passive mission (SMAP) radiometer winds with co-locations less than 30 min. Overall excellent consistency is found between SMAP and this new SAR winds. This paper opens perspectives for MetOp-SG SCA, the next-generation C-band scatterometer with co- and cross-polarization capability.

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.

Tide has been monitored by tide-gauge stations and spaceborne radar altimeters. A new method to estimate the characteristics of coastal tide from X-band marine radar image sequences is proposed. The significant wave height (SWH) is firstly retrieved from X-band marine radar image sequence, and then filtered to remove the influences of noises. The main tide period is retrieved by analyzing the spectrum of the time series of filtered SWH. The change rate of the tide elevation can be obtained from the change rate of the amplitude of filtered SWH. The method is validated by comparing with the predictions from an ocean tide model.

The robust wind wave growth functions established with ideal fetch-limited and quasi-steady wind conditions (e.g., [11-13]; and references therein) have been observed to be applicable to wind generated waves under considerably more varying conditions, including hurricanes [1, 3, 4, 14, 15] and rapidly accelerating and decelerating wind fields such as those encountered in mountain gap winds [16-19].

Stepped-Frequency Microwave Radiometer (SFMR) measurements and Precipitation Radar (PR) data on Tropical Rainfall Measuring Mission (TRMM) are collocated to acquire the normalized radar cross sections (NRCS) at low incidence angles. At the same time, the whitecap coverages are concluded from averaging previous results. Using the formation of Kudryavtsev et al, the NRCS of whitecap is obtained. The simulation and retrieval of the sea waves with whitecap are acquired. It is indicated that the sea wave spectrum with whitecap can be corrected with the help of wind speed provided by the rotating scatterometer on the same satellite.

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.

We present a method to retrieve wind speeds in hurricanes from spaceborne passive microwave radiometer data. Brightness temperature (TB) observations acquired at the 6.9-GHz horizontal polarization channel by the AMSR-E and AMSR2 onboard the Earth Observing System Aqua and Global Change Observation Mission-Water 1 satellites are selected for wind retrieval due to the fact that the signal at this frequency is sensitive to high wind speeds but less sensitive to rain scatter than those acquired at other higher frequency channels. The AMSR-E and AMSR2 observations of 53 hurricanes between 2002 and 2014 are collected and collocated with stepped-frequency microwave radiometer (SFMR) measurements. Based on the small slope approximation/small perturbation method model and an ocean surface roughness spectrum, the wind speeds are retrieved from the TBdata and validated against the SFMR measurements. The statistical comparison of the entire data set shows that the bias and root-mean-square error (RMSE) of the retrieved wind speeds are 1.11 and 4.34 m/s, respectively, which suggests that the proposed method can obtain high wind speeds under hurricane conditions. Two case studies show that the wind speed retrieval bias and RMSE are 1.08 and 3.93 m/s for Hurricane Earl and 0.09 and 3.23 m/s for Hurricane Edouard, respectively. The retrieved wind speeds from the AMSR-E and AMSR2 continuous three-day observations clearly show the process of hurricane intensification and weakening.

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.

A new algorithm to retrieve wave parameters, such as significant wave height (SWH), peak wave period, wavelength, and wave direction, from marine X-band radar image sequences is proposed. To analyze the heterogeneous nearshore wave field, the empirical orthogonal function is used to extract the principal components (PCs) of the radar image sequence. To retrieve the SWH, a linear relationship between SWH and the standard deviation of PC is established. The maximum entropy power spectral density (PSD) of the first PC is used to derive the peak wave period. The PSD on spatial scale is used to estimate two perpendicular components of the wavelength, and then, the wavelength and wave direction are determined by geometric relationship. Inversion schemes are validated by comparing with data from the in situ buoy; the root-mean-square error between the wave parameters retrieved from radar image sequences and measured by the buoy is 0.21 m for SWH and 0.84 s for the peak wave period, and the biases for these are 0.01 m and 0.33 s.

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.

In this Paper presentation, compare the normal Radar backscatter coefficient between oil spill area, clean sea area, Ship, platform area and Enteromorpha area. The result display the backscatter coefficient of oil spill area is lower than clean sea and the Enteromorpha area, ship, platform area is higher than clean sea.

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.

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].

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.

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.

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.

A new ocean wave retrieval method for the along-track interferometric synthetic aperture radar (ATI-SAR) phase image is presented. The new algorithm, named parametric retrieval algorithm (PRA), uses the full nonlinear mapping relations as proposed by He and W. Alpers (2003). It differs from previous retrieval algorithms in that it does not requireaprioriinformation on the sea state and also calibration of the ATI-SAR phase image. The advantage of PRA is that it jointly retrieves information of wave parameters and wind speed. The method has been validated by collocating two X-band HH-polarized ATI-SAR phase image and one C-band HH-polarized ATI-SAR phase image spectra with spectral buoy measurements. The wave parameters and wind speed estimated from PRA are in agreement well with buoy observations. The proposed method can be employed by future satellite missions such as TerraSAR.

A neural network wind speed (WS) retrieval algorithm was developed using 6.9 and 10.7, as well as 36.5 GHz for both horizontal and vertical polarized brightness temperature (Tb) of Advanced Microwave Scanning Radiometer-E (AMSR-E) aboard AQUA. The artificial xneural networks (ANN) technique is employed to find the transfer function relating the input AMSR-E six channels Tb and output (WS) parameter. Input data consist of nearly 12 months (January 2005 - December 2005) of AMSR-E observed brightness temperature and surface marine observations of WS from National Data Buoy Center (NDBC) and Tropical Atmosphere Ocean Project (TAO). The performance of the algorithm is assessed with independent surface marine observations. The retrieval results demonstrate that the combination bright temperatures of lower frequencies such as 6.9 and 10.7 GHz, as well as higher frequencies such as 36.5 GHz from AMSR-E as input parameters provides reasonable estimates of wind speed. The root mean square (rms) error between estimated WS from AMSR-E observations and the buoy measurements is 1.53 m/s.

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.

A new method is presented to retrieve near-sea surface instantaneous air temperature and dew point temperature. Satellite data include the air temperature and point temperature at 1000 mb are inversed by TOVS data. Sea surface temperature is derived from AVHRR data. Ship-measured data come from the observations of NEAR-GOOS (North-East Asia Regional- Global Ocean observing) experiment. Then two different artificial neural network models are developed to retrieve near- sea surface instantaneous air temperature and dew point temperature. For the single parameter neural network model (SANN) model, the root mean square (RMS) error of near-sea surface instantaneous air temperature and dew point temperature are 1.85degC and 2.59degC respectively, but those for multi-parameter neural network (MANN) model are 1.92degcand 2.67degC respectively. The results indicate that the retrieval accuracy of the SANN model is superior to that of the MANN model and prove that there is a close relationship between near- sea surface air temperature and dew point temperature.