Synthetic Aperture Radar Wave Mode Research Articles

A Two-Stage Strategy for Retrieving Two-Dimensional Ocean Wave Spectra from Chinese Gaofen-3 SAR Wave Mode Products

A Two-Stage Strategy for Retrieving Two-Dimensional Ocean Wave Spectra from Chinese Gaofen-3 SAR Wave Mode Products

Optimized Estimation of Azimuth Cutoff for Retrieval of Significant Wave Height and Wind Speed From Polarimetric Gaofen-3 SAR Wave Mode Data

Optimized Estimation of Azimuth Cutoff for Retrieval of Significant Wave Height and Wind Speed From Polarimetric Gaofen-3 SAR Wave Mode Data

Comparison of BPNN and Dual-Branch CNN for Significant Wave Height Estimation From Polarimetric Gaofen-3 SAR Wave Mode Data

Comparison of BPNN and Dual-Branch CNN for Significant Wave Height Estimation From Polarimetric Gaofen-3 SAR Wave Mode Data

Polarization-Enhancement Effects for the Retrieval of Significant Wave Heights from Gaofen-3 SAR Wave Mode Data

In order to investigate the impact of utilizing multiple pieces of polarization information on the performance of significant wave height (SWH) estimation from Gaofen-3 SAR data, the extreme gradient boosting (XGBoost) models were developed, validated, and compared across 9 single-polarizations and 39 combined-polarizations based on the collocated datasets of Gaofen-3 SAR wave mode imagettes matched with SWH data from ERA5 reanalysis as well as independent SWH observations from buoys and altimeters. The results show that the performance of our SWH inversion models varies across the nine different single-polarizations. The co-polarizations (HH, VV, and RL) and hybrid-polarizations (45° linear, RH, and RV) generally exhibit superior performance compared to the cross-polarizations (HV, VH, and RR) at low to moderate sea states, while the cross-polarizations are more advantageous for high SWH estimation. The combined use of multiple pieces of polarization information does not always improve the model performance in retrieving SWH from Gaofen-3 SAR. Only the polarization combinations that incorporate cross-polarization information have the potential to enhance the model performance. In these cases, the performance of our models consistently improves with the incorporation of additional polarization information; however, this improvement diminishes gradually with each subsequent polarization and may eventually reach a saturation point. The optimal estimation of SWH is achieved with the polarization combination of HV + VH + RR + RH + RV + 45° linear, which shows consistently lower RMSEs compared to ERA5 SWH (0.295 m), buoy SWH (0.273 m), Cryosat-2 SWH (0.109 m), Jason-3 SWH (0.414 m), and SARAL SWH (0.286 m). Nevertheless, it still exhibits a slight overestimation at low sea states and a slight underestimation at high sea states. The inadequate distribution of data may serve as a potential explanation for this.

Evaluation of Multi-Incidence Angle Polarimetric Gaofen-3 SAR Wave Mode Data for Significant Wave Height Retrieval

Significant wave height (SWH) is one of the most important descriptors for ocean wave fields. The polynomial regression (PolR) and Gaussian process regression (GPR) models are implemented to explore the effects of polarization and incidence angles on the SWH estimation from multi-incidence angle quad-polarization Gaofen-3 SAR wave mode data, based on the collocated data set of approximately 12,000 Gaofen-3 wave mode imagettes, matched with SWH from the fifth generation reanalysis (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The results show that the model performance improves, as long as polarimetry information increases. The hybrid polarizations perform stronger than the co-polarizations or cross-polarizations alone, and they show better performance over the low to high seas. The lower incidence angles are more favorable for SAR SWH inversion. It is superior to introduce incidence angle in piecewise way, rather than to include it as an independent variable in the models. Then, the final PolR and GPR models, with the superior input scheme that includes the quad-polarized features and introduces the incidence angle in piecewise way, are assessed independently through a comparison with observations from altimeter and buoys. The accuracies of our SWH estimates are comparable or even higher than other published results. The GPR model outperforms the PolR model, due to the superiority of the added nonlinearity of GPR.

Automated Global Classification of Surface Layer Stratification Using High‐Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar

AbstractA three‐state global estimator of marine surface layer atmospheric stratification is demonstrated using more than 600,000 Sentinel‐1 synthetic aperture radar wave mode images at incidence angle ≈36.8°. Stratification is quantified using a bulk Richardson number, Ri, derived from collocated ERA5 surface analyses. The three stratification states are defined as unstable: Ri < −0.012, near‐neutral: −0.012 < Ri < +0.001, and stable: Ri > +0.001. These boundaries are identified by the characteristic boundary layer coherent structures that form in these regimes and modulate the surface roughness imaged by the radar. An automated machine learning algorithm identifies the coherent structures impressed on the images. Data from 2016 to 2019 are used to examine spatial and temporal variation in these state estimates in terms of expected wind and thermal forcing. This new satellite‐based approach for detecting air‐sea stratification has implications for weather modeling and air‐sea flux products.

Impact of Polarization Basis on Wind and Wave Parameters Estimation Using the Azimuth Cutoff From GF-3 SAR Imagery

The azimuth cutoff wavelength of SAR is an important parameter for retrieval of sea surface wind and wave. Earlier studies have fully demonstrated the substantial dependence of azimuth cutoff wavelength on polarization, but the present studies only focus on H-V linear polarization bases (HH, HV/VH, and VV) without considering the effects of other polarization bases (e.g., linear rotated, circular, and elliptical polarization). Benefiting from the quad-polarization advantage of GaoFen-3 SAR wave mode data and the support of polarization basis transformation theory, this study used 4,648 SAR data to study the correlation between cutoff wavelength and wind and wave parameters (e.g., significant wave height, and wind speed) under different polarization bases, and analyzed the variation of correlation coefficient caused by polarization basis change. Finally, the results were applied to evaluating the performance of wind and wave parameters retrieval. The results of the study show that the azimuth cutoff is strongly dependent on the polarization state of electromagnetic wave. The azimuth cutoff wavelength under the elliptical polarization bases has higher correlation with wind and wave than that under H-V linear, circular, and linear rotated polarization bases. Using the azimuth cutoff wavelength of the elliptical polarization bases can significantly improve the retrieval accuracy of wind and wave parameters. This study shall enhance the capabilities of polarized SAR systems to precisely derive more ocean surface properties. The result implies that polarization basis is an important factor that must be considered in future ocean SAR studies.

Indian Ocean Crossing Swells: New Insights from “Fireworks” Perspective Using Envisat Advanced Synthetic Aperture Radar

Synthetic Aperture Radar (SAR) in wave mode is a powerful sensor for monitoring the swells propagating across ocean basins. Here, we investigate crossing swells in the Indian Ocean using 10-years Envisat SAR wave mode archive spanning from December 2003 to April 2012. Taking the benefit of the unique “fireworks” analysis on SAR observations, we reconstruct the origins and propagating routes that are associated with crossing swell pools in the Indian Ocean. Besides, three different crossing swell mechanisms are discriminated from space by the comparative analysis between results from “fireworks” and original SAR data: (1) in the mid-ocean basin of the Indian Ocean, two remote southern swells form the crossing swell; (2) wave-current interaction; and, (3) co-existence of remote Southern swell and shamal swell contribute to the crossing swells in the Agulhas Current region and the Arabian Sea.

A labelled ocean SAR imagery dataset of ten geophysical phenomena from Sentinel‐1 wave mode

AbstractThe Sentinel‐1 mission is part of the European Copernicus program aiming at providing observations for Land, Marine and Atmosphere Monitoring, Emergency Management, Security and Climate Change. It is a constellation of two (Sentinel‐1 A and B) Synthetic Aperture Radar (SAR) satellites. The SAR wave mode (WV) routinely collects high‐resolution SAR images of the ocean surface during day and night and through clouds. In this study, a subset of more than 37,000 SAR images is labelled corresponding to ten geophysical phenomena, including both oceanic and meteorologic features. These images cover the entire open ocean and are manually selected from Sentinel‐1A WV acquisitions in 2016. For each image, only one prevalent geophysical phenomenon with its prescribed signature and texture is selected for labelling. The SAR images are processed into a quick‐look image provided in the formats of PNG and GeoTIFF as well as the associated labels. They are convenient for both visual inspection and machine learning‐based methods exploitation. The proposed dataset is the first one involving different oceanic or atmospheric phenomena over the open ocean. It seeks to foster the development of strategies or approaches for massive ocean SAR image analysis. A key objective was to allow exploiting the full potential of Sentinel‐1 WV SAR acquisitions, which are about 60,000 images per satellite per month and freely available. Such a dataset may be of value to a wide range of users and communities in deep learning, remote sensing, oceanography and meteorology.

Calibration of the Copolarized Backscattering Measurements From Gaofen-3 Synthetic Aperture Radar Wave Mode Imagery

Accurate absolute radiometric calibration of spaceborne synthetic aperture radar (SAR) sensor is of great importance in quantitative oceanic monitoring. Traditionally, the calibration constant is determined by analyzing measurements of man-made calibrators deployed on land at a high expense. In this paper, a technique called numerical weather prediction (NWP)-based ocean calibration method for estimating the calibration constant on the basis of SAR observations over ocean is applied to the first Chinese C-band SAR satellite Gaofen-3 (GF-3). The ocean calibration is performed on GF-3 wave mode SAR images at VV and HH polarizations over a period of one year from September 2017 to August 2018. Verification against the independent scatterometer-derived winds shows that after NWP-based ocean recalibration, the accuracy of wind speed retrieval from GF-3 wave mode imagery could reach the wind speed estimation performance using the state-of-the-art of SAR data. These results indicate that GF-3 wind retrievals are promising for operational oceanic products and applications if the SAR data are appropriately calibrated by the proposed NWP-based ocean calibration approach.

Typhoon/Hurricane-Generated Wind Waves Inferred from SAR Imagery

The wide-swath mode of synthetic aperture radar (SAR) is a good way of detecting typhoon/hurricane winds with a cross-polarization mode. However, its ability to detect wind waves is restricted because of its spatial resolution and nonlinear imaging mechanisms. In this study, we use the SAR-retrieved wind speed, Sentinel-1 SAR wave mode and buoy data to examine fetch- and duration-limited parametric models (denoted H-models), to estimate the wave parameters (significant wave height Hs, dominant wave period Tp) generated by hurricanes or typhoons. Three sets of H-models, in total 6 models, are involved: The H-3Sec model simulates the wave parameters in 3 sections of a given storm (right, left and back); H-LUT models, including the H-LUTI model and H-LUTB model, provide a better resolution of the azimuthal estimation of wind waves inside the storm by analyzing the dataset from Bonnie 1998 and Ivan 2004; and the third set of models is called the H-Harm models, which consider the effects of the radius of the maximum wind speed rm on the wave simulation. In the case of typhoon Krovanh, the comparison with wave-mode measurements shows that the duration-limited models underestimate the high values for the wind-wave Hs, while the fetch models’ results are more accurate, especially for the H-LUTI model. By analyzing 86 SAR wave mode images, it is found that the H-LUTI model is the best among the 6 H-models, and can effectively simulate the wind-wave Hs, except in the center area of the typhoon; root mean square errors (rmse) can reach 0.88 m, and the coefficient correlation (R2) is 0.86. The H-Harm models add rm as an additional factor to be considered, but this does not add significant improvement in performance compared to the others. This limitation is probably due to the fact that the data sets used to develop the H-Harm models have only a limited coverage range, with respect to rm. Applying H-models to RADARSAT-2 ScanSAR mode data, we compare the retrieved wave parameters to collected buoy measurements, showing good consistency. The H-LUTI model, using a fetch-limited function, does the best among these 6 H-models, whose rmse and R2 are 0.86 m and 0.77 for Hs, and 1.06 s and 0.76 for Tp, respectively. Results indicate the potential for H-models to simulate waves generated by typhoons/hurricanes.

Identifying storm-induced wave origins using SAR wave mode data

Because of weak dissipation effects, swells generated by fierce storms can propagate across an entire ocean basin; therefore, observing swell generation and decay and retrieving storm characteristics from a swell by satellite remote sensing are possible. In this study, based on the dispersion relation and geometrical optics principle, we used SAR wave mode data from 2003 to 2010 provided by GlobWave to track swells with peak wavelengths of more than 300 m to locate a storm-generated far-traveling swell and present the swell field related to this “static” origin. Through a comparison with ECMWF wind datasets, we conducted validations and explored some conditions that cause misjudgments in swell origins. Finally, we obtained the spatiotemporal distribution characteristics of satellite-observed swell origins (i.e., the fierce wind condition) and their evolution. This work can be used as a reference for wave models, providing early swell warnings, determining air-sea surface interactions, and determining global climate change.

Fuzzy Logic Applied to Track Generation Areas of Swell Systems Observed by SAR

Recently, with the availability of a great number of synthetic aperture radar (SAR) wave mode spectra, it has been possible to derive a set of great circle lines of swell propagation whose intersection points indicate the position of the storm generating the observed swell field. However, due to the inherent limitations of SAR spectra, the locus of convergence of great circle of swell propagation can be sometimes diffuse or contain multiple convergence regions. In this letter, we adapted the fuzzy cluster logic method to identify the regions of convergence of SAR wave field rays. The analysis of the results of the fuzzy algorithm clearly indicates the ability of this statistical method to identify the cluster center region of swell fields observed in SAR wave mode images. The measure of success of the method was how well the generation center of the swell could be traced back to an existing strong storm system.

Revealing forerunners on Envisat's wave mode ASAR using the Global Seismic Network

Swells radiating across ocean basins are fingerprints of the large ocean storms that generated them, which are otherwise poorly observed. Here we analyze the signature of one swell event in the seismic noise recorded all around the Pacific and we show that it is a natural complement to the global coverage provided by the Synthetic Aperture Radar wave mode data from ENVISAT. In particular the seismic stations are much more sensitive to low frequency and amplitude signals than buoys and SAR, capturing swell forerunners a couple of days before they can be detected from space or in situ data. This information helps detect in the SAR measurements the presence of very long swell, with periods of 22 s in our case example, that were otherwise excluded.

Ocean Wave Integral Parameter Measurements Using Envisat ASAR Wave Mode Data

An empirical algorithm to retrieve integral ocean wave parameters such as significant wave height (SWH), mean wave period, and wave height of waves with period larger than 12 s <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$({H}_{12})$ </tex></formula> from synthetic aperture radar (SAR) images over sea surface is presented. The algorithm is an extension to the Envisat Advanced SAR (ASAR) wave mode data based on the CWAVE approach developed for ERS-2 SAR wave mode data and is thus called CWAVE_ENV (CWAVE for Envisat). Calibrated ASAR images are used as the only source of input without needing prior information from an ocean wave model (WAM) as the standard algorithms used in weather centers. This algorithm makes SAR an independent instrument measuring integrated wave parameters like SWH and mean wave period to altimeter quality. A global data set of 25 000 pairs of ASAR wave mode images and collocated reanalysis WAM results from the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to tune CWAVE_ENV model coefficients. Validation conducted by comparing the retrieved SWH to in situ buoy measurements shows a scatter index of 0.24 and 0.16 when compared to the ECMWF reanalysis WAM. Two case studies are presented to evaluate the performance of the CWAVE_ENV algorithm for high sea state. A North Atlantic storm during which SWH is above 18 m as observed by SAR and Radar Altimeter simultaneously is analyzed. For an extreme swell case that occurred in the Indian Ocean, the potential of using SWH measurements from ASAR wave mode data derived by the CWAVE_ENV algorithm is demonstrated.

On the retrieval of significant wave heights from spaceborne Synthetic Aperture Radar (ERS-SAR) using the Max-Planck Institut (MPI) algorithm

Synthetic Aperture Radar (SAR) onboard satellites is the only source of directional wave spectra with continuous and global coverage. Millions of SAR Wave Mode (SWM) imagettes have been acquired since the launch in the early 1990's of the first European Remote Sensing Satellite ERS-1 and its successors ERS-2 and ENVISAT, which has opened up many possibilities specially for wave data assimilation purposes. The main aim of data assimilation is to improve the forecasting introducing available observations into the modeling procedures in order to minimize the differences between model estimates and measurements. However there are limitations in the retrieval of the directional spectrum from SAR images due to nonlinearities in the mapping mechanism. The Max-Planck Institut (MPI) scheme, the first proposed and most widely used algorithm to retrieve directional wave spectra from SAR images, is employed to compare significant wave heights retrieved from ERS-1 SAR against buoy measurements and against the WAM wave model. It is shown that for periods shorter than 12 seconds the WAM model performs better than the MPI, despite the fact that the model is used as first guess to the MPI method, that is the retrieval is deteriorating the first guess. For periods longer than 12 seconds, the part of the spectrum that is directly measured by SAR, the performance of the MPI scheme is at least as good as the WAM model.

Assessment of ERS synthetic aperture radar wave spectra retrieved from the Max‐Planck‐Institut (MPI) scheme through intercomparisons of 1 year of directional buoy measurements

One year of directional buoy measurements comprising the period from May 1994 to April 1995 acquired in deep ocean waters by an offshore heave‐pitch‐roll buoy are used for the assessment of the directional wave spectra retrieved from synthetic aperture radar (SAR) images using the Max‐Planck‐Institut (MPI) scheme. SAR is the only sensor so far deployed from satellites that can provide measurements of the directional wave spectrum with high spatial and temporal coverage when operating in the so‐called SAR wave mode. Millions of SAR wave mode imagettes have been and are still being acquired over all oceanic basins yielding a powerful data set for investigating wind waves. However, directional spectral information retrieved from SAR images has not yet been assessed against in situ measurements. For the first time, detailed validations of the main wave parameters, that is, significant wave height, mean direction of propagation, and mean wavelength, are performed. It is shown that in terms of these parameters the first‐guess spectra taken from the wave model WAM are in better agreement with the buoy measurements than the MPI scheme retrievals. When considering only the longer waves in the part of the spectrum observed by SAR, on the other hand, the algorithm performs at least as well as the third‐generation WAM wave model. In addition to the limitations of the MPI scheme in extending the spectral information beyond the high wave number cut‐off, an observed misinterpretation of wind sea energy as swell by the MPI scheme is shown to be caused by the use of a quasi‐linear approximation of the imaging model in the numerical iteration procedure.

Global wind speed retrieval from sar

The global availability of synthetic aperture radar (SAR) wave mode data from the European Remote Sensing (ERS) satellites ERS-1 and ERS-2, as well as ENVISAT, allows for the investigation of the wind field over the ocean on a global and continuous basis. For this purpose, 27 days of ERS-2 SAR wave mode data were processed, representing a total of 34310 imagettes of size 10 km /spl times/5 km, available every 200 km along the satellite track. In this paper, two methods for retrieving wind speeds from SAR imagettes are presented and validated, showing the applicability of ENVISAT alike SAR wave mode data for global ocean wind retrieval. The first method is based on the well-tested empirical C-band scatterometer (SCAT) models, which describe the dependency of the normalized radar cross section (NRCS) on wind speed and direction. To apply C-band models to SAR data, the NRCS needs to be accurately calibrated. This is performed by a new efficient method utilizing a subset of colocated measurements from ERS-2 SCAT and model winds from the European Centre for Medium-Range Weather Forecast (ECMWF). SAR wind speeds are computed from the calibrated imagettes and compared to the entire set of colocated ERS-2 SCAT and ECMWF model data. Comparison to ERS-2 SCAT winds result in a correlation of 0.95 with a bias of -0.01 m s/sup -1/ and an rms error of 1.0 m s/sup -1/. The second approach is based on neural networks (NNs), which allow the retrieval of wind speeds from uncalibrated SAR imagettes. NNs are trained using the mean intensity of ERS-2 SAR imagettes and colocated wind data from the ERS-2 SCAT and ECMWF model data. Validation of the NN-retrieved SAR wind speeds to ERS-2 SCAT and ECMWF model wind data result in a correlation of 0.96 with a bias of -0.04 m s/sup -1/ and an rms error of 0.93 m s/sup -1/.

A semiparametric algorithm to retrieve ocean wave spectra from synthetic aperture radar

A new wave retrieval method for the ERS synthetic aperture radar(SAR) wave mode is presented. The new algorithm, named semiparametric retrieval algorithm (SPRA), uses the full nonlinear mapping relations as proposed by Hasselmann and Hasselmann [1991]. It differs from previous retrieval algorithms in that it does not require a priori information on the sea state. Instead, it combines the observed SAR spectrum with the collocated wind vector from the ERS scatterometer to make an estimate of the wind sea spectrum. The residual signal in the SAR spectrum is interpreted as swell. The method has been validated by collocating over 5 years of SAR wave mode observations with spectral buoy measurements at 11 locations on the open ocean. For wave components longer than 225 m, the standard deviation between the retrieved spectra and buoy observations is 0.41 m, which corresponds to a relative RMS error of 29%. About 10% of the observed SAR spectra were rejected, in particular in light wind conditions when nonwave features such as those caused by slicks dominated the imagette. The bias and scatter in the results obtained under light wind conditions could be reduced by introducing a wind‐dependent tilt modulation. This wind‐dependent tilt formulation is derived from the empirical CMOD4 relation between the wind vector, the incidence angle, and the radar backscatter for the ERS scatterometer.

Annual validation of significant wave heights of ERS‐1 synthetic aperture radar wave mode spectra using TOPEX/Poseidon and ERS‐1 altimeter data

Significant wave heights retrieved globally during 1994 from low bit rate imagette spectra of the synthetic aperture radar (SAR) operating in the intermittent wave mode (SWM) onboard the first European Remote Sensing (ERS‐1) satellite (Hsswm) are validated using independent, buoy‐validated satellite altimeter data from TOPEX/Poseidon (Hstop) and ERS‐1 (Hsers). Hsswm is retrieved using the extended inversion algorithm of the fully nonlinear wave‐to‐SAR spectral integral transform [Hasselmann et al., 1996]. The statistical comparison shows that globally retrieved Hsswm agree remarkably well with collocated data of both altimeters. Histograms of global Hsswm are well approximated by the universal lognormal distribution function. A small but systematic underestimation of Hsswm with respect to both altimeters by 0.1 m is noticed. This bias is small compared to the uncorrelated root‐mean‐square (rms) deviation of 0.5 m. The underestimation originates from underestimations of the high sea states dominated by wind sea. The entire range of swell waves of Hsswm, however, corresponds very closely to Hstop. Thus the high accuracy of SWM‐retrieved swell wave heights substantiates their suitability for model validation and for operational wave data assimilation. To achieve also reliable estimates of the high sea states, the combined analysis of wave and wind data from both modeling and observation is the most promising approach.