Parameterized first-guess spectrum method for retrieving directional spectrum of swell-dominated waves and huge waves from SAR images

Sun, Jian; Guan, Changlong

doi:10.1007/bf02842769

Cited by 42 publications

(11 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To obtain H s from C-band SAR [Hasselmann and Hasselmann, 1991;Mastenbroek and de Valk, 2000;Sun and Guan, 2006;Schulz-Stellenfleth et al, 2007;Zhang et al, 2012Zhang et al, , 2015, one must know the local wind speed. Moreover, the empirical models [e.g., Li et al, 2011;Shao et al, 2016] are not applicable for hurricanes due to unavailable data in extreme sea state used in the algorithm tuning processes.…”

Section: Relationship Among Wind-wave Triplets In Tropical Cyclonesmentioning

confidence: 99%

“…Therefore, wind speed can be retrieved by using the wave information (H s or T p ). Recent studies show that C-band SAR H s retrievals from either empirical-based [e.g., Li et al, 2011;Shao et al, 2016] or theoretical-based algorithms [Hasselmann and Hasselmann, 1991;Mastenbroek and de Valk, 2000;Sun and Guan, 2006;Schulz-Stellenfleth et al, 2007;Zhang et al, 2015] are within 20% error range against buoy measurements or numerical wave model results. However, it is still a challenge to retrieve accurate H s under tropical cyclones.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Shao

Hwang

et al. 2017

JGR Oceans

View full text Add to dashboard Cite

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

show abstract

Section: Relationship Among Wind-wave Triplets In Tropical Cyclonesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Shao

Hwang

et al. 2017

JGR Oceans

View full text Add to dashboard Cite

show abstract

“…Additionally, Mastenbroek et al [12] introduced a semiparametric algorithm (SPRA) that incorporates wind scatterometer data as supplementary inputs. In a distinct approach, Sun and Guan [13] developed the Parameterized First-guess Spectrum Method (PFSM) to separately retrieve the wind-sea component and the linear-mapping swell. Unlike MPI-based methods, Engen and Johnsen [14] utilized SAR cross spectra for wave spectrum inversion.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Strategy for Retrieving 2-D Ocean Wave Spectra From Chinese Gaofen-3 SAR Wave Mode Products

Fang,

Fan,

Cao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Synthetic Aperture Radar (SAR) is widely used for observing sea surfaces and retrieving two-dimensional wave spectra. However, existing methods for retrieving directional wave spectra from SAR imagettes face challenges due to the complex non-linear SAR-wave imaging relationship and the limitation of first-guess spectra. This study proposes a novel twostage machine learning strategy for retrieving two-dimensional directional wave spectra from Chinese Gaofen-3 SAR wave mode products. We achieve the generation of complete two-dimensional wave spectra and several wave parameters solely from GF-3 SAR data without necessitating any additional inputs. In the first stage, we employ the Energy Attention Conditional Generative Adversarial Network (EA-CGAN) to retrieve the normalized wave spectrum. The generator of the EA-CGAN establishes a non-linear transformation from normalized SAR cross spectra to normalized wave spectra to enhance the capabilities. In the second stage, the XGBoost model retrieves the intensity of the wave spectrum. The EA-CGAN and XGBoost models were trained on an extensive dataset that consists of about 11,000 Gaofen-3 SAR wave mode imagettes and two-dimensional wave spectra from the fifth-generation reanalysis (ERA-5) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The results of the evaluation using test samples reveal high consistency between the retrieved wave spectra and ERA-5 wave spectra in terms of spectral similarity, peak period, peak direction, significant wave height, and mean wave periods. Compared to the traditional methods, our approach offers enhanced effectiveness, demonstrating the potential of advanced deep learning in highprecision SAR wave spectrum inversion.

show abstract

“…Traditionally, two methods are used to invert waves from an SAR intensity image: theoretically based algorithms and empirical models. The methodology of the theoretically based algorithm is exploited through the SAR wave mapping mechanism [21], e.g., the Max-Planck-Institut (MPI) algorithm [22], the semi-parametric retrieval algorithm (SPRA) [23], the partition rescaling and shift algorithm (PARSA) [24], the parameterized first-guess spectrum method (PFSM) (e.g., [25,26]) and the quad-polarized technique [27]. In order to deal with the complex modulation transfer function (MTF) of the SAR mapping mechanism, empirical models have also been developed, e.g., CWAVE_ERS for ERS-1/2 [28], CWAVE_ENVI for ENVISAT-ASAR [29], CWAVE_S1 for S-1 [30,31] and CSAR_WAVE for GF-3 [32].…”

Section: Introductionmentioning

confidence: 99%

Validation of Surface Waves Investigation and Monitoring Data against Simulation by Simulating Waves Nearshore and Wave Retrieval from Gaofen-3 Synthetic Aperture Radar Image

Hao,

Shao,

Shi

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The Chinese-French Oceanography SATellite (CFOSAT) jointly developed by the Chinese National Space Agency (CNSA) and the Centre National d’Etudes Spatiales (CNES) of France carries a wave spectrometer (Surface Waves Investigation and Monitoring, SWIM). SWIM has one nadir and five off-nadir beams to measure ocean surface waves. These near-nadir beams range from 0° to 10° at an interval of 2°. In this work, we investigated the performance of wave parameters derived from wave spectra measured by SWIM at off-nadir beams during the period 2020 to December 2022, e.g., incidence angles of 6°, 8° and 10°, which were collocated with the wave simulated by Simulating Waves Nearshore (SWAN). The validation of SWAN-simulated significant wave heights (SWHs) against National Data Buoy Center (NDBC) buoys of National Oceanic and Atmospheric Administration (NOAA) exhibited a 0.42 m root mean square error (RMSE) in the SWH. Our results revealed a RMSE of 1.02 m for the SWIM-measured SWH in the East Pacific Ocean compared with the SWH simulated by SWAN, as well as a 0.79 correlation coefficient (Cor) and a 1.17 squared error (Err) for the wave spectrum at an incidence angle of 10°, which are better than those (i.e., the RMSEs were > 1.1 m with Cors < 0.76 and Errs > 1.2) achieved at other incidence angles of SWH up to 14 m. This analysis indicates that the SWIM product is a relevant resource for wave monitoring over global seas. The collocated wave retrievals for more than 300 cases from Gaofen-3 (GF-3) synthetic aperture radar (SAR) images in China Seas were also used to verify the accuracy of SWIM-measured wave spectra. The energy of the SWIM-measured wave spectra represented by SWH was found to decrease with an increasing incidence angle in a case study. Moreover, the SWIM-measured wave spectra were most consistent with the SAR-derived wave spectra at an incidence angle of 10°, yielding a 0.77 Cor and 1.98 Err between SAR-derived and SWIM wave spectra under regular sea state conditions (SWH < 2 m). The error analysis indicates that the difference in SWH between SWIM at an incidence angle of 10° and SWAN has an increasing tendency with the growth in sea surface wind and sea state and it stabilizes to be 0.6 m at SWH > 4 m; however, the current and sea level have less influence on the uncertainties of the SWIM product.

show abstract

Parameterized first-guess spectrum method for retrieving directional spectrum of swell-dominated waves and huge waves from SAR images

Cited by 42 publications

References 10 publications

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

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

A Two-Stage Strategy for Retrieving 2-D Ocean Wave Spectra From Chinese Gaofen-3 SAR Wave Mode Products

Validation of Surface Waves Investigation and Monitoring Data against Simulation by Simulating Waves Nearshore and Wave Retrieval from Gaofen-3 Synthetic Aperture Radar Image

Contact Info

Product

Resources

About