Wind speed retrieval from the Gaofen-3 synthetic aperture radar for VV- and HH-polarization using a re-tuned algorithm

Shao, Weizeng; Nunziata, Ferdinando; Zhang, Youguang; Corcione, Valeria; Migliaccio, Maurizio

doi:10.1080/22797254.2021.1924082

Cited by 23 publications

(5 citation statements)

References 42 publications

(58 reference statements)

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“…10). SAR-derived wind direction is used in the CSARMOD-GF for re-calibrated GF-3 data to invert the wind speed [55]. Fig.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-Based Wind Direction Retrieval From Quad-Polarized Gaofen-3 SAR Images

Shao,

Zhou,

Zhang

et al. 2024

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

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In our study, an intelligent method for inverting wind direction from quad-polarized Gaofen-3 (GF-3) synthetic aperture radar (SAR) images is proposed. Specifically, 11300 acquired in wave (WAV) mode are used to retrieve the wind directions using a spectrum-transformation approach and prior information from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis at version 5 (ERA-5) data at 1-hour intervals with 0.25° grids. The dependence of the wind direction on the polarimetric correlation coefficient (PCC) between the co-(vertical-vertical (VV) and horizontal-horizontal (HH)) and cross-polarization (vertical-horizontal (VH) and horizontal-vertical (HV)) channels is studied. It is found that the PCCs in four combination polarizations have asymmetric characteristics with respect to the wind direction with correlation coefficients (CORs) of greater than 0.4 or less than -0.4. Following this rationale, the scheme for inverting wind direction from quad-polarized SAR is trained according to machine learning, in which the matrix PCCs, wind directions, azimuthal angles, and slopes from SAR intensity spectra at the peaks are used as inputs. Subsequently, this intelligent approach is applied to 1300 images in quad-polarization stripmap (QPS) mode, and the retrieval results are validated against advanced scatterometer (ASCAT) measurements. The statistical analysis shows that the root mean squared error (RMSE) of the wind direction is 17.7°, the COR is 0.98, and the scatter index (SI) is 0.11. In addition, the wind speeds inverted using a geophysical model function (GMF) CSARMOD-GF are compared with well-calibrated ASCAT products, resulting in an RMSE of 1.85 m/s, a COR of 0.78, and an SI of 0.28 for the wind speed. Thus, this work provides an automatic scheme for inverting wind from quad-polarized GF-3 SAR images without any external information.

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“…10). SAR-derived wind direction is used in the CSARMOD-GF for re-calibrated GF-3 data to invert the wind speed [55]. Fig.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-Based Wind Direction Retrieval From Quad-Polarized Gaofen-3 SAR Images

Shao,

Zhou,

Zhang

et al. 2024

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

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“…where g is the gravitational acceleration of 9.8 m/s 2 ; V is the satellite flight velocity, assumed to be 7600 m/s for the GF-3 SAR; R is the satellite slant range in m; U 10 is the SAR-derived sea surface wind speed at a height of 10 m in m/s; θ is the radar incidence angle in degrees; and ϕ is the angle of the wave propagation direction relative to the radar look direction in degrees. It should be noted that U 10 was retrieved using the geophysical model function (GMF) CSARMOD-GF specially designed for re-calibrated images [43], which describes an empirical relationship between a wind vector and a co-polarized SAR backscattering signal expressed as a normalized radar cross-section united in linear σ 0 :…”

Section: Methodsmentioning

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

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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.

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“…GF-3 satellite image retrieval algorithms for wind and wave [27] have been developed. One such algorithm is the co-polarized GMF CSARMOD-GF, specifically adapted for GF-3 to address calibration issues [14]. The fundamental principle behind the GMF CSARMOD-GF is to establish the relationship between the NRCS and a wind vector, stated as follows:…”

Section: Auxiliary Datamentioning

confidence: 99%

“…The geophysical model function (GMF) in vertical-vertical (VV) polarization, initially designed for wind retrieval from a scatterometer [11], can also be used for SAR wind retrieval. The co-polarized (VV and horizontal-horizontal (HH)) GMFs have been further improved through SAR measurements, i.e., C-SARMOD for Sentinel-1 (S-1) [12], C-SARMOD2 [13] for RADARSAT-2 (R-2), and CSARMOD-GF for Gaofen-3 (GF-3) [14]. Other studies have been conducted for wind retrieval using the SAR-derived azimuthal cut-off wavelength [15,16] and theoretical backscattering model [17].…”

Section: Introductionmentioning

confidence: 99%

A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

Leng,

Hao,

Shao

et al. 2024

Remote Sensing

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This study introduces a new machine learning-based algorithm for the retrieving significant wave height (SWH) using synthetic aperture radar (SAR) images. This algorithm is based on the azimuthal cut-off wavelength and was developed in quad-polarized stripmap (QPS) mode in coastal waters. The collected images are collocated with a wave simulation from the numeric model, called WAVEWATCH-III (WW3), and the current speed from the HYbrid Coordinate Ocean Model (HYCOM). The sea surface wind is retrieved from the image at the vertical–vertical polarization channel, using the geophysical model function (GMF) CSARMOD-GF. The results of the algorithm were validated against the measurements obtained from the Haiyang-2B (HY-2B) scatterometer, yielding a root mean squared error (RMSE) of 1.99 m/s with a 0.82 correlation (COR) and 0.27 scatter index of wind speed. It was found that the SWH depends on the wind speed and azimuthal cut-off wavelength. However, the current speed has less of an influence on azimuthal cut-off wavelength. Following this rationale, four widely known machine learning methods were employed that take the SAR-derived azimuthal cut-off wavelength, wind speed, and radar incidence angle as inputs and then output the SWH. The validation result shows that the SAR-derived SWH by eXtreme Gradient Boosting (XGBoost) against the HY-2B altimeter products has a 0.34 m RMSE with a 0.97 COR and a 0.07 bias, which is better than the results obtained using an existing algorithm (i.e., a 1.10 m RMSE with a 0.77 COR and a 0.44 bias) and the other three machine learning methods (i.e., a >0.58 m RMSE with a <0.95 COR), i.e., convolutional neural networks (CNNs), Support Vector Regression (SVR) and the ridge regression model (RR). As a result, XGBoost is a highly efficient approach for GF-3 wave retrieval at the regular sea state.

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Wind speed retrieval from the Gaofen-3 synthetic aperture radar for VV- and HH-polarization using a re-tuned algorithm

Cited by 23 publications

References 42 publications

Machine Learning-Based Wind Direction Retrieval From Quad-Polarized Gaofen-3 SAR Images

Machine Learning-Based Wind Direction Retrieval From Quad-Polarized Gaofen-3 SAR Images

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

A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

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