Wind speed retrieval from Chinese Gaofen-3 synthetic aperture radar using an analytical approach in the nearshore waters of China’s seas

Yao, Ru; Shao, Weizeng; Jiang, Xingwei; Yu, Tan

doi:10.1080/01431161.2022.2079019

Cited by 13 publications

(4 citation statements)

References 74 publications

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“…SAR is well recognized as an effective sensor for sea surface monitoring with an acceptable spatial resolution. Over time, theoretical and empirical algorithms related to wind and wave retrieval from SAR images have been developed and maturely implemented for C-band SAR, i.e., S-1 [12] and GF-3 [17]. Additionally, a DCA-based algorithm empirically inverts SAR current velocity in the radar look direction [34][35][36].…”

Section: Discussionmentioning

confidence: 99%

“…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]. However, due to the saturation of the co-polarized SAR backscattering signal at the regular sea state [18] and at a strong wind speed of >25 m/s (i.e., cyclonic wind profile), GMF usually inverts cross-polarized vertical-horizontal and horizontal-vertical images [19,20] using a machine learning method [21].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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“…In this section, the sea surface backscattering model for simulating the SAR roughness [54] is described, including the wind, wave, and current terms. In particular, the empirical model for the NB estimation caused by wave breaking is introduced.…”

Section: Methologymentioning

confidence: 99%

Retrieval of Rain Rates for Tropical Cyclones From Sentinel-1 Synthetic Aperture Radar Images

Zhao

Shao

Lai

et al. 2023

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

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The purpose of this study was to develop a method for retrieving the rain rate from C-band (~5.3 GHz) synthetic aperture radar (SAR) images during tropical cyclones (TCs). Seven dual-polarized (vertical-vertical [VV] and vertical-horizontal [VH]) Sentinel-1 (S-1) SAR images were acquired in the interferometric-wide (IW) swath mode during the Satellite Hurricane Observation Campaign. These images were collocated with rain rates measured by the Stepped-Frequency Microwave Radiometers onboard National Oceanic and Atmospheric Administration aircraft. Wind speeds were retrieved from the VH-polarized SAR images using the geophysical model function (GMF) S1IW.NR. We determined the difference between the measured normalized radar cross section (NRCS) based on VV-polarized SAR and the predicted NRCS derived using the geophysical model function CMOD5.N forced with wind speeds retrieved from VH-polarized SAR images. Rain cells were identified as regions in the images where the NRCS difference was greater than 0.5 dB or smaller than -0.5 dB. We found that the difference in the NRCS decreased and the VH-polarized wind speed increased with increasing rain rate. Based on these findings, we developed an empirical function for S-1 SAR rain retrieval in a TC, naming it CRAIN_S1. The validation of the CRAIN_S1 results with Tropical Rainfall Measuring Mission data resulted in a root mean square error of 0.58 mm/hr and a correlation of 0.89. This study provides an alternate method for rain monitoring utilizing SAR data with a fine spatial resolution.

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“…As concluded in [5], the HY-2B geophysical data records (GDRs) after systematic corrections using the MOE determination method have high accuracy. Scatterometer-based winds are valuable sources for the development and validation of SAR wind retrieval algorithms [11,36,37] due to their high levels of quality and fine spatial resolutions. The main limitation of ASCATs and the HY-2B is that they are unable to detect strong winds (>25 m/s).…”

Section: Remoted-sensed Windsmentioning

confidence: 99%

Can Sea Surface Waves Be Simulated by Numerical Wave Models Using the Fusion Data from Remote-Sensed Winds?

Shi,

Shao,

Shi

et al. 2023

Remote Sensing

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The purpose of our work is to investigate the performance of fusion wind from multiple remote-sensed data in forcing numeric wave models, and the experiment is described herein. In this study, 0.125° gridded wind fields at 12 h intervals were fused by using swath products from an advanced scatterometer (ASCAT) (a Haiyang-2B (HY-2B) scatterometer) and a spaceborne polarimetric microwave radiometer (WindSAT) during the period November 2019 to October 2020. The daily average wind speeds were compared with observations from National Data Buoy Center (NDBC) buoys from the National Oceanic and Atmospheric Administration (NOAA), yielding a 1.66 m/s root mean squared error (RMSE) with a 0.81 correlation (COR). This suggests that fusion wind was reliable for our work. The fusion winds were used for hindcasting sea surface waves by using two third-generation numeric wave models, denoted as WAVEWATCH-III (WW3) and Simulation Wave Nearshore (SWAN). The WW3-simulated waves in the North Pacific Ocean and the SWAN-simulated waves in the Gulf of Mexico were validated against the measurements from the NDBC buoys and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) for the period June−September 2020. The analysis of significant wave heights (SWHs) up to 9 m yielded a < 0.5 m RMSE with a > 0.8 COR for the WW3 and SWAN models. Therefore, it was believed that the accuracy of the simulation using the two numeric models was comparable with that forced by a numeric atmospheric model. An error analysis was systematically conducted by comparing the modeled WW3-simulated SWHs with the monthly average products from the HY-2B and a Jason-3 altimeter over global seas. The seasonal analysis showed that the differences in the SWHs (i.e., altimeter minus the WW3) were within ±1.5 m in March and June; however, the difference was quite significant in December. It was concluded that remote-sensed fusion wind can serve as a driving force for hindcasting waves using numeric wave models.

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Wind speed retrieval from Chinese Gaofen-3 synthetic aperture radar using an analytical approach in the nearshore waters of China’s seas

Cited by 13 publications

References 74 publications

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

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

Retrieval of Rain Rates for Tropical Cyclones From Sentinel-1 Synthetic Aperture Radar Images

Can Sea Surface Waves Be Simulated by Numerical Wave Models Using the Fusion Data from Remote-Sensed Winds?

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