Wave Retrieval Under Typhoon Conditions Using a Machine Learning Method Applied to Gaofen-3 SAR Imagery

Shao, Weizeng; Ding, Yingying; Li, Jichao; Gou, Shuiping; Nunziata, Ferdinando; Yuan, Xiaojun; Zhao, Liangbo

doi:10.1080/07038992.2019.1683444

Cited by 21 publications

(13 citation statements)

References 36 publications

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“…The basic parameters of the three patterns are shown in Table 1 [24]. ScanSAR modes of Gaofen-3 play an important role in the inversion of ocean parameters and ocean monitoring [25][26][27]. Take NSC as an example to illustrate the working mechanism of ScanSAR.…”

Section: Scalloping Modeling For Gf-3 Scansar Modementioning

confidence: 99%

An Improved Descalloping Method Combined With Imaging Parameters for GaoFen-3 ScanSAR

et al. 2020

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Due to the specific working mechanism of alternately transmitting and receiving signals between multiple adjacent swaths, scanning synthetic aperture radar (ScanSAR) will cause periodical wavelike modulation of the intensity image along azimuth direction, which is known as scalloping. Conventional descalloping methods are achieved by proper azimuth antenna pattern (AAP) correction and multi-looking techniques but are limited by the accuracy of Doppler centroid estimating and the thermal noise. Another type of method extracts and suppresses the scalloping texture on the image, but the scanning parameters of ScanSAR are insufficiently considered. The period of scalloping on the image is related to the period of switching between subswathes. While the harmonics can be calculated by the period of a periodic signal, an improved frequency filtering method combined with imaging parameters is proposed. The scalloping modulation model of ScanSAR combined with imaging parameters is constructed, and the harmonics of scalloping texture are accurately calculated and filtered. For the low signal to noise ratio (SNR) image, the antenna pattern is modified according to SNR to avoid scalloping caused by noise. For non-uniform scenes, scalloping suppression is achieved by using the scalloping features acquired by uniform scenes. To separate the non-uniform scenes from uniform scenes, we still use the characteristics of harmonics caused by scalloping. Our descalloping method achieves accurate suppression of scalloping without sea-land segmentation and ship mask and the residual scalloping is reduced from 1.0 to 0.3–0.5 dB. The residual scalloping and statistical characteristics of the image are analyzed to demonstrate the effectiveness of the proposed method.

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Section: Scalloping Modeling For Gf-3 Scansar Modementioning

confidence: 99%

An Improved Descalloping Method Combined With Imaging Parameters for GaoFen-3 ScanSAR

et al. 2020

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“…Cannata et al [13] reported the use of statistical analysis and ML random forest methodology to infer the wave heights from ubiquitous seismic noise, generated by energy transfer from the ocean to the solid earth. Shao et al [14] demonstrated wave data retrieval by recorded synthetic aperture radar images. The core of their methodology was a numeric wave model, which simulated a wave field, to which recorded waves were compared through random forest and decision tree ML methodologies.…”

Section: Introductionmentioning

confidence: 99%

Efficient machine learning method for spatio-temporal water surface waves reconstruction from polarimetric images

Ginio¹,

Liberzon²,

Lindenbaum³

et al. 2023

Meas. Sci. Technol.

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Accurate and cost-effective sea state measurements, in terms of spatio-temporal distribution of water surface elevation (water waves), is of great interest for scientific research and various engineering, industrial, and recreational applications. To this end, numerous measurement techniques have been developed over the years. None of these techniques, however, are universally applicable across various ocean and laboratory conditions and none provide near-real-time data. We utilized the latest advances in polarimetric imaging to develop a new remote sensing method based on machine learning methodology and polarimetric reflection measurements for inferring surface waves elevation and slope. The method utilizes a newly available, inexpensive polarimetric camera providing images of the water surface in a high spatio-temporal resolution at several linear polarization angles. Algorithms based on artificial neural networks (ANNs) are then trained to obtain high-resolution reconstructions of the water surface slope state from those images. The ANNs are trained on laboratory-collected supervised datasets of prescribed mechanically generated monochromatic wave trains and tested on a stochastic wave field of JONSWAP spectral shape. The proposed method, based on inferring the surface slope from polarimetric images, provides a dense estimate of the water surface. The results of this study pave the way for the development of accurate and cost-effective near-real-time remote sensing tools for both laboratory and open sea wave measurements.

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“…Therefore, the WW3 model is usually employed for wave simulations over large regions, such as global seas [17] or the western Pacific Ocean [18], while the SWAN model is typical used to analyze coastal waters [19]. Model-simulated waves are also commonly used in synthetic aperture radar (SAR) wave monitoring [20,21] and, in particular, as auxiliary data for typhoon analysis [22].…”

Section: Introductionmentioning

confidence: 99%

A Study of Wave-Induced Effects on Sea Surface Temperature Simulations during Typhoon Events

Sun

Shao

et al. 2021

JMSE

Self Cite

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In this work, we investigate sea surface temperature (SST) cooling under binary typhoon conditions. We particularly focus on parallel- and cross-type typhoon paths during four typhoon events: Tembin and Bolaven in 2012, and Typhoon Chan-hom and Linfa in 2015. Wave-induced effects were simulated using a third-generation numeric model, WAVEWATCH III (WW3), and were subsequently included in SST simulations using the Stony Brook Parallel Ocean Model (sbPOM). Four wave-induced effects were analyzed: breaking waves, nonbreaking waves, radiation stress, and Stokes drift. Comparison of WW3-simulated significant wave height (SWH) data with measurements from the Jason-2 altimeter showed that the root mean square error (RMSE) was less than 0.6 m with a correlation (COR) of 0.9. When the four typhoon-wave-induced effects were included in sbPOM simulations, the simulated SSTs had an RMSE of 1 °C with a COR of 0.99 as compared to the Argos data. This was better than the RMSE and COR recovered between the measured and simulated SSTs, which were 1.4 °C and 0.96, respectively, when the four terms were not included. In particular, our results show that the effects of Stokes drift, as well as of nonbreaking waves, were an important factor in SST reduction during binary typhoons. The horizontal profile of the sbPOM-simulated SST for parallel-type typhoon paths (Typhoons Tembin and Bolaven) suggested that the observed finger pattern of SST cooling (up to 2 °C) was probably caused by drag from typhoon Tembin. SST was reduced by up to 4 °C for cross-type typhoon paths (Typhoons Chan-hom and Linfa). In general, mixing significantly increased when the four wave-induced effects were included. The vertical profile of SST indicated that disturbance depth increased (up to 100 m) for cross-type typhoon paths because the mixing intensity was greater for cross-type typhoons than for parallel-type typhoons.

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Wave Retrieval Under Typhoon Conditions Using a Machine Learning Method Applied to Gaofen-3 SAR Imagery

Cited by 21 publications

References 36 publications

An Improved Descalloping Method Combined With Imaging Parameters for GaoFen-3 ScanSAR

An Improved Descalloping Method Combined With Imaging Parameters for GaoFen-3 ScanSAR

Efficient machine learning method for spatio-temporal water surface waves reconstruction from polarimetric images

A Study of Wave-Induced Effects on Sea Surface Temperature Simulations during Typhoon Events

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