Ocean Wave Inversion Based on Airborne IRA Images

Sun, Da Zhi; Zhang, Yanmin; Wang, Yunhua; Chen, Ge; Sun, Hanwei; Yang, Lei; Bai, Yining; Yu, Fangjie; Zhao, Chunjiang

doi:10.1109/tgrs.2021.3101223

Cited by 10 publications

(7 citation statements)

References 35 publications

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“…Nevertheless, the effect of the layover for the wind waves is the largest when the WPD of wind waves is consistent with the cross-track direction. Sun et al, (2022) [36] estimated the maximum allowed SWH and wind speed at different incidence angles, and the result showed that the OWPs could be inversed based on the InIRA images if the incidence angle was greater than a few degrees, even under high sea state conditions. According to the field measurement data of the mobile weather station, the EWD was southeastward, and the velocity bunching had a small nonlinear effect on the imaging.…”

Section: Discussionmentioning

confidence: 99%

“…However, an excessive multi-look number could distort the SSH. Moreover, Sun et al, (2021) [36], in a more systematic study on the selection of multi-look numbers, suggested that an inappropriate multi-look number could cause additional random phase errors and even partly suppress the inversion of wind waves. In addition to random phase noise, an additional phase introduced by the scattering of the sea surface objects, the distribution of the flat earth phase, and the ground distance resolution, accordingly, needed to be further considered when filtering the interference phases.…”

Section: Ocean Wave Inversionmentioning

confidence: 99%

“…In order to better compare with in situ buoy data, the 2D wavenumber spectra were converted to 1D wavenumber spectra using the following equation [36]:…”

Section: Verification Based On In Situ Datamentioning

confidence: 99%

“…The 1D relative height of the sea surface was further obtained after tidal correction, and the 1D frequency spectra S buoy ( f ) were calculated using the fast Fourier transform (FFT). The 1D OWS are accordingly obtained using the following equation [36]:…”

Section: Verification Based On In Situ Datamentioning

confidence: 99%

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Ocean Wave Inversion Based on a Ku/Ka Dual-Band Airborne Interferometric Imaging Radar Altimeter

Pan

Qiu

et al. 2022

Remote Sensing

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Ocean wave parameters (OWPs), including wave propagation direction (WPD), significant wave height (SWH), and main wave wavelength (MWW) can be typically retrieved using an interferometric imaging radar altimeter (InIRA). However, the inversion accuracy of ocean waves in Ku (15.8 GHz) and Ka (35.8 GHz) bands has not yet been evaluated due to the lack of field observation data. In this paper, to assess the inversion accuracy of OWPs in Ku and Ka bands, an airborne observation experiment using simultaneous Ku and Ka bands was carried out for the first time in Rizhao, Shandong Province, China. A dual-band InIRA (DInIRA) was configured with small incidence angles (4°–18°) and a Global Navigation Satellite System (GNSS) buoy; a mobile weather station was placed at the intersection of the plane routes for validation. Afterward, the WPD, SWH, and MWW were retrieved based on the imaging of sea surface height. As compared with the field in situ data, the WPD inversion results of main wind wave were found to be consistent with the measurement environmental wind direction. The SWH inversion biases, retrieved by the Ku and Ka bands, were 0.38 m and 0.27 m; the MWW inversion biases for the swells were equal to 16.75 m and 3.67 m; and the MWW inversion biases about the wind waves were 2.32 m and 0.57 m. Ultimately, it was established that the OWPs could be effectively retrieved by the DInIRA, and the inversion accuracy of the SWH and the MWW in the Ka band outperformed that in the Ku band.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Ocean Wave Inversionmentioning

confidence: 99%

“…In order to better compare with in situ buoy data, the 2D wavenumber spectra were converted to 1D wavenumber spectra using the following equation [36]:…”

Section: Verification Based On In Situ Datamentioning

confidence: 99%

Section: Verification Based On In Situ Datamentioning

confidence: 99%

See 2 more Smart Citations

Ocean Wave Inversion Based on a Ku/Ka Dual-Band Airborne Interferometric Imaging Radar Altimeter

Pan

Qiu

et al. 2022

Remote Sensing

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“…As part of the efforts for China's future IRA satellite altimeter missions for monitoring ocean surface topography, an airborne interferometric altimeter (Airborne IRA) has been made by the Beijing Institute of Radio Measurement. The airborne IRA data were studied in three previous experiments: on 31 March 2019, in the Xiaomaidao Sea Area in Qingdao, China [9][10][11]; on 16 November 2020, in the Rizhao Sea Area, China [11][12][13]; and on 6 November 2021, in the South China Sea [11]. The SSHA reconstruction from the airborne IRA was improved by applying a multichannel likelihood method [13].…”

Section: Introductionmentioning

confidence: 99%

Sea Surface Height Wavenumber Spectrum from Airborne Interferometric Radar Altimeter

He,

Xu,

Sun

et al. 2024

Remote Sensing

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The proposed “Guanlan” ocean science satellite, led by China’s Laoshan Laboratory, includes an interferometric radar altimeter (IRA) as a key payload. As an integral part of its development, an airborne IRA experiment was conducted on 6 November 2021, with a flight path of approximately 90 km in the South China Sea. This study investigates the IRA’s ability to observe ocean sea surface height (SSH) across scales ranging from meters to mesoscale. The sea surface height anomaly (SSHA) of the IRA is aligned with the SSHA of the AVISO at scales greater than 30 km, but also demonstrates the ability to capture small-scale SSHA changes in two dimensions. We analyzed wavenumber spectra of SSHA obtained from the airborne IRA, ICESat-2, and SARAL/AltiKa satellite for this region. The results show a good agreement in power spectral density (PSD) levels between ICESat-2, SARAL/AltiKa and IRA at scales larger than 30 km. Within the submesoscale range of 1–10 km, the IRA SSHA spectrum exhibits a distinctly negative slope and the lowest energy level. The minimum PSD level of the IRA fell in the range of 10−4–10−3 m2/cycle/km, at scales around 1 km, which is more than an order of magnitude lower than that of ICESat-2, forming a spectral gap that is in agreement with the theoretical expectation. Furthermore, IRA-derived wave direction and significant wave height matched well with the MFWAM wave data. The results of this study underscore the considerable potential of airborne IRA in capturing SSHA across a range of scales, from oceanic waves to submesoscale.

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Intelligent Hash Function Based Key-Exchange Scheme for Ocean Underwater Data Transmission

Soni,

Keshta,

Maaliw

et al. 2024

The Springer Series in Applied Machine Learning

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Ocean Wave Inversion Based on Airborne IRA Images

Cited by 10 publications

References 35 publications

Ocean Wave Inversion Based on a Ku/Ka Dual-Band Airborne Interferometric Imaging Radar Altimeter

Ocean Wave Inversion Based on a Ku/Ka Dual-Band Airborne Interferometric Imaging Radar Altimeter

Sea Surface Height Wavenumber Spectrum from Airborne Interferometric Radar Altimeter

Intelligent Hash Function Based Key-Exchange Scheme for Ocean Underwater Data Transmission

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