A global sea state dataset from spaceborne synthetic aperture radar wave mode data

Li, Xiaoming; Huang, Bingqing

doi:10.1038/s41597-020-00601-3

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…A series of empirical algorithms for retrieving sea state parameters from SAR imagery have been constantly improved over the past few years (e.g. Stopa and Mouche, 2017;Rikka et al, 2018;Li and Huang, 2020).…”

Section: Approaches To Estimating Maritime Parameters From Sarmentioning

confidence: 99%

Multiparametric sea state fields from synthetic aperture radar for maritime situational awareness

Pleskachevsky¹,

Tings²,

Wiehle³

et al. 2022

Remote Sensing of Environment

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Section: Approaches To Estimating Maritime Parameters From Sarmentioning

confidence: 99%

Multiparametric sea state fields from synthetic aperture radar for maritime situational awareness

Pleskachevsky¹,

Tings²,

Wiehle³

et al. 2022

Remote Sensing of Environment

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“…CWAVE‐type empirical models have been developed for ERS/SAR, ENVISAT/ASAR and S1/SAR WV data. Recently, we have finished processing the ten‐year WV data set of ENVISAT/ASAR to obtain the sea state parameters based on the CWAVE_ENV model, and the results suggest good agreements with in situ buoy data and RA data (Li & Huang, 2020). Therefore, we also chose parameters similar to those used in CWAVE‐type algorithms to retrieve SWH by the S1 data: the mean NRCS (denoted

{\overset{σ}{true}}_{0}

), normalized image variance (

c var

), and 20 spectral parameters computed from the variance spectrum of a sub‐image.…”

Section: Methodsmentioning

confidence: 90%

Retrieval of Ocean Wave Heights From Spaceborne SAR in the Arctic Ocean With a Neural Network

Huang

2021

JGR Oceans

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Prior to the launch of the Chinese French Oceanic Satellite (CFOSAT) with its onboard Surface Waves Investigation and Monitoring (SWIM) sensor, the only sensor capable of imaging ocean waves in two dimensions from space was the spaceborne synthetic aperture radar (SAR), which provides images with high spatial resolution. The SAR imaging mechanism of ocean waves is complex which is generally explained by three modulations: tilt modulation, hydrodynamic modulation and velocity bunching (Alpers et al., 1981; Valenzuela, 1978). While tilt and hydrodynamic modulations are also shared by real-aperture radar as the dominant imaging mechanisms of ocean waves, velocity bunching is unique for SAR to image ocean waves. The moving scatterer of water particles with a velocity either toward or away from a moving SAR sensor, causes an azimuthal shift in SAR images. In addition, velocity bunching in the SAR resolution cell leads to an azimuth cutoff , that is, the minimum SAR-detectable wavelength of ocean waves traveling in the azimuth direction. Therefore, the nonlinearity of SAR ocean wave imaging complicates their retrieval. In the following, we briefly summarize the existing methods used to retrieve ocean wave information in terms of both two-dimensional spectra and integral wave parameters. The Max Planck Institute (MPI) scheme developed by Hasselmann and Hasselmann (1991) and Hasselmann et al. (1996) is a widely used method to retrieve two-dimensional ocean wave spectra from spaceborne

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“…Although these could be the possible explanations of the larger errors compared against buoys found here, it remains to do further studies. One feasible remedy for this could be the further cross-calibration between ASCAT derived SWH and buoy data (many more matchups needed) to refine our model (e.g., see corrections for 10-years SAR sea state products [54]). However, this is beyond the scope of the paper.…”

Section: Buoy Comparisonmentioning

confidence: 99%

Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network

et al. 2021

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Sea state estimation from wide-swath and frequent-revisit scatterometers, which are providing ocean winds in the routine, is an attractive challenge. In this study, state-of-the-art deep learning technology is successfully adopted to develop an algorithm for deriving significant wave height from Advanced Scatterometer (ASCAT) aboard MetOp-A. By collocating three years (2016–2018) of ASCAT measurements and WaveWatch III sea state hindcasts at a global scale, huge amount data points (>8 million) were employed to train the multi-hidden-layer deep learning model, which has been established to map the inputs of thirteen sea state related ASCAT observables into the wave heights. The ASCAT significant wave height estimates were validated against hindcast dataset independent on training, showing good consistency in terms of root mean square error of 0.5 m under moderate sea condition (1.0–5.0 m). Additionally, reasonable agreement is also found between ASCAT derived wave heights and buoy observations from National Data Buoy Center for the proposed algorithm. Results are further discussed with respect to sea state maturity, radar incidence angle along with the limitations of the model. Our work demonstrates the capability of scatterometers for monitoring sea state, thus would advance the use of scatterometers, which were originally designed for winds, in studies of ocean waves.

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A global sea state dataset from spaceborne synthetic aperture radar wave mode data

Cited by 12 publications

References 33 publications

Multiparametric sea state fields from synthetic aperture radar for maritime situational awareness

Multiparametric sea state fields from synthetic aperture radar for maritime situational awareness

Retrieval of Ocean Wave Heights From Spaceborne SAR in the Arctic Ocean With a Neural Network

Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network

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