Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network

Li, Xiao-Ming; Qin, Tingting; Wu, Ke

doi:10.20944/preprints202005.0300.v1

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…In particular, an increasing underestimation trend with sea state is not observed. We recently used the same way to solve the underestimation of SSW retrievals by the same S1 EW data in HH polarization (Li, Qin, et al., 2020).…”

Section: Discussionmentioning

confidence: 99%

“…As most of the S1 EW and IW data acquired over in situ buoys are in VV polarization, we found only 305 pairs of S1 data and National Data Buoy Center (NDBC) buoy data in the period from October 2014 to October 2019 (Li, Qin, et al., 2020). Therefore, in this study, we used RA measurements of SWH in the Arctic ocean as ground truth to develop the BPNN model.…”

Section: Data Setsmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Data Setsmentioning

confidence: 99%

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

Huang

2021

JGR Oceans

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Retrieving Ocean Surface Winds and Waves from Augmented Dual-Polarization Sentinel-1 SAR Data Using Deep Convolutional Residual Networks

et al. 2023

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Sea surface winds and waves are very important phenomena that exist in the air–sea boundary layer. With the advent of climate change, cascade effects are bringing more attention to these phenomena as warmer sea surface temperatures bring about stronger winds, thereby altering global wave conditions. Synthetic aperture radar (SAR) is a powerful sensor for high-resolution surface wind and wave observations and has accumulated large quantities of data. Furthermore, deep learning methods have been increasingly utilized in geoscience, especially the inversion of ocean information from SAR imagery. Here, we propose a method to invert various parameters of ocean surface winds and waves using Sentinel-1 SAR IW mode data. To ensure this method is more robust and scalable, we augmented the input data with dual-polarized SAR imagery, an incident angle, and a more constrained homogeneity test. This method adopts a deeper structure in order to retrieve more wind and wave parameters, and the use of residual networks can accelerate training convergence and improve regression accuracy. Using 1600 training samples filtered by a novel homogeneity test and with significant wave heights between 0 and 10 m, results from error parameters including the root mean square error (RMSE), scatter index (SI), and correlation coefficient (COR) show the great performance of this proposed method. The RMSE is 0.45 m, 0.76 s, and 1.90 m/s for the significant wave height, mean wave period, and wind speed, respectively. Furthermore, the temporal variation and spatial distribution of the estimates are consistent with China–France Oceanography Satellite (CFOSAT) observations, buoy measurements, WaveWatch3 regional model data, and ERA5 reanalysis data.

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Joint Inversion of Sea Surface Wind and Current Velocity Based on Sentinel-1 Synthetic Aperture Radar Observations

Sun,

Li,

Lin

et al. 2024

JMSE

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Spaceborne synthetic aperture radar (SAR) has been proven to be a useful technique for observing the sea surface wind and current over the open ocean given its all-weather data-gathering capability and high spatial resolution. In addition to the commonly used radar return magnitude quantified by normalized radar cross section (NRCS), the Doppler centroid anomaly (DCA) has added another dimension of information. In this study, we combine the NRCS and DCA for a joint inversion of wind and surface current information using a Bayesian method. SAR-estimated Doppler is corrected by a series of steps, including the removal of scalloping effect and land correction. The cost function of this inversion scheme is constructed based on NRCS, DCA, and a background model wind. The retrieved wind results show the quality of performance through comparison with the in situ buoy measurements, showing a mean bias and a root-mean-square error (RMSE) of 0.33 m/s and 1.45 m/s for wind speed and 6.94° and 35.74° for wind direction, respectively. The correlation coefficients for wind speed and direction reach 0.931 and 0.661, respectively. Based on the obtained wind field, the line-of-sight velocity of the sea surface current is then derived by removing the wind contribution using the empirical model. The results show a consistent spatial pattern relative to the high-frequency radars, with the comparison relative to the drifter-measured current velocity exhibiting a mean bias of 0.02 m/s and RMSE of 0.32 m/s, demonstrating the reliability of the proposed inversion scheme. Such results will serve as a prototype for future spaceborne sensors to combine the radar return and Doppler information for the joint retrieval of wind vector and surface current velocity. This technique could be readily extended to the radar configuration of rotating beams for monitoring winds and current vectors.

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Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network

Cited by 8 publications

References 29 publications

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

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

Retrieving Ocean Surface Winds and Waves from Augmented Dual-Polarization Sentinel-1 SAR Data Using Deep Convolutional Residual Networks

Joint Inversion of Sea Surface Wind and Current Velocity Based on Sentinel-1 Synthetic Aperture Radar Observations

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