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

Wu, Ke; Li, Xiao-Ming; Huang, Bingqing

doi:10.1029/2020jc016946

Cited by 24 publications

(12 citation statements)

References 48 publications

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“…The most common method is to establish the (multiple) linear regression relationship between the measured values and the corrected values, e.g., [15,16]. Recently, with the progress of artificial intelligence technology, the NN method has gradually become an important means to calibrate ocean observation and simulation results [17][18][19][20]. However, most of the existing studies focus on the calibration of wave height, e.g., [15,16,19,20], and studies on using other synchronous parameters to calibrate wavelength information would be eagerly anticipated.…”

Section: Introductionmentioning

confidence: 99%

Calibration Experiments of CFOSAT Wavelength in the Southern South China Sea by Artificial Neural Networks

Liu

et al. 2022

Remote Sensing

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The wave data measured by CFOSAT (China France Oceanography Satellite) have been validated mainly based on numerical model outputs and altimetry products on a global scale. It is still necessary to further calibrate the data for specific regions, e.g., the southern South China Sea. This study analyses the practicability of calibrating the dominant wavelength by using artificial neural networks and mean impact value analysis based on two sets of buoy data with a 2-year observation period and contemporaneous ERA5 reanalysis data. The artificial neural network modeling experiments are repeated 1000 times randomly by Monte Carlo methods to avoid sampling uncertainty. Both experimental results based on the random sampling method and chronological sampling method are performed. Independent buoy observations are used to validate the calibration model. The results show that although there are obvious differences between the CFOSAT wavelength data and the field observations, the parameters observed by the satellite itself can effectively calibrate the data. In addition to the wavelength, nadir significant wave height, nadir wind speed, and the distance between the calibration point and satellite observation point are the most important parameters for the calibration. Accurate data from other sources, such as ERA5, would be helpful to further improve the calibration results. The variable contributing the most to the calibration effect is the mean wave period, which virtually provides relatively accurate wavelength information for the calibration network. These results verify the possibility of synchronous self-calibration for the CFOSAT wavelength data and provide a reference for the further calibration of the satellite products in other regions.

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Section: Introductionmentioning

confidence: 99%

Calibration Experiments of CFOSAT Wavelength in the Southern South China Sea by Artificial Neural Networks

Liu

et al. 2022

Remote Sensing

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“…Li et al [41] compared the wave height estimated from the ASAR WM data with buoys and numerical models, and gave the RMSE is 0.64 and 0.25 m, respectively. Wang et al [20] represents that the RMSE about wave height using GF-3 WM data is between 0.50 and 0.60 m. Pleskachevsky et al [14] estimated the SWH from S1 data in the North Sea and eastern Baltic Sea, and the RMSE is 0.80 m. Wu et al [25] gives the RMSE of 0.71 m for SWH based on the S1 data in the arctic ocean using BPNN. Therefore, compared with the above model, our model has accepted accuracy.…”

Section: B Comparison With Wave Height From Ramentioning

confidence: 99%

“…These algorithms have been developed very rapidly for wave inversion in recent years. The backpropagation neural network (BPNN) was used by Wu et al [25] and Stopa and Mouche [26] to extract SWH based on Sentinel-1 WM data. The deep learning method has also been used to retrieve wave parameters from SAR images [27].…”

Section: Introductionmentioning

confidence: 99%

Wave Height Estimation and Validation Based on the UFS Mode Data of Gaofen-3 in South China Sea

Cui

2022

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

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The ultrafine strip (UFS) of the Gaofen-3 (GF-3) satellite provides extensive ocean wave details due to the high spatial resolution of 3 m. In this article, a new empirical model is developed to estimate wave height from GF-3 UFS mode data in South China Sea (SCS). Traditional methods either have more input parameters or complex forms or complex transformations, and most of them depend on the visible wave pattern. The model has the advantages of fewer input parameters and unnecessary visual wave patterns. The wave height estimation model is developed based on the collated data pairs between GF-3 UFS data in horizontal-horizontal polarization and the ERA5 reanalysis dataset from 2018 to 2021. The datasets are randomly divided into two groups. One group (about 80%) is used to develop the empirical relation; the other group (about 20%) and some altimeter data are used for significant wave height (SWH) verification. The comparison of the model data with the remaining ERA5 (ECMWF Reanalysis v5) data and the altimeter shows that the root-mean-square error is 0.41 and 0.47 m, the scatter index is 29.24% and 29.79%, and the correlation coefficient is 0.90 and 0.82, respectively. These statistical indices suggest that the developed model is suitable for SWH retrievals. So, the study results indicate that the GF-3 UFS mode data provide valuable information about wave conditions in the SCS.

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“…Back propagation (BP) neural networks, with powerful nonlinear mapping capability, have shown tremendous potential in the field of Earth sciences, such as nearshore bathymetric prediction from remote sensing optical images (Lai et al., 2022; Sandidge & Holyer, 1998), sea surface chlorophyll prediction (Wang & Wang, 2021), and ocean significant wave height inversion (Wu et al., 2021). These studies indicate that BP neural networks can successfully simulate various geophysical transfer functions with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Bathymetric Prediction Using Multisource Gravity Data Derived From a Parallel Linked BP Neural Network

Heyuan

Feng

et al. 2022

JGR Solid Earth

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Seafloor topography, or bathymetry, has significant economic, military, and scientific research value. High-precision seafloor topographic data are helpful for studying the geographic features of the seafloor and the structure of the Earth's crust. Moreover, the seafloor requires detailed mapping to ensure fairway safety and aid in submarine navigation.Echo sounding techniques have been classically used for mapping the seafloor. From single-beam sounding in the 1950s to multibeam sounding in the 1980s , echo sounding techniques have advanced from point measurement to surface measurement. However, it is still a difficult task to obtain global seafloor topography with traditional methods within a short period (Carron et al., 2001;Sandwell & Smith, 2001). Fortunately, with the improvements in the accuracy and density of satellite altimetry data, using gravity anomalies (GAs) obtained from altimetry data to predict depth has become a feasible approach. Dixon et al. (1983) were among the first researchers to use SEASAT altimetry data to predict depth. Sandwell (1994, 1997) used GAs to construct a global seafloor model and exceedingly filled in the blank areas in nautical charts. Since then, a growing number of global seafloor models have been released, such as ETOPO1 (

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

Cited by 24 publications

References 48 publications

Calibration Experiments of CFOSAT Wavelength in the Southern South China Sea by Artificial Neural Networks

Calibration Experiments of CFOSAT Wavelength in the Southern South China Sea by Artificial Neural Networks

Wave Height Estimation and Validation Based on the UFS Mode Data of Gaofen-3 in South China Sea

Bathymetric Prediction Using Multisource Gravity Data Derived From a Parallel Linked BP Neural Network

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