The spaceborne wave scatterometer Surface Waves Investigation and Monitoring(SWIM) can provide global ocean Directional Wave Height Spectra(DWHS) data product. However, under specific sea conditions, the performance of SWIM DWHS data product decreases due to the presence of parasitic peaks at low wavenumbers, the non-linear surfboard effect in the radar imaging mechanism, and a slight underestimation of the speckle noise spectral density. In this study, by leveraging the DWHS measured by National Data Buoy Center(NDBC) buoys and employing an indirect colocation method, DWHS calibration models based on the AU-Net(Attention U-Net) were developed. These models were established for the pure wind wave sea with wind speeds ranging from 9m/s to 19m/s and the single swell sea with significant wave heights between 1m and 5m, respectively, for SWIM beams 6°, 8°, and 10°. The effectiveness of the established calibration model was verified from two aspects. Firstly, by comparing the SWIM DWHS before and after correction with buoy DWHS, the calibration results show that the wavelength measurement upper limit of SWIM DWHS has been extended from 500m to 1100m; under pure wind wave sea(the single swell sea), the correlation coefficients(CC) of DWHS by SWIM beams 6°, 8°, and 10° increased from 0.35, 0.47, and 0.43(0.72, 0.72, and 0.76) to 0.99, 0.99, and 0.99(1, 0.99, and 0.99), and the structural similarity index(SSIM) has increased from 0.30, 0.42, and 0.37(0.70, 0.57, and 0.64) to 0.99(0.99). Secondly, the performance of significant wave heightHsand peak wavelength λpcalculated from each SWIM DWHS sample before and after correction, has been verified using MFWAM reanalysis data. The validation results show that for SWIM beams 6°, 8°, and 10° under pure wind wave sea(the single swell sea), the RMSE ofHsis merely 0.2m, 0.19m, and 0.17m(0.12m, 0.09m, and 0.14m) after calibration, while the mean bias(MB) is only 0m, -0.04m, and -0.01m(-0.06m, -0.02m, and -0.09m). Similarly, the RMSE of λpis just 8.99m, 8.85m, and 8.44m(20.44m, 19.50m, and 21.77m), the MB of λpis merely 0.97m, 1.33m, and 0.15m(1.59m, 2.06m, and 1.90m).

The EN regression models are implemented for estimating significant wave height (SWH) from quad-polarization Gaofen-3 SAR wave mode data based on the collocated data set of ~11200 Gaofen-3 imagettes matched with SWH from ERA5 reanalysis. The importance of SAR features for SWH estimation from EN is analyzed. The model performance is evaluated through a comparison with observations from buoys and altimeters. The results show that the 20 EOF spectral parameters, NRCS, cvar, and θ are significant for EN to estimate SWH from Gaofen-3 SAR. The EN models achieve good performance with RMSEs smaller than 0.5 m. The co-polarization models show better performance at low sea states but worse performance at high sea states compared to the cross-polarization models.

The Surface Wave Investigation and Monitoring (SWIM) aboard the China-France Oceanic Satellite (CFOSAT) provides the ocean wave spectrum (70–50 m wavelength range). However, the accuracy of this data is affected by speckle noise, low-frequency parasitic peaks, and missing information in the short wavelength range. To improve the accuracy of the SWIM wave spectrum, this letter introduces a vision transformer (ViT) deep learning (DL) model combined with a deconvolution block, which leverages buoy wave spectrum and full wavenumber wind wave spectrum to improve the SWIM wave spectrum with high precision and wide wavelength range. The results show that the linear correlation coefficient of the improved wave spectrum has increased from 0.510 to 0.833. Furthermore, the accuracy of spectrum parameters is enhanced. Particularly, compared with the original SWIM spectrum, the root mean square error (RMSE) for the mean wave period (MWP) and peak wave period (PWP) decreased by 70.19% and 71.68%, respectively.

SWIM is a wave spectrometer on board CFOSAT designed to measure the wave spectrum over a width of 180 km [1]. In this paper, the ability of SWIM to detect ISWs is explored for the first time using SWIM data to investigate the internal isolated waves (ISWs) in the Sulu Sea. As an example, two identical ISWs are detected by the 8 ° and 10 ° incidence angle beams, and the empirical mode decomposition (EMD) method is used to separate and extract the ISWs signal from the echo power of SWIM and match the extracted signal with the ISWs; the SWIM data are detrended and moving average processed to calculate the perturbation changes of the SWIM echo signal caused by ISWs. The qualitative analysis shows that ISWs cause a perturbation in the SWIM echo power and that SWIM has the ability to detect ISWs. A preliminary comparison shows that the magnitude of the SWIM echo power variation is related to the characteristics of the ISWs themselves and the magnitude of the energy contained in the beam when the ISWs are detected.

The Surface Waves Investigation and Monitoring (SWIM) omnidirectional wave spectra and its shape parameters are compared by matching the SWIM wave spectrum data with the buoy wave spectra of the National Data Buoy Center (NDBC). The results show that the omnidirectional wave spectrum of SWIM has significant overestimation at low frequencies and significant underestimation at high frequencies. And there are spurious peaks at low frequencies. The overestimation and underestimation of the SWIM 6° beam wave spectrum are the most obvious. We believe that the presence of spurious peaks is due to the amplification of the noise floor in the SWIM omnidirectional spectrum at low frequencies. This phenomenon is greatly relieved with the increase of Significant Wave Height (SWH) . The difference between the SWIM spectrum shape parameters frequency spread (σf) and the "peakedness" of the omnidirectional spectrum (Qp) and the buoy is large. The difference between them decreases significantly with increasing SWH.

The 200 dual-polarized Sentinel-1 SAR images covering typhoons from 2018 to 2022 are collocated with ERA5 reanalysis data. The SAR-ERA5 collocations are randomly divided into two subsections: one for model training (80%), and the other for independent testing (20%). Based on the selected training samples, three machine learning models, including the Back Propagation Neural Networks (BPNN), the Random Forest (RF), and the Gaussian Process Regression (GPR), are built for the estimation of typhoon sea surface wind speed (SSWS) from VV data. The results show that the three machine learning models achieve significantly better performance than the traditional method. BPNN has the best performance with the bias, root mean square error (RMSE) and scattering index (SI) respectively being -1.33 m/s, 3.25 m/s and 1.5%. The GPR model performs slightly better than BPNN at higher wind speeds. However, the performance of RF is relatively poor. The additional introduction of VH information improves the model performance.

Compared with traditional underwater topography measurement methods such as multi-beam or sonar, remote sensing satellites provide a new method for underwater topography detection, but it's difficult to retrieve high-resolution and high-precision underwater topography only using a single SAR image. This paper aims to perform the complementary and synergetic effect of Multi-source SAR data and proposes a shallow sea topography detection model based on Multi-source SAR. We also verified the model with four SAR images of GF-3, Sentinel-1, ALOS PALSAR and ENVISAT ASAR satellite. The mean relative error of the detected topography is 12.50%, and the correlation coefficient is 0.91. The results show that the model proposed in this paper can effectively retrieve high-precision and high-resolution underwater topography maps.

The Sentinel-3 synthetic aperture radar altimeter provides two kinds of rain flag by using the changes of the three backscatter coefficients, which provides a foundation for the comprehensive use of all the backscatter coefficients to determine rain conditions. This paper analyzes the statistical relationship between the two backscatter coefficients of Ku band SAR mode and PLRM mode and the C band backscatter coefficient when it is not rain, then the deviation generated by the backscatter coefficients of the two bands in the rainfall state is compared, provides a new method for determining rain flag. Finally, the measurement data of precipitation radar and altimeter data are matched and verified, and the result of the new method of updating the threshold judgment condition is more accurate.

The dependence of azimuth cutoff wavelength (λc) on significant wave height (SWH) and wind speed (U) at C-band VV, HH, VH, and HV polarizations is analyzed based on the collocations between the quad-polarization Gaofen-3 SAR wave mode images and the ERA5 wind and wave reanalysis. Then the influence of pixel spacing on the dependence is discussed. The results show the co-polarized (VV and HH) λchas an evident positive dependence with SWH (with U), while for the cross-polarization (VH and HV), it is relatively weaker. In addition, the cross-polarized λcis more related to$U$than to SWH. Moreover, the size of pixel spacing affects the estimated value of λc. The dependence of λcon SWH and$U$shows a decreasing trend with pixel spacing increasing in all four polarizations. But this trend is more significant for co-polarization, especially for VV.

The upwind-downwind asymmetry in radar return from the sea surface is well known. This paper develops a modified two-scale model to better describe the difference between upwind and downwind of the radar backscatter at moderate incidence angles caused by skewness of the non-Gaussian sea surface. The unknown parameter in the modified model is estimated by fitting the model to CMOD5.n at different incidence angles under various wind conditions. Then the modified model is compared with CMOD5.n and the advanced scatterometer (ASCAT) backscatter measurements. The results show that the model predictions are in rather good agreement with the reference data. The modified model can accurately describe the difference between the upwind and downwind normalized radar cross section (NRCS) with the root mean square difference being about 0.02 dB compared with that of CMOD5.n.

An improved two-scale model (I-TSM) for the incidence angle transition range (5-11°) is proposed by analyzing the contributions of both quasi-specular and Bragg scatterings to the total sea surface scattering. With normalized radar cross section (NRCS) products of surface wave investigation and monitoring (SWIM) instrument, the mean square slope (MSS) and reflection coefficient of the geometrical optics (GO) model are modified to simulate the quasi-specular scattering in I-TSM, and a Bragg scattering contribution factor adaptive to the incidence angle and wind speed is fitted to provide the Bragg scattering component for I-TSM. At different incidence angles and wind speeds, I-TSM combines the quasi-specular and Bragg scatterings in variable proportions by the fitting factor. I-TSM avoids the critical angle selection and separation problems of the traditional TSM in the incidence angle transition range. Compared with the GO model and traditional TSM, I-TSM has significantly higher accuracies with root mean square error (RMSE) of 0.309dB, relative error (RE) of 2.019% and scattering index (SI) of 0.03 when SWIM NRCS products are the standard data in the validation. I-TSM will provide high-precision NRCS simulation data in the incidence angle transition range for scattering characteristics analysis and echo simulation of SWIM.

The comparison and verification of ocean wave spectrum by remote sensing and by in-situ measurements at the spectral level is quite rare, because the use of the traditional comparison method lead to very limited spatio-temporal matching pairs. In this paper, a new comparison method is proposed. With this method, under different sea conditions (wind wave mainly/swell mainly) and sea surface conditions (wind speed smaller than 20m/s, significant wave height from 1m to 7m), mean directional wave height spectra from SWIM (Surface Waves Investigation and Monitoring) are compared at the spectral level to the buoy counterparts, in different classes of sea-state. This includes the comparison of the omni-directional wave height spectrum and the directional function at the peak wave number. The comparison results show that under medium and high sea conditions, wave directional spectra provided by the SWIM beams at 8 ° and 10 ° incidence have a high consistency with those from buoy data. Under low sea conditions, the measurement bias of SWIM wave directional spectra mainly comes from three phenomena which are, by order of importance, an abnormal lifting of spectral energy caused by non- wave components at low wave numbers (parasitic peak), from the non-linear surfboard effect in the radar imaging mechanism and from a slight underestimation of speckle noise spectral density.

This paper assesses the sea surface height (SSH), significant wave height (SWH) and wind speed (U) measurements derived from Haiyang-2B (HY-2B) altimeter products from April 2019 to September 2019. Crossover analysis is used to assess to SSH measurements. The mean standard deviations of the SSH crossover differences for HY-2B is 5.09 cm, which is lower than Jason-2 (5.31 cm) and Jason-3 (5.27 cm). Compared with the National Data Buoy Center (NDBC) observations, the HY-2B SWH measurements show a root-mean-square error (RMSE) of 0.205 m with a positive bias of 0.150 m. The HY-2B wind speed measurements show a RMSE of 1.139 m/s with a negative bias of 0.563 m/s. The SSH, SWH and wind speed measurements from HY-2B products show high accuracy, but should be further calibrated.