Empirical algorithms have become the mainstream of significant wave height (SWH) retrieval from synthetic aperture radar (SAR). But the plentiful features from multi-polarizations make the selection of input for the empirical model a problem. Therefore, the XGBoost models are developed and evaluated for SWH retrieval from polarimetric Gaofen-3 wave mode imagettes using the SAR features of different polarization combinations, and then the importance of each feature on the models is further discussed. The results show that the reliability of SWH retrieval models is independently confirmed based on the collocations of the SAR-buoy and SAR-altimeter. Moreover, the combined-polarization models achieve better performance than single-polarizations. In addition, the importance of different features to the different polarization models for SWH inversion is not the same. For example, the normalized radar cross section (NRCS), cutoff wavelength (λ_c), and incident angle (θ) have more decisive contributions to the models than other features, while peak wavelength (λ_p) and the peak direction (φ) have almost no contribution. Besides, NRCS of cross-polarization has a more substantial effect, and the λ_c of hybrid polarization has a stronger one than other polarization models.

The co-located normalized radar backscatter cross section measurements from the Global Precipitation Measurement (GPM) Ku/Ka-band dual-frequency precipitation radar (DPR) and sea surface wind; wave and temperature observations from the National Data Buoy Center (NDBC) moored buoys are used to analyze the dependence and sensitivity of Ku- and Ka-band backscatter on surface conditions at low-incidence angles. Then the potential for inverting wind and wave parameters directly from low-incidence σ⁰ measurements is discussed. The results show that the KaPR σ⁰ is more sensitive to surface conditions than the KuPR σ⁰ overall. Nevertheless; both the KuPR σ⁰ and KaPR σ⁰ are strongly correlated with wind speed (U₁₀) and average wave steepness (δ_a) with the exception of specific transitional incidence angles. Moreover, U₁₀ and δ_a could be retrieved from pointwise σ⁰ near nadir and near 18°. Near 18°; wind direction information is needed as the effect of wind direction on σ⁰ becomes increasingly significant with incidence angle. To improve the performance of U₁₀ retrieval; especially for low U₁₀; auxiliary δ_a information would be most helpful; and sea surface temperature is better taken into account. Other wave parameters; such as significant wave height; wave period and wave age; are partly correlated with σ⁰. It is generally more difficult to retrieve those parameters directly from pointwise σ⁰. For the retrieval of those wave parameters; various auxiliary information is needed. Wind direction and wave direction cannot be retrieved from pointwise σ⁰.

Significant wave height (SWH) is one of the most important descriptors for ocean wave fields. The polynomial regression (PolR) and Gaussian process regression (GPR) models are implemented to explore the effects of polarization and incidence angles on the SWH estimation from multi-incidence angle quad-polarization Gaofen-3 SAR wave mode data, based on the collocated data set of approximately 12,000 Gaofen-3 wave mode imagettes, matched with SWH from the fifth generation reanalysis (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The results show that the model performance improves, as long as polarimetry information increases. The hybrid polarizations perform stronger than the co-polarizations or cross-polarizations alone, and they show better performance over the low to high seas. The lower incidence angles are more favorable for SAR SWH inversion. It is superior to introduce incidence angle in piecewise way, rather than to include it as an independent variable in the models. Then, the final PolR and GPR models, with the superior input scheme that includes the quad-polarized features and introduces the incidence angle in piecewise way, are assessed independently through a comparison with observations from altimeter and buoys. The accuracies of our SWH estimates are comparable or even higher than other published results. The GPR model outperforms the PolR model, due to the superiority of the added nonlinearity of GPR.

The present study utilizes the backward propagation neural network (BPNN) and the dual-branch convolutional neural network (DB-CNN) algorithms to construct models for estimating significant wave height (SWH) from polarimetric Gaofen-3 SAR wave mode data, using a dataset of 11 164 images that are collocated with SWH from the European Centre for Medium-Range Weather Forecasts fifth generation reanalysis (ERA5). The models are assessed and compared across nine polarizations [vertical–vertical (VV), horizontal–horizontal (HH), RL, vertical–horizontal (VH), horizontal-vertical (HV), RR, 45° linear, RV, and RH] and various sea states using the SAR-ERA5 test samples as well as buoy and altimeter SWH observations. The results demonstrate the robust performance of BPNN models, with RMSEs around 0.30–0.32 m on SAR-ERA5 test data, 0.32–0.48 m on buoy data, 0.40–0.48 m on Jason-3 data, and 0.36–0.42 m on SARAL data. By comparison, the DB-CNN models, which additionally include two-dimensional (2-D) image spectra as input, only exhibit improved performance at VV, 45° linear, and RL polarizations, while showing negligible improvement at HH, RV, and RH polarizations and a notable degradation at VH, HV, and RR polarizations. Furthermore, the DB-CNN models generally fail to improve the overestimation (underestimation) in low (high) seas, and they even aggravate the overestimation (underestimation) under most polarizations. Additionally considering the heightened complexity, increased vulnerability to overfitting, and training times that are more than 200 times longer, the use of complex deep learning network structures to incorporate 2-D spectral information appears to be operationally limited for SAR SWH retrieval.

Synthetic Aperture Radar (SAR) is widely used for observing sea surfaces and retrieving two-dimensional wave spectra. However, existing methods for retrieving directional wave spectra from SAR imagettes face challenges due to the complex non-linear SAR-wave imaging relationship and the limitation of first-guess spectra. This study proposes a novel twostage machine learning strategy for retrieving two-dimensional directional wave spectra from Chinese Gaofen-3 SAR wave mode products. We achieve the generation of complete two-dimensional wave spectra and several wave parameters solely from GF-3 SAR data without necessitating any additional inputs. In the first stage, we employ the Energy Attention Conditional Generative Adversarial Network (EA-CGAN) to retrieve the normalized wave spectrum. The generator of the EA-CGAN establishes a non-linear transformation from normalized SAR cross spectra to normalized wave spectra to enhance the capabilities. In the second stage, the XGBoost model retrieves the intensity of the wave spectrum. The EA-CGAN and XGBoost models were trained on an extensive dataset that consists of about 11,000 Gaofen-3 SAR wave mode imagettes and two-dimensional wave spectra from the fifth-generation reanalysis (ERA-5) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The results of the evaluation using test samples reveal high consistency between the retrieved wave spectra and ERA-5 wave spectra in terms of spectral similarity, peak period, peak direction, significant wave height, and mean wave periods. Compared to the traditional methods, our approach offers enhanced effectiveness, demonstrating the potential of advanced deep learning in highprecision SAR wave spectrum inversion.

This study presents a new 0.25° gridded 6-hourly global ocean surface wind vector dataset from 2000 to 2015 produced by blending satellite wind retrievals from five active scatterometers (QuikSCAT, ASCAT-A, ASCAT-B, OSCAT, and HY-2A), nine passive radiometers (four SSM/I sensors, two SSMIS sensors, TMI, AMSR-E, and AMSR2) and one polarimetric radiometer (WindSat) with reanalysis from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) employing an optimum interpolation method (OIM). The accuracy of this wind product is determined through various comparisons with buoy measurements, NCEP/NCAR reanalysis and the cross-calibrated multi-platform (CCMP) winds. The comparisons indicate that OIM winds agree well with buoys, showing a root-mean-squared difference of 1.32 m s⁻¹ for wind speed and 24.73° for wind direction over 0–30 m s⁻¹ wind speed range. And the quality of OIM winds is improved significantly relative to NCEP/NCAR reanalysis and can be comparable with CCMP winds. Furthermore, OIM winds can reveal abundant small-scale features that are not visible in reanalysis data. In addition, the wind speed and direction retrievals of most satellites are proved to play an important role in generating the high-quality product, but the procedure for including HY-2A winds and WindSat wind directions should be further explored.