The purpose of this study is to include rain effects in wind field retrieval from C-band synthetic aperture radar (SAR) imagery collected under tropical cyclone conditions. An effective and operationally attractive approach to detect rain cells in SAR imagery is proposed and verified using four Sentinel-1 (S-1) SAR images collected in dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) interferometric-wide swath imaging mode during the Satellite Hurricane Observation Campaign. SAR images were collocated with ancillary observations that include sea surface wind and rain rate from the Stepped-Frequency Microwave Radiometer (SFMR) on board of the National Oceanic and Atmospheric Administration aircraft. The winds are inverted from VV- and VH-polarized S-1 image using the CMOD5.N and S1IW.NR geophysical model functions (GMFs), respectively. Location and radius of cyclone’s eye, together with the TC central pressure, are calculated from the VV-polarized SAR-derived wind and a parametric model. A cost function is proposed that consists of the difference between the measured VV-polarized SAR normalized radar cross section (NRCS) and the NRCS predicted using CMOD5.N forced with the wind speed retrieved by the VH-polarized SAR images using S1IW.NR GMF and the wind direction retrieved from the patterns visible in the SAR image. This cost function is related to the SFMR rain rate. Experimental results show that the difference between measured and predicted NRCS values range from 0.5 dB to 5 dB within a distance of 100 km from the cyclone’s eye, while the difference increases spanning from 3 dB to 6 dB for distances larger than 100 km. Following this rationale, first the rain bands are extracted from SAR imagery and, then, the composite wind fields are reconstructed by replacing: (1) dual-polarized SAR-derived winds over the rain-free regions; (2) winds simulated using the radial-vortex model over the rain-affected regions. The validation of the composite wind speed against SFMR winds yields a 0.7 correlation (COR) at all flow directions up to retrieval speeds of 70 m s⁻¹. This result outperforms the winds estimated using the VH-polarized S1IW.NR GMF, which call for high error accuracy, such as about 4 m s⁻¹ with a 0.45 COR ranged from 330° to 360°.

In this work, three types of machine learning algorithms are applied for synthetic aperture radar (SAR) wind retrieval in tropical cyclones (TCs), and the optimal method is confirmed. In total, 30 Sentinel-1 (S-1) images in dual-polarization (vertical–vertical [VV] and vertical–horizontal [VH] were collected during the period from 2016 to 2021, which were acquired in interferometric-wide and extra-wide modes with pixels of 10 m and 40 m, respectively. More than 100,000 sub-scenes with a spatial coverage of 3 km are extracted from these images. The dependences of variables estimated from sub-scenes, i.e., VV-polarized and VH-polarized normalized radar cross-section (NRCS), as well as the azimuthal wave cutoff wavelength, on wind speeds from the stepped-frequency microwave radiometer (SFMR) and the soil moisture active passive (SMAP) radiometer are studied, showing the linear relations between wind speed and these three parameters; however, the saturation of VV-polarized NRCS and the azimuthal wave cutoff wavelength is observed. This is the foundation of selecting input variables in machine learning algorithms. Two-thirds of the collocated dataset (20 images) are used for training the process using three machine learning algorithms, i.e., eXtreme Gradient Boosting (XGBoost), Multi-layer Perceptron, and K-Nearest Neighbor, and the coefficients are fitted after training completion through 20 images collocated with SFMR and SMAP data. Another 10 images are taken for validation up to 70 m/s, yielding a 2.53 m/s root mean square error (RMSE) with a 0.96 correlation and 0.12 scatter index (SI) using XGBoost. The result is better than the >5 m/s error achieved using the existing cross-polarized geophysical model function and the other two machine learning algorithms; moreover, the comparison between wind retrievals using XGBoost and Level-2 CyclObs products shows about 4 m/s RMSE and 0.18 SI. This suggests that the machine learning algorithm XGBoost is an effective method for inverting the TC wind field utilizing SAR measurements in dual-polarization.

Oil spills lead to catastrophic problems. In most oil spill cases, the spatial and temporal intractability of the detriment cannot be neglected, and problems related to economic, social and environmental factors constantly appear for a long time. Remote sensing has been widely used as a powerful means to conduct oil spill detection. Optical polarization remote sensing, thriving in recent years, shows a novel potential for oil spill detection. This paper provides a demonstration of the use of open-source POLDER/PARASOL polarization time-series data to detect oil spill. The Deepwater Horizon oil spill, one of the largest oil spill disasters, is utilized to explore the potential of optical polarization remote sensing for oil spill detection. A total of 24 feature combinations are organized to quantitatively study the positive effect of adding polarization information and the appropriate way to describe polarization characteristics. Random forest classifier models are trained with different combinations, and the results are assessed by 10-fold cross-validation. The improvement from adding polarization characteristics is remarkable ((average) accuracy: +0.51%; recall: +2.83%; precision: +3.49%; F₁ score: +3.01%, (maximum) accuracy: +0.80%; recall: +5.09%; precision: +6.92%; F₁ score: +4.72%), and coupling between the degree of polarization and the phase angle of polarization provides the best description of polarization information. This study confirms the potential of optical polarization remote sensing for oil spill detection, and some detailed problems related to model establishment and polarization feature characterization are discussed for the further application of polarization information.

Satellite remote sensing data form the basis of ocean observation and applications. China has established a satellite network platform comprising ocean color satellite constellations, ocean dynamic environment satellite constellations, and ocean observation and monitoring satellite constellations. This platform provides consistent and reliable ocean observation data crucial for marine scientific research, economic development, and early warning and forecasting. This paper comprehensively describes the development process and plans for China’s ocean satellites from their inception. It offers detailed technical specifications of ocean satellites and outlines the current applications of ocean water color satellites (HY-1), ocean dynamics and environment satellites (HY-2), and ocean surveillance and monitoring satellites (GF-3) in ocean parameter inversion, target identification and detection, and early warning and forecasting. In the future, to enhance the level of industrialization in ocean remote sensing in China, it is imperative to leverage the diversity and timeliness of ocean remote sensing data. Additionally, emerging technologies such as cloud computing and artificial intelligence should be harnessed, and the application potential of various satellite data resources should be explored.

The main purpose of our work is to investigate the performance of wave breaking and its effect on wave retrieval in data acquired from the Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) at C-band and the German TerraSAR-X (TS-X) at X-band. The SAR images available for this study included 140 GF-3 images acquired in quad-polarization strip (QPS) mode and 50 dual-polarized (vertical-vertical (VV) and horizontal-horizontal (HH)) TS-X images acquired in stripmap (SM) mode. Moreover, these images were collocated with the waves simulated by the numeric WAVEWATCH-III (WW3) (version 5.16) model and HYbrid Coordinate Ocean Model (HYCOM) current. In particular, a few images covered the moored buoys monitored by the National Data Buoy Center (NDBC) of the National Oceanic and Atmospheric Administration (NOAA). The comparison between the WW3-simulated results and the significant wave heights (SWHs) from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA-5) showed that the correlation coefficient (COR) was 0.4–0.6 with a root mean squared error (RMSE) of about 0.2 m at SWHs of 0–4 m. The winds were inverted using VV-polarized geophysical model functions (GMFs), e.g., CSARMOD-GF for the GF-3 images and XMOD2 for the TS-X images. The Bragg resonant roughness in the normalized radar cross section (NRCS) was simulated using a radar backscattering model and the SAR-derived wind, WW3-simulated wave parameters, and HYCOM current. Then, the contribution of the non-polarized (NP) wave breaking to the SAR data was estimated by the VV-polarized NRCS, the HH-polarized NRCS, and the polarization ratio (PR) of the co-polarized Bragg resonant components in the NRCS. Because co-polarized Bragg resonant components in the NRCSs have poor results, due to the saturation for wind speeds greater than 20 m/s, the analysis of wave breaking is excluded at such conditions. The results revealed that the backscattering signal in the C-band was more sensitive to wave breaking than the backscattering signal in the X-band. Interestingly, the ratio had a linear correlation with wind speed. Moreover, the variation in the bias (inverted SWH minus WW3 simulation) showed that the bias increased as the wind speed (>8 m/s) and whitecap coverage (>0.005) increased. Following this rationale, wave retrieval during tropical cyclones should consider the influence of wave breaking.

This study introduces a new machine learning-based algorithm for the retrieving significant wave height (SWH) using synthetic aperture radar (SAR) images. This algorithm is based on the azimuthal cut-off wavelength and was developed in quad-polarized stripmap (QPS) mode in coastal waters. The collected images are collocated with a wave simulation from the numeric model, called WAVEWATCH-III (WW3), and the current speed from the HYbrid Coordinate Ocean Model (HYCOM). The sea surface wind is retrieved from the image at the vertical–vertical polarization channel, using the geophysical model function (GMF) CSARMOD-GF. The results of the algorithm were validated against the measurements obtained from the Haiyang-2B (HY-2B) scatterometer, yielding a root mean squared error (RMSE) of 1.99 m/s with a 0.82 correlation (COR) and 0.27 scatter index of wind speed. It was found that the SWH depends on the wind speed and azimuthal cut-off wavelength. However, the current speed has less of an influence on azimuthal cut-off wavelength. Following this rationale, four widely known machine learning methods were employed that take the SAR-derived azimuthal cut-off wavelength, wind speed, and radar incidence angle as inputs and then output the SWH. The validation result shows that the SAR-derived SWH by eXtreme Gradient Boosting (XGBoost) against the HY-2B altimeter products has a 0.34 m RMSE with a 0.97 COR and a 0.07 bias, which is better than the results obtained using an existing algorithm (i.e., a 1.10 m RMSE with a 0.77 COR and a 0.44 bias) and the other three machine learning methods (i.e., a >0.58 m RMSE with a <0.95 COR), i.e., convolutional neural networks (CNNs), Support Vector Regression (SVR) and the ridge regression model (RR). As a result, XGBoost is a highly efficient approach for GF-3 wave retrieval at the regular sea state.