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The main purpose of this letter is to investigate the performance of the calibration of the Chinese civilian satellite carrying synthetic aperture radar (SAR), denoted as 1mC-SAR, which is a successor of the previous Gaofen-3 (GF-3) satellite. In total, more than 500 1mC-SAR images in co-polarization modes (vertical–vertical [VV] and horizontal–horizontal [HH]) are collocated with the wind products from Haiyang-2 (HY-2B/2C/2D) scatterometers in April – May 2023. The well-developed geophysical model function (GMF) CMOD7 and polarization ratio PR2011 in the C-band are used to simulate the normalized radar cross section (NRCS) employing the wind vector from HY-2. There are more than 2000 matchups for VV-polarization and more than 1000 matchups for HH-polarization. Comparison of the simulations and 1mC-SAR measurements yields a root mean squared error (RMSE) of 3.03 dB, a correlation coefficient (r) of 0.93, a scatter index (SI) of −0.14, and a bias of −2.22 dB for the VV-polarized NRCS, which is slightly better than the 3.68 dB RMSE, 0.90 r, −0.15 SI, and −3.08 dB bias of the HH-polarized NRCS. The variations in the difference in the VV-polarized NRCS (1mC-SAR measurements minus simulations) with respect to the wind speed and incidence angle are analysed. Despite the difference in the VV-polarization (i.e. <2 dB) at wind speeds of greater than 3 m s−1, the difference in the VV-polarization approaches 3 dB at several incidence angles, which needs to be further improved. [ABSTRACT FROM AUTHOR]

In this study, a machine learning approach is applied to wind speed retrieval from vertical-vertical (VV) polarized synthetic aperture radar (SAR) during tropical cyclones (TC). More than 2400 dual-polarized (VV and vertical-horizontal (VH)) Sentinel-1 (S-1) images acquired in interferometric wide (IW) and extra wide (EW) mode are collected in 2016–2022. Along-track observations from stepped-frequency microwave radiometer (SFMR) and soil moisture active passive (SMAP) are available for several images. The azimuthal cut-off wavelength (ACW) is derived from VV-polarized image and the Doppler centroid anomaly (DCA) is provided from Ocean (OCN) product. It is found that SFMR wind speed linearly relates with ACW and DCA. Following this rationale, a machine learning method, called eXtreme Gradient Boosting (XGBoost), is trained for constructing SAR wind speed retrieval geophysical model function (GMF) through abundant matchups from 2000 images collocated with wind speeds from soil moisture active passive (SMAP) microwave radiometer and SFMR, denoted as TCWIND2-S1. Then GMF TCWIND2-S1 is applied for more than 400 images and the validation against SMAP and SFMR products up to 70 m s−1 shows a 3.01 m s−1 root mean squared error (RMSE) with a 0.16 scatter index (SI) and a 0.92 correlation (COR). Retrieval is also compared with CyclObs wind products derived from more than 400 dual-polarized images, yielding a 2.66 m s−1 RMSE with a 0.19 SI and a 0.93 COR. Our work provides an approach for VV-polarized SAR wind retrieval at wind speed ranged from 0 to 70 m s−1 during TCs. This work is conveniently adopted for TerraSAR-X without operating at cross polarization channel. [ABSTRACT FROM AUTHOR]

In this letter, we present a validation of the wind speed retrieval from data from the calibration microwave radiometer onboard the Haiyang-2B (HY-2B) satellite against the observations from the stepped-frequency microwave radiometer (SFMR) onboard hurricane aircraft of the National Oceanic and Atmospheric Administration (NOAA) during 50 tropical cyclones (TCs). These data were collected during the cyclone season for the period from 2019 to 2022. The validation yields a root mean square error (RMSE) of 2.99 m s−1, a correlation coefficient (r) of 0.69, and a scatter index (SI) of 0.21 for the wind speed at wind speeds of <25 m s−1, which are worse than those achieved through the comparison with the HY-2B scatterometer, i.e. an RMSE of 1.15 m s−1, an r value of 0.96, and an SI of 0.11. This is probably caused by the fact that the brightness temperature measurement from the calibration microwave radiometer is likely affected by whitecaps caused by wave breaking; however, whitecaps have less influence on the sea surface backscattering signal under a regular sea state. At wind speeds of >25 m s−1, the accuracy is reduced to an RMSE of 2.19 m s−1, an r value of 0.75, and an SI of 0.07. In the presence of rain, significant distortion was observed, i.e. a variation of 3 m s−1 for a rain rate of 15 mm hr−1. This behaviour was also observed in the analysis of the HY-2B scatterometer product at low-to-moderate wind speeds. In this sense, it is believed that correction associated with rain has to be conducted in wind retrieval from HY-2B data. [ABSTRACT FROM AUTHOR]

In this paper, sea surface waves during complex sea state (i.e. tropical cyclones (TCs) and Kuroshio current) are inverted from dual-polarized (vertical – vertical (VV) and vertical – horizontal (VH)) synthetic aperture radar (SAR) images using an existing algorithm, namely, the parameterized first-guess spectrum method (PFSM). In particular, the wind term is included in two modulation transfer functions (MTFs), i.e. tilt modulation and velocity bunching. More than 3000 Sentinel (S-1) images acquired in interferometric wide (IW) mode and extra wide (EW) mode are collected, and the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Oceans (IFREMER) is collected corresponding to the TC images. Note that these images are located within 500 km away from TC eyes. The wave spectra during the TCs are then inverted using the PFSM algorithm and the SAR-derived wind product. Simultaneously, the 0.05° gridded wave spectra collocated with these images are simulated using a third-generation numeric wave model, called WAVEWATCH-III (WW3), in which the reconstructed wind based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Marine Environment Monitoring Service (CMEMS) current, and the CMEMS sea level are treated as the forcing field. Comparison of the SAR wave retrievals and simulations reveals that the root mean square errors (RMSEs) of the significant wave height (SWH) and mean wave period (MWP) are 0.55 m and 0.43 s, their correlation coefficients (COR) are 0.94 and 0.92, and their scatter index (SI) values are 0.17 and 0.07. These results demonstrate the better performance of the PFSM algorithm using theoretical MTFs, i.e. an RMSE of 0.68 m and COR of 0.90 for the SWH and an RMSE of 0.60 s and a COR of 0.87 for the MWP. Although the accuracy of the wave retrieval from the SAR image under complex sea state is improved when implementing the tilt and velocity bunching MTFs with the strong wind term, SWH retrievals by algorithm PFSM suffer saturation problem at SWH > 8 m. [ABSTRACT FROM AUTHOR]

In this study, the feasibility of current retrieval from Sentinel-1 (S-1) synthetic aperture radar (SAR) in the radar look/range direction is investigated. S-1 Ocean (OCN) products acquired in interferometric wide (IW) mode in the regions with the western boundary current, i.e., the western Pacific and western Atlantic, are collected for the period from 2020 to 2022, which are collocated with the current field from HYbrid Coordinate Ocean Model (HYCOM). The OCN wind, HYCOM current, and Stokes drift estimated from the OCN wave parameters are geometric projected to be the range direction. In addition, the Doppler centroid anomaly (DCA) is estimated using the difference between the radar return Doppler frequency and the predicted Doppler shift, which are derived from the OCN products. The dependences of the upper ocean dynamics in the range direction on the DCA are studied, and it is found that the range Stokes drift, wind speed, and current speed are linearly related to the DCA. Based on deep learning, denoted as multi-layer perceptron, a range current retrieval algorithm from the OCN product is developed using two-thirds of the collocated dataset, and the root mean square error (RMSE) of the range current speed converges to 0.15 ms−1. In particular, the wave-induced surface Stokes drift is considered in the process. Comparison of one-third of the dataset yields an RMSE of 0.14 ms−1 for the range current speed, a correlation coefficient (r) of 0.85, and a bias of −0.001 ms−1. Validation against several moored buoys shows an RMSE of 0.12 ms−1 with a r of 0.74 and a bias of −0.01 ms−1. Under this circumstance, it is believed that the algorithm used in this study is applicable for range current retrieval from the S-1 OCN product. [ABSTRACT FROM AUTHOR]

A wind field reconstruction method for dual-polarized (vertical-vertical [VV] and vertical-horizontal [VH]) Sentinel-1 (S-1) synthetic aperture radar (SAR) images collected during tropical cyclones (TCs) that does not require external information is proposed. Forty S-1 images acquired in interferometric-wide (IW) and extra-wide (EW) modes during the Satellite Hurricane Observation Campaign in 2015–2022 were collected. Stepped-frequency microwave radiometer (SFMR) observations made onboard the National Oceanic and Atmospheric Administration's hurricane aircraft are available for 13 images. The geophysical model functions, namely VV-polarized C-SARMOD and cross-polarized S-1 IW/EW mode wind speed retrieval model after noise removal (S1IW.NR/S1EW.NR), were employed to invert the wind fields from the collected images. TC wind fields were reconstructed based on SAR-derived winds, enhancing TC intensity representation in the VV-polarized SAR retrievals and minimizing the error of the VH-polarized SAR retrievals at the sub-swath edge. The wind speeds retrieved from the SAR IW image were validated against the remote-sensing products from the soil moisture active passive (SMAP) radiometer, yielding a root mean squared error (RMSE) of approximately 4.3 m s−1, which is slightly smaller than the RMSE (4.4 m s−1) for the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Sea (IFREMER). However, the CyclObs wind product has better performance than the approach proposed in this paper for the S-1 EW mode. Moreover, the RMSE of the wind speed between SAR-derived wind speed obtained using the proposed approach and the CyclObs wind product is within 3 m s−1 in all flow directions clockwise relative to north centered on the TC's eye. This study provides an alternative method for TC wind retrieval from dual-polarized S-1 images without suffering saturation problem and external information; however, the pattern of the wind field around the TC's eye needs to be further improved, especially at the head and back of the TC's eye. [ABSTRACT FROM AUTHOR]

The aim of our work is to improve the modulation transfer function (MTF) of velocity bunching by the quad-polarized GF-3 synthetic aperture radar (SAR). In total, more than 10,000 Gaofen-3 (GF-3) images acquired in wave mode (WV) in quad-polarization are collocated with ocean waves simulated from WAVEWATCH-III (WW3) model and ocean currents from the HYbrid Coordinate Ocean Model (HYCOM). The validation of WW3-simulated significant wave height (SWH) against the measurements from National Data Buoy Center (NDBC) of National Oceanic and Atmospheric Administration (NOAA) yields a 0.48 m root-mean-square error (RMSE) of SWH with a 0.88 correlation coefficient (r). The winds are inverted using the geophysical model function (GMF), denoted as CSARMOD-GF, which is specially for the re-calibrated GF-3 SAR in vertical-vertical (VV) polarization. The azimuthal cut-off wavelength λc caused by velocity bunching is theoretically calculated by wave spectrum from WW3 model and practically estimated from SAR intensity image, respectively. It is found that the velocity bunching is independent with polarization; however, the bias (theoretic minus SAR-derived λc) increases with sea state growing and has no obvious relationship with current. Following this rationale, the MTF of velocity bunching is empirically constructed by including the term of wind speed and the analysis shows a 3.76 m RMSE with a 0.92 r through comparing the SAR-derived λc in VV polarization and simulated values. Under this circumstance, the accuracy of wave retrieval is anticipated to be improved when using the proposed MTF of velocity bunching. [ABSTRACT FROM AUTHOR]

In this paper, an essential modulation transfer function (MTF) on synthetic aperture radar (SAR), denoted as tilt modulation, is improved through five vertical–vertical (VV) polarized Gaofen-3 (GF-3) SAR images acquired in Global Observation (GLO) and Wide ScanSAR (WSC) mode that were captured during the 2017 tropical cyclone (TC) season in the western Pacific Ocean. These images are reprocessed to be Level-1B products and collocated with the wind fields from Global and Regional Assimilation and Prediction System – Typhoons Model. More than 9,000 matchups are exploited for studying the dependence of tilt on the wind speed and incidence angle. It is found that quantitative tilt MTF (herein denoted as the derivative scattering coefficient), which is proportional to first-order derivative normalized radar cross-section with respect to incidence angle, is inversely related with the incidence angle between 14° and 46°. In addition, the wind speed has an obvious influence on the derivative scattering coefficient due to each scattering facet essentially being modulated by strong wind-induced long-waves, whereas traditional tilt MTF does not conform to the actual situation. However, the sensitivity of wind speed on the derivative scattering coefficient weakens at larger incidence angles (>40°). Following this rationale, the empirical tilt MTF in VV polarization has been developed. The reanalysis results indicate that the values from empirical tilt MTF could depict the role of wind. The improvement herein could overcome the limitation in tilt MTF, namely, the lack of a strong wind's influence, and it is expected to be implemented for SAR wave retrieval in TCs. [ABSTRACT FROM AUTHOR]

The strong winds in tropical cyclones (TCs) are commonly retrieved from cross-polarized SAR images using a geophysical model function (GMF). However, the accuracy of wind retrieval in cross-polarization is significantly reduced at the edges of sub-swaths. In this study, a novel approach to TC wind retrieval from VV polarized SAR images is proposed based on using the azimuthal cutoff wavelength to represent the effect of velocity bunching. A total of 12 dual-polarized (VV and VH) Sentinel-1 (S-1) images acquired in the interferometric wide (IW) mode were used, five of which were collocated with measurements taken by the Stepped-Frequency Microwave Radiometer (SFMR) on board an NOAA aircraft. The SAR-based azimuthal cutoff wavelengths were found to be linearly related to the SFMR wind speeds. Based on this finding, an empirical GMF for TC wind speed retrieval from VV S-1 images was constructed. The inversion results from seven images using this approach were validated against the wind products from the Advanced Scatterometer and the European Center for Medium-Range Weather Forecasts. The RMSE of the wind speed was 2.15 m s−1 and the correlation coefficient (COR) was 0.83 at wind speeds of less than 25 m s−1, while the RMSE was 2.66 m s−1 and the COR was 0.97 when compared with wind retrieval using the VH-polarized GMF S1IW.NR at wind speeds greater than 25 m s−1. The proposed algorithm performs well and has two advantages: (1) it is not subject to the saturation problem of the VV backscattering signal and (2) the discontinuity of the retrieval results obtained using VH GMF at the edges of sub-swaths is improved. [ABSTRACT FROM AUTHOR]

The goal of this study was to investigate the performance of a spectral-transformation wave retrieval algorithm and confirm the accuracy of wave retrieval from C-band Chinese Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) images. More than 200 GF-3 SAR images of the coastal China Sea and the Japan Sea for dates from January to July 2020 were acquired in the Quad-Polarization Strip (QPS) mode. The images had a swath of 30 km and a spatial resolution of 8 m pixel size. They were processed to retrieve Significant Wave Height (SWH), which is simulated from a numerical wave model called Simulating WAves Nearshore (SWAN). The first-guess spectrum is essential to the accuracy of Synthetic Aperture Radar (SAR) wave spectrum retrieval. Therefore, we proposed a wave retrieval scheme combining the theocratic-based Max Planck Institute Algorithm (MPI), a Semi-Parametric Retrieval Algorithm (SPRA), and the Parameterized First-guess Spectrum Method (PFSM), in which a full wave-number spectrum and a non-empirical ocean spectrum proposed by Elfouhaily are applied. The PFSM can be driven using the wind speed without calculating the dominant wave phase speed. Wind speeds were retrieved using a Vertical-Vertical (VV) polarized geophysical model function C-SARMOD2. The proposed algorithm was implemented for all collected SAR images. A comparison of SAR-derived wind speeds with European Center for Medium-Range Weather Forecasts (ECMWF) ERA-5 data showed a 1.95 m/s Root-Mean-Squared Error (RMSE). The comparison of retrieved SWH with SWAN-simulated results demonstrated a 0.47 m RMSE, which is less than the 0.68 m RMSE of SWH when using the PFSM algorithm. [ABSTRACT FROM AUTHOR]

Empirical wind-retrieval algorithms for synthetic aperture radar (SAR), such as the geophysical model function (GMF) CMOD5N in the C-Band, are currently well developed. The retrieval accuracy of GMFs, however, is lower in the presence of marine phenomena such as oceanic fronts and mesoscale eddies, making SAR wind retrieval challenging under these conditions. In this study, we proposed a scheme for wind retrieval from Chinese Gaofen-3 (GF-3) SAR images using an analytical approach to obtain the wind speed in the nearshore waters of China's seas. Approximately 300 images were acquired from 2018 to 2021 in the copolarization channels [vertical–vertical (VV) and horizontal–horizontal (HH)]. The images were collocated with the measurements from the Advanced Scatterometer (ASCAT) and the Chinese Haiyang-2B (HY-2B) scatterometer. Our analytical approach was based on the Bragg resonant and non-Bragg (NB) scattering model for the calculation of the normalized radar backscatter cross-section (NRCS), which accounted for the influence of wave-current interactions and breaking waves in nearshore waters. We used the sea surface currents obtained from the HYbrid Coordinate Ocean Model (HYCOM) to simulate the Bragg resonant roughness. We derived the contribution of the NB breaking waves from the SAR-measured NRCS values in both the VV and HH channels. By comparing the retrieval results obtained using the analytical approach with the scatterometer products, we concluded that the root mean square error (RMSE) of the wind speed was 2.01 m s−1 with a 0.91 correlation coefficient (COR), which was less than the 2.93 m s−1 RMSE and 0.72 COR for the GMF CMOD5N. The variation in the bias of the HYCOM currents was approximately 1 m s−1 for current speeds greater than 0.3 m s−1. These findings verified the stability of the analytical approach for SAR wind retrieval in complex sea states. [ABSTRACT FROM AUTHOR]

In this study, the non-Bragg (NB) scattering due to breaking waves as measured by the C-band synthetic aperture radar (SAR) is investigated using more than 300 Gaofen-3 (GF-3) SAR images, which were acquired in quad-polarization stripmap (QPS) mode, that is, co-polarization [vertical–vertical (VV) and horizontal–horizontal (HH)] and cross-polarization [vertical–horizontal (VH) and horizontal–vertical (HV)]. First, the quality of SAR-based wind estimation is checked against the Haiyang-2B (HY-2B) scatterometer and European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5), indicating a wind speed accuracy of 1.62 m s−1 root-mean-square error (RMSE) and a 0.89 correlation. Then, the SAR-derived wind and HYbrid Coordinate Ocean Model (HYCOM) sea surface current are used to simulate Bragg resonant roughness. The non-polarized (NP) wave breaking contribution σwb on co-polarized SAR-measured normalized radar cross section (NRCS) σ0 is studied, which is derived using two methods: an approach of the Bragg theory and empirical function. Numerical simulations are contrasted with actual SAR measurements: they show that the theoretical-based approach provides accurate enough simulations of the NP contribution, especially at the HH-polarization channel. To deeply understand the behavior of sea surface scattering under breaking conditions, the third-generation WAVEWATCH-III (WW3) model is used to simulate wake-breaking parameters, i.e. whitecap coverage (WCC), whitecap foam thickness (WCT) and whitecap breaking height (WCH), which are then collocated with SAR images. The difference between simulated co-polarized NRCS and the measured one versus sea surface dynamics parameters (i.e. SAR-derived wind speed, HYCOM sea surface speed, and WW3-simulated significant wave height) shows that NP enhances HH-polarized backscattering, while it damps the VV-polarized backscattering. In addition, the contribution of σwb could be ignored for WCC and WCT larger than 15 × 10−3 and 40 × 10−3 m, respectively. Moreover, the ratio reduces with the increasing WCH greater than 2.0 m; in particular, the ratio likely remains to be 0.1 as WCH is greater than 2.5 m. Generally, the HH-polarized backscattering is relatively sensitive with the wave-breaking parameters; however, this behavior has to be further studied utilizing buoy-measured wave breaking data. [ABSTRACT FROM AUTHOR]

In this study, a re-tuned algorithm based on the geophysical model function (GMF) C-SARMOD2 is proposed to retrieve wind speed from Synthetic Aperture Radar (SAR) imagery collected by the Chinese C-band Gaofen-3 (GF-3) SAR. More than 10,000 Vertical-Vertical (VV) and Horizontal-Horizontal (HH) polarization GF-3 images acquired in quad-polarization stripmap (QPS) and wave (WV) modes have been collected during the last three years, in which wind patterns are observed over open seas with incidence angles ranging from 18° to 52°. These images, collocated with wind vectors from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis at 0.125° resolution, are used to re-tune the C-SARMOD2 algorithm to specialize it for the GF-3 SAR (CSARMOD-GF). In particular, the CSARMOD-GF performs differently from the C-SARMOD2 at low-to-moderate incidence angles smaller than about 34°. Comparisons with wind speed data from the Advanced Scatterometer (ASCAT), Chinese Haiyang-2B (HY-2B) and buoys from the National Data Buoy Center (NDBC) show that the root-mean-square error (RMSE) of the retrieved wind speed is approximately 1.8 m/s. Additionally, the CSARMOD-GF algorithm outperforms three state-of-the-art methods – C-SARMOD, C-SARMOD2, and CMOD7 – that, when applied to GF-3 SAR imagery, generating a RMSE of approximately 2.0–2.4 m/s. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to quantitatively examine the characteristics of suspended sediment on synthetic aperture radar (SAR) observations of sea roughness. In this work, seven Sentinel-1 (S-1) SAR images in 2019 acquired in the Interferometric Wide-swath (IW) mode at vertical-vertical (VV) polarization channel were collected, where Yangtze River freshwater dominants and suspended sediment concentration (SSC) are higher in continental shelf of East China Sea (ECS). These images were collocated with waves simulated from WAVEWATCH-III (WW3) and SSC inverted from the Haiyang-1C (HY-1C) Coastal Zone Imager (CZI) image. The comparison of WW3-simulated SWH yielded a 0.45-m root-mean-square error (RMSE) with a 0.78 correlation (COR) when comparing the simulations with the measurements from Haiyang-2B (HY-2B). The SAR-measured normalized radar cross-section (NRCS) is linearly related with the HY-1 C SSC at SSC > 1200 mg l–1, which is caused by the change of dielectric constant of sea water and viscosity. Moreover, the NRCS is simulated by the three-scale radar backscattering model. It is generally found that HY-1 C SSC enhances the NRCS in difference between simulations and observations up to 8 dB at SSC of 2000 mg l–1, which could distort the accuracy of SAR wind retrieval. [ABSTRACT FROM AUTHOR]

This work focused on the rain-induced characteristics of synthetic aperture radar (SAR) at the C- and X-bands. Six SAR images were collected in tropical cyclones: four C-band dual-polarized Sentinel-1 (S-1) images during the 2016 hurricane season and two vertical–vertical) polarized TerraSAR-X (TS-X) images of Hurricane Sandy (2012). Wind speeds were retrieved from the S-1 images at the vertical–horizontal polarization channel, and winds from the TS-X images were obtained from the Hurricane Research Division of the National Oceanic and Atmospheric Administration. Simultaneously, Tropical Rainfall Measuring Mission (TRMM) rain rates, current data from the HYbrid Coordinate Ocean Model and wave spectra simulated by the WAVEWATCH-III (WW3) model were collocated. The three-scale backscattering model was employed to simulate the normalized radar cross-section (NRCS)without considering the rain effect, yielding a 2.21-dB root mean square errorand 2.23-dB for S-1 and TS-X, respectively. The difference between model-simulated and observed NRCS was analysed using the TRMM rain rate. The results indicate a linear relationship of the difference at the X-band with the TRMM rain rate, while the difference at the C-band exhibited a 'V' relationship with the TRMM rain rate. It was also discovered that the difference at the X-band decreased with increasing wind speed, while the difference at the C-band increased with increasing wind speed. [ABSTRACT FROM AUTHOR]

The spatial resolution of Chinese Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) image, a pixel size of 500 m at azimuth/flight direction for Global Observation (GLO) and 100 m for Wide ScanSAR (WSC) modes, is relatively coarse, and waves shorter than 100 m are undetectable. In this study, SAR images collected under typhoon conditions by the GF-3 mission are exploited for wave retrieval purposes. In detail, six imagery collected during 2017 and 2018 over the China Seas captured four typhoons. The GLO and WSC modes images are collocated with simulated wave fields using a numeric wave model, called WAVEWATCH-III (WW3) and are processed using a machine learning method to retrieve sea surface waves. In this work, the criterion for the training completion in the process of machine learning is set as 0.15 m of Root mean Square Error (RMSE) for the Significant Wave Height (SWH). Then, the retrieved SWH is compared with measurements collected by the Jason-2 altimeter, showing a 0.61 m RMSE. The proposed algorithm is shown to outperform empirical approaches developed to retrieve the SWH using GF-3 SAR imagery acquired in the GLO and WSC imaging modes. [ABSTRACT FROM AUTHOR]

Our work describes the accuracy of Chinese quad-polarization Gaofen-3 (GF-3) synthetic aperture radar (SAR) wave mode data for wave retrieval and provides guidance for the operational applications of GF-3 SAR. In this study, we evaluated the accuracy of the SAR-derived significant wave height (SWH) from 10,514 GF-3 SAR images with visible wave streaks acquired in wave mode by using the existing wave retrieval algorithms, e.g., the theoretical-based algorithm parameterized first-guess spectrum method (PFSM), the empirical algorithm CSAR_WAVE2 for VV-polarization, and the algorithm for quad-polarization (Q-P). The retrieved SWHs were compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis field with 0.125° grids. The root mean square error (RMSE) of the SWH is 0.57 m, found using CSAR_WAVE2, and this RMSE value was less than the RMSE values for the analysis results achieved with the PFSM and Q-P algorithms. The statistical analysis also indicated that wind speed had little impact on the bias with increasing wind speed. However, the retrieval tended to overestimate when the SWH was smaller than 2.5 m and underestimate with an increasing SWH. This behavior provides a perspective of the improvement needed for the SWH retrieval algorithm using the GF-3 SAR acquired in wave mode. [ABSTRACT FROM AUTHOR]

In this study, a total of 37 images of Landsat Operational Land Imager/Thematic Mapper/Enhanced Thematic Mapper plus were adopted to delineate the qualitative changes of Suspended Sediment Concentration (SSC) in Hangzhou Bay, China. Combined with in-situ SSC, remote sensing reflectance of the water (Rrs), water depth and simulated currents, the influence of both seabed topography and tidal currents on the SSC distribution was analysed. The results showed: (1) four High SSC Areas (HSA) and two Low SSC areas (LSA) in Hangzhou Bay. (2) SSC has a negative correlation with bathymetry, which is especially obvious during mid to late period of flood tide. HSAs appear in the shallow water (3–7 m depth) area, while the LSAs distribute in the deep water area (10–15 m depth). (3) The surface SSC distribution during the mid to late period of flood tide can help us estimate the topography information. The results of this paper can be used to other coastal embayments similar to Hangzhou Bay. [ABSTRACT FROM AUTHOR]

In this article, the polarization ratio (PR) of TerraSAR-X (TS-X) vertical–vertical (VV) and horizontal–horizontal (HH) polarization data acquired over the ocean is investigated. Similar to the PR of C-band synthetic aperture radar (SAR), the PR of X-band SAR data also shows significant dependence on incidence angle. The normalized radar cross-section (NRCS) in VV polarization data is generally larger than that in HH polarization for incidence angles above 23°. Based on the analysis, two PR models proposed for C-band SAR were retuned using TS-X dual-polarization data. A new PR model, called X-PR hereafter, is proposed as well to convert the NRCS of TS-X in HH polarization to that in VV polarization. By using the developed geophysical model functions of XMOD1 and XMOD2 and the tuned PR models, the sea surface field is retrieved from the TS-X data in HH polarization. The comparisons within situbuoy measurements show that the combination of XMOD2 and X-PR models yields a good retrieval with a root mean square error (RMSE) of 2.03 m s–1and scatter index (SI) of 22.4%. A further comparison with a high-resolution analysis wind model in the North Sea is also presented, which shows better agreement with RMSE of 1.76 m s–1and SI of 20.3%. We also find that the difference between the fitting of the X-PR model and the PR derived from TS-X dual-polarization data is close to a constant. By adding the constant to the X-PR model, the accuracy of HH polarization sea surface wind speed is further improved with the bias reduced by 0.3 m s–1. A case acquired at the offshore wind farm in the East China Sea further demonstrates that the improvement tends to be more effective for incidence angles above 40°. [ABSTRACT FROM AUTHOR]