Currently, global directional wave spectra are routinely providing by synthetic aperture radar (SAR) on Sentinel-1 in Wave Mode and real aperture radar, i.e., SWIM onboard CFOSAT. However, SAR spectra suffer from azimuth cut-off effects, while SWIM spectra exhibit parasitic peaks and 180° ambiguity. Leveraging the complementary of these two satellite radars for directional spectra merging remains an open scientific challenge. In this letter, we propose a deeply-learned based 2D wave spectrum fusion model aiming to integrate CFOSAT SWIM and Sentinel-1A wave mode Level-2 products. After spatiotemporal collocating, and rejecting suspicious spectra, two-year period (June 2022 to June 2024) of 2D wave spectra triplets (S-1, SWIM, and ERA5) are used for the fusion model training. The deep learning merged spectra show good consistency with ERA5 benchmark regarding 2D spectra, the derived omni - directional energy distribution and the integrated wave parameters. Comparative analysis with ERA5 reanalysis demonstrates that our fused directional spectrum could effectively compensate for high-frequency information loss and spectral distortion in SAR products, meanwhile suppress SWIM's parasitic peaks and resolve directional ambiguity. Qualitatively, compared to ERA5, the Brüning’s correlation coefficient and square error of our fusion results reaches 0.95 and 0.17, significantly outperforming official SAR (0.79 and 0.58) and SWIM (0.83 and 0.52) products. The fusion mitigates SAR’s high-frequency losses and SWIM’s artifacts, enhancing ocean wave spectrum accuracy for improved geophysical applications.

Marine ranching plays a vital role in sustainable fishery resource utilization and marine ecosystem protection. Accurate water quality monitoring through remote sensing is essential in these areas; however, aquaculture facilities such as floating buoys introduce additional reflected signals that can distort remote sensing reflectance (Rrs(λ)), leading to significant errors in water quality parameter retrievals. Despite this challenge, previous studies have neither systematically quantified this interference nor developed effective correction methods. This study proposes a novel correction method to mitigate aquaculture-induced distortions inRrs(λ), enhancing the accuracy of remote sensing-based water quality assessments. Using the mussel aquaculture ranching area off Gouqi Island, China, as a case study, we systematically analyze the spectral influence of aquaculture facilities onRrs(λ) derived from Landsat observations. Comparative spectral analysis between affected and unaffected areas reveals that the high reflectance characteristics of aquaculture facilities cause abnormally elevatedRrs(λ) values, particularly in the shortwave infrared bands. To address this issue, we introduce an aquaculture facility influence factor and develop a pixel-based dynamic correction approach that adjusts for varying degrees of aquaculture-induced distortions across different pixels. Validations using field-measuredRrs(λ) demonstrates that the mean absolute percentage errors at wavelengths of 443, 483, 561, 655, 865, and 1609 nm decreased significantly from 17.6%, 19.8%, 14.8%, 26.8%, 50.9%, and 180.6% before correction to 9.5%, 11.0%,9.2%, 10.4%, 7.7%, and 20.4%, respectively. The effectiveness of the correction method is further supported by improvements in the retrieval of water transparency (Zsd). Sensitivity analysis further reveals that uncorrectedZsdretrieval errors increase exponentially with aquaculture facility coverage, exceeding 60% when coverage reaches 10%, underscoring the necessity of correctingRrs(λ) distortions caused by aquaculture facilities. Overall, the proposed correction method provides a robust and adaptable framework for improving satellite-based water quality monitoring in complex aquaculture regions, with potential applicability across diverse marine ranching environments and integration with various satellite sensors.

After over 12 years of development, the HY-2 series microwave scatterometers have been providing stable and reliable Ku-band observation data for operational forecasting departments and marine scientific research departments. The observation data of the satellite constellation comprising the HY-2B/C/D series satellites have played an important role in fields such as numerical forecasting models, typhoon monitoring, and polar sea ice edge identification. In 2019, the HY-2A scatterometer was retired after eight years of operation, having accumulated valuable experience for subsequent payloads and improvements in the processing algorithms of ground systems. Beginning from the HY-2B satellite, the performance of the scatterometer payload has been greatly improved, and the ground data processing system has also been improved accordingly. In addition, compared to the ECMWF Reanalysis v5 (ERA5) wind field, the wind speed accuracy has been increased from 1.35 to 0.9 m/s, and the wind direction accuracy has been increased from 27° to 12°, which is closely related to the improvement of the signal-to-noise ratio (SNR) of the observed data. In this article, the optimization process from experimental satellite payload to operational payload is introduced, then the calculation method of the normalized radar cross section (NRCS) is described in detail, and the consistency of observation data of four payloads is analyzed. The results show that the change in signal bandwidth, increase in signal slice, and improved slice finding and accumulation method have improved the data’s SNR reduced the measurement error, thus fundamentally ensuring the improvement of wind field inversion accuracy.

The China France Oceanography Satellite (CFOSAT) was launched on October 29, 2018, and is equipped with two sensors: a space-borne rotating fan beam scatterometer (CSCAT) and a space-borne surface wave investigation and monitoring (SWIM) radar. The CSCAT is dedicated to monitoring sea surface winds (SSWs), and the SWIM radar is designed to measure surface ocean waves. In this study, CSCAT SSW products during global tropical cyclones (TCs) were validated using wind data from multiple sources, including European Center for Medium-Range Weather Forecasts (ECMWFs) reanalysis (ERA5), cross-calibrated multi-platform (CCMP), soil moisture active passive (SMAP), WindSat, HY-2B, advanced scatterometer (ASCAT), and buoy wind. Wave parameters (significant wave height (SWH), dominant direction, and wavelength) were validated using wave data from ERA5, Jason3, and the HY-2B altimeter. The results show the following: 1) errors of the CFOSAT wind speed (WS) and SWH increase with the enhancement of TC intensity; 2) the WS accuracy of wind vector cells (WVCs) near the nadir is better than that at the nadir and the edge of the swath; 3) the wind direction (WD) correlation between the CSCAT and ERA5, ASCAT, and WindSat is more obvious at high WSs (>20 m/s) than at lower WSs (< 6 m/s); 4) the accuracy of the SWIM off-nadir SWH product is lower than that of the nadir SWH; and 5) a triple collocation comparison among the CFOSAT SSW, ERA5 SSW, and WindSat SSW indicates that the CFOSAT SSW provides the best performance.

Polar sea ice is an important and sensitive indicator of climate change. The scatterometer onboard the China-France Oceanography Satellite (CFOSAT) is a Ku-band rotating fan-beam scatterometer with dual polarization capability and dedicated to the measurement of sea surface wind vectors. Because of the wide swath, it is also a good candidate for sea ice detection in the polar regions. The paper explorers a Bayesian sea ice detection method for the CFOSAT scatterometer. The performance of the approach is analyzed and validated against coincident sea ice concentration product of Special Sensor Microwave/Imager (SSMI). The results shows that the sea ice detection approach is feasible and effective. The resulting agreement between the sea ice maps of the CFOSAT scatterometer and sea ice concentration of SSMI is high.

China has launched five satellites scatterometers, namely, the Haiyang-2A scatterometer (HSCAT-A), Haiyang-2B scatterometer (HSCAT-B), China-French Satellite scatterometer (CSCAT), Haiyang-2C scatterometer (HSCAT-C) and Haiyang-2D scatterometer (HSCAT-D). This article aims to introduce calibration and validation methods for products of these Chinese scatterometers. Active transponders are used for post-launch absolute calibration, while the Numerical Weather Prediction Ocean Calibration (NOC) method is applied for post-launch relative calibration for the sigma0 observed in these Chinese scatterometers. As a complement to NOC, a natural terrestrial target of the Amazon rainforest is chosen as a calibration site. A long time-series analysis of sigma0 over the Amazon rainforest is also used to assess the temporal stability. To validate the winds from these Chinese scatterometers, the HSCAT-A/B/C and CSCAT winds are compared to in-situ buoy, ECMWF and ASCAT winds, respectively. The comparison results show that all these four Chinese scatterometers winds are comparable and meet the mission requirements, i.e., the error is less than 2 m/s or 10% in terms of the speed and 20° in terms of the direction. HSCAT-B and HSCAT-C perform better than HSCAT-A and show similar qualities to that of ASCAT-A.

Rain is known to be the most significant phenomenon in degrading the Ku-band scatterometer wind quality. After the decommission of the National Aeronautics and Space Administration scatterometer (NSCAT), little work has been done in characterizing the impact of rain on Ku-band fan beam scatterometer. In this paper, the rain impact on the backscatter measurements as well as the retrieved wind quality of the China-France Oceanography Satellite (CFOSAT) scatterometer (CSCAT) is investigated using the European Centre for Medium-range Weather Forecasts (ECMWF) winds and the Global Precipitation Measurement (GPM) mission's Microwave Imager (GMI) rain data as reference. The dependence of rain effects on the observing incidence angle is studied with the objective to optimize the configurations of wind inversion and quality control (QC). It is shown that the backscatter measurements at low incidence angles (~30°) are much less affected by rain than those at higher incidence angles. The operational CSCAT processing proves to be effective in screening rain-contaminated wind vectors but at the expense of many valuable winds. An adapted wind inversion scheme is proposed to further improve the CSCAT wind quality under rainy conditions.

The first rotating fan beam scatterometer onboard China-France Oceanography Satellite (CFOSAT) was successfully launched on October 29, 2018. CFOSAT SCATterometer (CSCAT) is dedicated to the monitoring of sea surface wind vectors but also provides valuable data for the applications over land and Polar Regions. This article provides an overview of the relevant procedures of CSCAT data processing, including onboard signal processing and operational ground processing. Then a post-launch analysis is carried out to evaluate the first results of CSCAT L1 and L2 products. It shows that the CSCAT instrument is generally stable in terms of noise measurements and internal calibration, unless there is any important change in the system configuration. Specifically, the CSCAT backscatter (σθ) precision and wind quality are studied using a set of collocated ancillary data. The σθprecision degrades as wind speed decreases, and it is relatively low at high incidence angles (e.g., θ >46°). In particular, backscatter estimation of the horizontally polarized beam should be further improved by correcting the noise subtraction factor. The retrieved CSCAT winds are in good agreement with the European Centre for Medium Range Weather Forecasts (ECMWF) winds, the Advanced Scatterometer (ASCAT) winds, as well as the buoy winds. However, due to unresolved calibration and interbeam consistency problems, the wind quality degrades remarkably for the out-swath and the nadir-region wind vector cells, implying that the σθcalibration should be improved in the future updates.

After the launching of CFOSAT (China-France Oceanography Satellite), calibrations of the SCAT (scatterometer onboard the CFOSAT) are carried out to modify the bias of the observed NRCS (normalized radar cross section). In this paper, some preliminary calibration results of CFOSAT SCAT are presented, including both internal calibration onboard the CFOSAT and external calibration on the ground. First, the onboard calibration signals and noise signals are analyzed to ensure the SCAT works in a steady status. Then, calibrations on NRCS are carried out by using the SCAT data from Amazon rainforest area. Furthermore, Calibrations using transponders will be carried out at Inner-Mongolian. Preliminary calibration results show that the NRCS of CFOSAT SCAT are compliable with the NRCS of NSCAT.

The scatterometer (SCAT) onboard the China-France Oceanography Satellite (CFOSAT) is the first rotating fan-beam scatterometer (RFSCAT) in space for global ocean surface vector wind measurement. After the CFOSAT launched in Oct. 29, 2018, the main performances of the CFOSAT scatterometer (CFOSCAT) are being tested in orbit. In this paper, the preliminary data processing results of CFOSCAT are presented, including the calibration signal analyses, system noise analyses, normalized radar cross section (NRCS) processing results and wind retrieval results. The results show that CFOSCAT works properly in orbit, the main system specifications of scatterometer system satisfies the design. Preliminary results also validates the wind retrieval performance.

The sea surface winds from the CFOSAT scatterometer (CFOSCAT) are retrieved using the maximum likelihood estimator, and the inversion residual is used to sort the good-quality winds from the poor-quality ones. A two-dimensional variational analysis ambiguity removal (2DVAR) scheme is then applied over the CFOSCAT swath such that a unique wind field is selected from the available local scatterometer wind vector ambiguities. The preliminary results of CFOSCAT Level 2 (L2) processing show that the retrieved wind speed is overestimated under low-wind conditions (w15 m/s). Moreover, the inversion residual for the sweet swath (where there are more than 10 views) is generally higher than that for the nadir/outer swath. These imply that observations with different geometries (views) at the same WVC are inconsistent with respect to the geophysical model function, and thus a comprehensive calibration is highly demanded. A more detailed assessment of the CFOSCAT wind quality will be carried out after calibration and validation campaign.

The China-France Oceanography Satellite (CFOSAT) to be launched in October 2018 will carry two innovative payloads, i.e., the surface wave investigation and monitoring instrument and the rotating fan-beam scatterometer [CFOSAT scatterometer (CFOSCAT)]. Both instruments, operated in Ku-band microwave frequency, are dedicated to the measurement of sea surface wave spectra and wind vectors, respectively. This paper provides an overview of the system definition and characteristics of the CFOSCAT instrument. A prelaunch analysis is carried out to estimate the scatterometer backscatter and wind quality based on the developed CFOSCAT simulator prototype. The overall simulation includes two parts: first, a forward model is developed to simulate the ocean backscatter signals, accounting for both instrument and geophysical noise. Second, a wind inversion processor is used to retrieve wind vectors from the outputs of the forward model. The benefits and challenges of the novel observing geometries are addressed in terms of the CFOSCAT wind retrieval. The simulations show that the backscatter accuracy and the retrieved wind quality of CFOSCAT are quite promising and meet the CFOSAT mission requirements.

Ocean surface wind field is a very important marine dynamic parameter in marine environment forecasting and climatological studies. Spaceborne scatterometer is one of the most efficient remote sensors than can provide global ocean surface wind measurement. Polarimetric scatterometer (PolScat) simultaneously measures co-polarized and cross-polarized backscattering coefficient and the correlation coefficient of the co- and cross-polarized component of radar echoes which can significantly improve the performance of the sea surface wind field measurements. In this paper, we derive the error model of correlation scattering coefficient from radar echo signals. The effect of antenna polaxis deflection on correlation scattering coefficient error is analyzed. Moreover, an “end-to-end” system simulation model of PolScat is established. A modified Maximum Likelihood Estimation (MLE) is used for ocean surface wind field inversion. Both the co-polarized backscattering coefficient and correlation scattering coefficient are included in the objective function of MLE. The simulation results show that PolScat can effectively reduce the probability of ambiguous solutions. The wind speed and wind direction inversion accuracy of PolScat is better than 1m/s and 15° respectively.

Ocean surface current is a very important parameter of ocean dynamic environment. The observation and prediction of ocean surface current has attracted more and more concern. Doppler Scatterometer (DopScat) is a new type of radar for ocean surface wind and current field remote sensing. The ocean surface current inversion method of DopScat impacts the measurement accuracy directly. In this paper, we establishes the Maximum Likelihood Estimation(MLE) method to retrieve the ocean surface current and wind simultaneously. The retrieval accuracy for different position in cross-track, wind speed, and current speed are analyzed. The retrieval results show that the RMS of inversion current speed and direction can be smaller than 0.18m/s and 25°respectively, for medium wind speed condition. This is submitted for the special session of “New Developments of Chinese Oceanographic and Meteorological Satellites”.

In typhoon conditions, air-sea interactions are fierce, the wind speeds are quite high and usually tangled with complex rains. Under such circumstances, the combined observations of scatterometer and radiometer would be able to provide good information although traditional wind filed retrieving methods for them, i.e. MLE based on GMF (Geographical Modelling Function) for scatterometer and RTM(Radiative Transfer Method) which are originated from low or median wind conditions will not be applicable. In this paper, a neural network was employed to combine the observations of the two sensors onboard HY-2A satellite: radiometer (HRAD) and scatterometer (HSCAT) to achieve wind speed retrieving under typhoon conditions. The network was trained by GRAPS (global/regional assimilation and prediction system) data used as true values of wind speeds in typhoons. The established network was verified by data excluded from training data set. Results show that the wind speeds obtained from the network are better than the products of HY-2A compared to GRAPS data. The research of this paper has provided a clue of retrieving typhoon wind speeds for HY-2A products and for the data processing of coming HY satellite series, though further work on wind direction obtain, data comparison between different kinds of assimilation and perdition systems and the test of this method on other scatterometer and radiometer data are to be done in the near future.

The ocean surface current is a very important parameter of ocean dynamic environment. It is connected to global climate change, marine environment forecasting, marine navigation, engineering security, and so on. The observation and prediction of ocean surface current have attracted more and more concern. Doppler Scatterometer (DopScat) is a new type of radar for ocean surface wind and current field remote sensing. The ocean surface current inversion method of DopScat impacts the measurement accuracy directly. In this paper, we establish the simulation model of a DopScat and provide the radial velocity error model. The numerical ocean surface Doppler spectrum model is also introduced and validated with the empirical geophysical model function in C-band (CDOP). The suitable ocean wave elevation spectrum and directional distribution function are selected. What is more, this paper establishes the maximum likelihood estimation (MLE) method to retrieve the ocean surface current and wind simultaneously. The retrieval accuracy for different positions in cross track, different wind speeds, and different current speeds are analyzed. At last, the global ocean current field is observed by DopScat and the ocean current is retrieved. In our simulation, the orbit parameters and observation geometry of DopScat are the same as that of HY-2A scatterometer. The retrieval results show that global current speed standard deviation can be smaller than 0.18 m/s for five days and$0.5 {^{\circ }} \times 0.5 {^{\circ }}$grid average.

As the launch of radars, such as the Precipitation Radar (PR) on Tropical Rainfall Measuring Mission (TRMM)[1] satellite and the Surface Wave Investigation and Monitoring (SWIM) on China France Oceanography SATellite (CFOSAT)[2], that operate in low incident angles, more and more NRCS data at low incident angle will be obtained. In order to retrieve the sea surface wind speed using low incident angle NRCS, the empirical GMF of NRCS with wind speed is established. The empirical nadir reflection coefficient |R(0)2| are calculated and the empirical relationship between mean square slop s(u) and wind speed is established. The mean square slop s(u) can be retrieved by fitting the NRCS at certain incident angles with the theoretical Gaussian GMF model. Then the wind speeds are calculated using the empirical corresponding relation between mean square slop and wind speed. The retrieved wind speeds are compared with Tao and NDBC buoy. The results show that the standard deviation (STD) and bias of retrieved wind speeds are smaller than 1.7m/s and 0.1m/s respectively.

This paper reviews several wind quality-sensitive parameters derived from HY-2A scatterometer data, such as the wind-inversion residual (or Maximum Likelihood Estimator, MLE) and its spatially averaged value, and the singularity exponent (SE) derived from an image processing technique, called singularity analysis. Their sensitivity to data quality is evaluated using the collocated European Centre for Medium-range Weather Forecasting (ECMWF) model output and satellite radiometer rain data. It shows that SE is the best quality indicator, followed by the spatially averaged MLE and the conventional MLE. A set of MLE and SE thresholds are derived from the sensitivity analysis in order to optimize the quality control (QC) for the HY-2A scatterometer.

The wind fields retrieved by scatterometers from GMF (Geographical Modelling Function) would be invalidated under rainy conditions, due to scattering and attenuation effects of rain drops. The scatterometer (HSCAT) and the scanning microwave radiometer (RM) onboard HY-2A can give observations over sea surface at the same time. In this paper, firstly, data of HSCAT and RM onboard HY-2A observing rainy ocean surface were selected as experiment data, with corresponding buoy wind fields. Secondly, an artificial neural network was established for wind fields retrieval under rain conditions, which was tested by other data sets.

The Rotating fan-beam scatterometer (RFSCAT) onboard the Chinese-French Oceanography Satellite (CFOSAT) is a novel kind of scatterometer whose performance had been preliminarily verified by airborne campaign. In this paper the whole data processing chain that can achieve wind field derivation from original data set transmitted from the scatterometer had been proposed with definition of corresponding data levels that would be applied for RFSCAT in the CFOSAT mission. The pre-processing part in the processing chain was validated for it was derived from the same system verified by the airborne campaign. While for the post-processing prime algorithms, validity was concluded from processing some of the data gained in that experiment as well. Further researches needed had been explored as well.

Airborne experiments were carried out to validate the spaceborne scatterometer. Airborne scatterometer use all the instruments of the spaceborne scatterometer to validate the system design and reliability, except the power amplifier and the antenna. The software of the scatterometer is redesigned due to the observation geometry of airborne platform. The wind retrieval results are compared with the HY-2A scatterometer and the real time wind vector measured on the ship at the center of testing region.

Rotating fan-beam scatterometer (RFSCAT) is a new type of satellite scatterometer that was proposed about one decade ago.However, just as other rotating scatterometers, relatively larger wind retrieval errors occur in the nadir and outer regions than in the middle regions of the swath. In order to address this problem, a modified wind vector retrieval algorithm for RFSCAT is presented in this paper. The new algorithm is featured with adaptively extending the range of wind direction for each wind vector cell position across the whole swath according to the distribution histogram of the retrieved wind direction bias. Simulation experiments demonstrated that the new established algorithm can effectively improve the wind direction retrieval accuracy in the nadir and outer regions of the RFSCAT swath.

An airborne multi-pencil beam rotating scatterometer is introduced in this paper. Multi-pencil beam scatterometer can obtain echoes of sea surface and precipitation simultaneously. When the radar system parameters and retrieving arithmetic are carefully designed, sea surface wind vector, three Domain wind vector above the sea and the vertical structure of the precipitation can be refined from the multi-angle echoes. In this paper, the key technologies, such as precipitation and sea surface decoupling arithmetic and pulse compression with low side lobe arithmetic are applied to obtain the wind vector and precipitation structure.

It is well known, that the backscattering radiometric accuracy of radar scatterometers for ocean surface wind and precipitation measurements is decided by the signal-to-noise ratio and number of independent samples for non-coherent average. For sea wind scatterometers and meteorological radars, pulse compression can be employed to improve the range resolution and increase the number of independent numbers. But for airborne and spaceborne scatterometers, the peak power of the transmitter and the pulse length are usually decided by the platform capability, orbit parameter and observation geometry. As a result, compromise and trade-off between the signal-to-noise ratio (SNR) for measurement of each resolution cell and the total number of independent samples of each pulse are usually required. In this paper, based on the radiometric accuracy model, an optimization scheme for design of the independent samples numbers and the corresponding system bandwidth are presented. Based on the presented optimization scheme, the bandwidth of a Ku-band rotating fan-beam radar scatterometer for sea surface wind measurement is design. Simulation validates the optimization and design results.

The Ku-band scatterometer onboard HY-2 (SCAT/HY-2) is a pencil-beam radar employing pulse compression techniques to ensure number of the independent measurement samples and the resulted precision of the backscattering coefficient for each resolution cell. In this paper, the system specifications of SCAT/HY-2 are introduced. The signal processing scheme, including pulse compression, resolution cell regroup and the area weighted incoherent average of the backscattered power, is presented and analyzed. Based on the analysis of the resolution cell after pulse compression processing, the radiometric accuracy and surface resolution of the SCAT/HY-2 for all the azimuth angles will be simulated. Simulation and analysis results shows that SCAT/HY-2 can satisfy the specification requirements of HY-2 satellite.