Microwave Scatterometer Research Articles

Polar Sea Ice Monitoring Using HY-2B Satellite Scatterometer and Scanning Microwave Radiometer Measurements

The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and passive microwave observations over the polar region. In this paper, a polar ice water discrimination model and Arctic sea-ice-type classification model based on the support vector machine (SVM) method were established and used to produce a daily sea ice extent dataset from 2019 to 2021 with data from SCA and SMR. First, suitable scattering and radiation parameters are chosen as input data for the discriminant model. Then, the sea ice extent was obtained based on the monthly ice water discrimination model, and finally, the ice over the Arctic was classified into multiyear ice (MYI) and first-year ice (FYI). The 3-year ice extent and MYI extent products were consistent with the similar results of the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSISAF). Using the OSISAF similar product as validation data, the overall accuracies (OAs) of ice/water discrimination and FYI/MYI discrimination are 99% and 97%, respectively. Compared with the high spatial resolution classification results of the Moderate Resolution Imaging Spectroradiometer (MODIS) and SAR, the OAs of ice/water discrimination and FYI/MYI discrimination are 96% and 86%, respectively. In conclusion, the SAC and SMR of HY-2B have been verified for monitoring polar sea ice, and the sea ice extent and sea-ice-type products are promising for integration into long-term sea ice records.

Sea Surface Wind Speed Estimation From the Combination of Satellite Scatterometer and Radiometer Parameters

AbstractSatellite research on global sea surface winds is crucial for understanding and monitoring the dynamics of earth's oceans, providing valuable insights into weather patterns, climate changes, and oceanographic processes. This study investigates the fusion of active (microwave scatterometer) and passive (microwave radiometer) satellite data using Machine learning (ML) for sea surface wind speed estimation. Employing Random Forest Regression (RF), Convolutional Neural Network Regression (CNN), and Multiple Linear Regression (MLR). Evaluation against reference data sets (Advanced Scatterometer, ERA5, Cross‐Calibrated Multi‐Platform (CCMP), buoy wind speeds) highlights the robustness of the proposed models. The research findings indicate that both RF and CNN exhibit superior accuracy in the active, passive, and joint active‐passive models compared to the simplistic MLR model. The joint models of the three regression methods outperform the individual active or passive models. The root mean square deviation (RMSD) accuracy of RF and CNN joint models, when compared with ASCAT, is in the order of 0.7 m/s, and when compared with buoys, the RMSD accuracy is around 1.1 m/s. The ML models, especially RF and CNN, demonstrate superior performance, providing accurate and reliable estimations crucial for meteorological and oceanographic applications. These findings underscore the potential operational use of ML techniques in enhancing remote sensing applications.

Assessment of C-Band Polarimetric Radar for the Detection of Diesel Fuel in Newly Formed Sea Ice

There is a heightened risk of an oil spill occurring in the Arctic, as climate change driven sea ice loss permits an increase in Arctic marine transportation. The ability to detect an oil spill and monitor its progression is key to enacting an effective response. Microwave scatterometer systems may be used detect changes in sea ice thermodynamic and physical properties, so we examined the potential of C-band polarimetric radar for detecting diesel fuel beneath a thin sea ice layer. Sea ice physical properties, including thickness, temperature, and salinity, were measured before and after diesel addition beneath the ice. Time-series polarimetric C-band scatterometer measurements monitored the sea ice evolution and diesel migration to the sea ice surface. We characterized the temporal evolution of the diesel-contaminated seawater and sea ice by monitoring the normalized radar cross section (NRCS) and polarimetric parameters (conformity coefficient (μ), copolarization correlation coefficient (ρco)) at 20° and 25° incidence angles. We delineated three stages, with distinct NRCS and polarimetric results, which could be connected to the thermophysical state and the presence of diesel on the surface. Stage 1 described the initial formation of sea ice, while in Stage 2, we injected 20L of diesel beneath the sea ice. No immediate response was noted in the radar measurements. With the emergence of diesel on the sea ice surface, denoted by Stage 3, the NRCS dropped substantially. The largest response was for VV and HH polarizations at 20° incidence angle. Physical sampling indicated that diesel emerged to the surface of the sea ice and trended towards the tub edge and the polarimetric scatterometer was sensitive to these physical changes. This study contributes to a greater understanding of how C-band frequencies can be used to monitor oil products in the Arctic and act as a baseline for the interpretation of satellite data. Additionally, these findings will assist in the development of standards for oil and diesel fuel detection in the Canadian Arctic in association with the Canadian Standards Association Group.

Research on Sea Surface Wind Speed FM Based on CYGNSS and HY-2B Microwave Scatterometer

Research on Sea Surface Wind Speed FM Based on CYGNSS and HY-2B Microwave Scatterometer

The improvement of HY-2 Satellite’s Microwave Scatterometer instrument and NRCS calculation

The improvement of HY-2 Satellite’s Microwave Scatterometer instrument and NRCS calculation

Evaluation of HY-2C and CFOSAT Satellite Retrieval Offshore Wind Energy Using Weather Research and Forecasting (WRF) Simulations

This study simulated the spatial distribution of wind speeds and wind energy density by using the WRF model, and we used the WRF-simulated results to evaluate the sea surface wind speeds retrieved from the HY-2C and CFOSAT satellite-borne microwave scatterometers over the Yellow Sea region. The main conclusions were as follows: (1) The combination of the MRF boundary layer parameterization scheme, the MM5 near-surface parameterization scheme, and the Global Data Assimilation System (GDAS) initial field demonstrated the best performance in simulating the 10 m wind speed in the Yellow Sea region, with a root-mean-square error (RMSE) of 1.57, bias of 1.24 m/s, and mean absolute percentage error (MAPE) of 17%. (2) The MAPE of the HY-2C inversion data was 9%, while the CFOSAT inversion data had an MAPE of 6%. The sea surface wind speeds derived from the HY-2C and CFOSAT satellite scatterometer inversions demonstrated high accuracy and applicability in this region. (3) The wind speed was found to increase with altitude over the Yellow Sea, with higher wind speeds observed in the southern region compared to the northern region. The wind power density increased with altitude, and the wind power density in the southern area of the Yellow Sea was higher than in the northern region. (4) The CFOSAT satellite inversion products were in good agreement with the WRF simulation results under low wind speed conditions. In contrast, the HY-2C satellite inversion products showed better agreement under moderate wind speed conditions. Under high wind speed conditions, both satellite inversion products exhibited minor deviations, but the HY-2C product had an overall overestimation, while the CFOSAT product remained within the range of −1 to 1 m/s. (6) The wind power density increased with the satellite-inverted 10 m wind speed. When the 10 m wind speed was less than 9 m/s, the wind power density exhibited a roughly cubic trend of increase. However, when the 10 m wind speed exceeded 9 m/s, the wind power density no longer increased with the rise in 10 m wind speed. These findings provide valuable insights into wind energy resources in the Yellow Sea region and demonstrate the effectiveness of satellite scatterometer inversions for wind speed estimation. The results have implications for renewable energy planning and management in the area.

Analysis of Doppler spectrum of a spaceborne Doppler scatterometer using an echoed signal simulation model

ABSTRACT The Doppler spectrum of a microwave scatterometer signal is determined by the relative motion between the scatterometer and the ocean surface. It is also determined by the modulation from the ocean surface backscattering coefficient. The relative motion includes contributions mainly from the motion of the platform, the phase velocity of the resonant Bragg waves, the orbital motions of ocean waves, and the ocean surface current. Estimating the Doppler centroid from the complex Doppler spectrum of the received signal is an important issue for the application of a spaceborne Doppler scatterometer in ocean surface current retrieval. In this study, we use an echoed signal simulation model based on a two-dimensional rough dynamic ocean surface to estimate the Doppler spectrum of a spaceborne Doppler scatterometer. We analyze the Doppler spectrum with different observation geometry and ocean state, and the effects of modulation on the Doppler spectrum with the pulse-pair method. Our results show that the high platform velocity and the modulation of the backscattering coefficient from the ocean surface are the main influencing factors that affect the shape of the Doppler spectrum of the received signal, which affects the accuracy of estimation of the Doppler centroid. For a Ka-band pencil-beam rotating Doppler scatterometer using the pulse-pair method, the modulation can lead to a measurement error of 1.2 m·s−1.

Observing Tropical Cyclone Morphology Using RADARSAT-2 and Sentinel-1 Synthetic Aperture Radar Images

Abstract Since the 1960s, meteorological satellites have been able to monitor tropical cyclones and typhoons. Their images have been acquired by passive remote sensing instruments that operate in the visible and infrared bands, where they only display the cloud-top structure of tropical cyclones and make it a challenge to study the air–sea interaction near the sea surface. On the other hand, active remote sensors, such as spaceborne microwave scatterometers and synthetic aperture radars (SARs), can “see” through clouds and facilitate observations of the air–sea interaction processes. However, SAR acquires images and provides the wind field at a much higher resolution, where the eye of a tropical cyclone at surface level can be identified. The backscattered signals received by the SAR can be processed into a high-resolution image and calibrated to represent the normalized radar cross section (NRCS) of the sea surface. In this study, 33 RADARSAT-2 and 102 Sentinel-1 SAR images of Atlantic and Indian Ocean tropical cyclones and Pacific typhoons from 2016 to 2021, which display eye structure, have been statistically analyzed with ancillary tropical cyclone intensity information. To measure the size of the eye, a 34-kt (∼17 m s−1) contour is defined around it and the amount and size of pixels within the eye is utilized to provide its area in square kilometers. Additionally, an azimuthal wavenumber for each shape of the eye was assigned. Results showed that eye areas increase with decreasing wind speed and increasing wavenumber and demonstrate that SAR-derived data are useful for studying tropical cyclones at the air–sea interface and provide results of these behaviors closely to data derived from best track archives.

Development of a geophysical model function for HY-2 satellite microwave scatterometer wind retrievals

Development of a geophysical model function for HY-2 satellite microwave scatterometer wind retrievals

Blending Sea Surface Winds from the HY-2 Satellite Scatterometers Based on a 2D-Var Method

The launch of the Haiyang-2 (HY-2) satellite constellation fosters the quick acquisition of global sea surface vector winds from the perspective of remote sensing. This study intends to develop a six-hourly mesoscale analysis of sea surface winds based on the microwave scatterometers onboard the HY-2 satellite series, with the objective of meeting the considerable demand for accurate and gap-free ocean wind forcing products. First, the accuracy of HY-2 scatterometers (HSCATs) in measuring wind is evaluated. In particular, the standard deviation (SD) errors of HSCATs data are assessed using the collocated buoy measurements with different temporal windows in order to account for the temporal representativeness errors in the blending analysis. Afterwards, a two-dimensional variational (2D-Var) method is implemented to blend the HSCATs measured winds and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis winds over the global ocean surface. This approach is different from existing methods in that it takes both wind error and background error correlation into account. The results show that the blended wind product is of a promising quality compared with independent wind references. Interestingly, the blended winds derived from 2D-Var in combination with an empirical background error correlation show smaller SD errors than those using a Gaussian error correlation function. Overall, the blended wind product should be valuable for forcing global ocean models or describing air-sea interaction processes on a scale close to the scatterometer measurements.

Year-round sea ice and snow characterization from combined passive and active microwave observations and radiative transfer modeling

Satellite microwave observations from 1.4 to 36 GHz already showed sensitivity to several geophysical parameters of sea ice such as Sea Ice Concentration (SIC), Sea Ice Thickness (SIT) or snow depth. The main goal of this article is to provide a realistic and comprehensive characterization of the sea ice and its snow cover that explains the microwave observations during a whole year using a radiative transfer model. For this purpose, we construct a unique dataset of passive microwave observations, to mimic the future Copernicus Imaging Microwave Radiometer (CIMR), along with the active microwave scatterometer data (ASCAT). CIMR database is used to classify sea ice microwave signatures in their spectral dimension with a machine learning technique while ASCAT data are used to help interpret the results of the classification. Classification results are then interpreted with a state-of-art sea ice and Snow Microwave Radiative Transfer model (SMRT) for all highlighted signatures and all seasons. Results make it possible to identify the specific behaviors from the observation co-variabilities for SIC, SIT, and snow structure. Our analysis underlined the role of the depth hoar over multi-year ice, for the interpretation of scattering signals in winter. Scattering signals that appear in late summer are explained by the presence of superimposed ice. This characterization will benefit from future advances in SMRT development, as well as the improved observations of future satellite missions.

Intercomparison of Arctic Sea Ice Backscatter and Ice Type Classification Using Ku-Band and C-Band Scatterometers

As a result of global warming, multiyear ice (MYI) is being replaced by first-year ice (FYI) in the Arctic. Microwave scatterometers in the Ku-band and C-band can provide daily observations of sea ice type. However, their comparative capabilities in mapping ice type have not been thoroughly evaluated. We present a systematic intercomparison of the backscatter signature in VV polarization ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$ </tex-math></inline-formula> ) and the sea ice classification from three scatterometer systems using the same ice classification approach. The systems are the Ku-band quick scatterometer (QSCAT) and the newly launched Chinese rotating fan-beam scatterometer (RFSCAT) and the C-band advanced scatterometer (ASCAT). Three freezing seasons are used, i.e., 2007/08 and 2008/09 for the QSCAT/ASCAT comparison and 2019/20 for the RFSCAT/ASCAT comparison. With reference to ASCAT, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$ </tex-math></inline-formula> bias between QSCAT and RFSCAT results from their different incidence angles. A continuous declining trend of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$ </tex-math></inline-formula> from MYI and FYI is observed during winter, with a greater difference between MYI and FYI in the Ku-band. The MYI and FYI extent derived from QSCAT/RFSCAT is highly consistent with that derived from ASCAT, with a difference less than 7% and 3% for MYI and FYI, respectively. The overall accuracy (OA) is around 77% and 80% for the RFSCAT results and ASCAT results, respectively, compared with Sentinel-1 SAR images. The classification results show high consistency (81%–89%) with ice charts from the Canadian Ice Service. The incorporation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {Tb}}_{36\mathrm {h}}$ </tex-math></inline-formula> from AMSR-E/AMSR2 improves the OA of the classification when using ASCAT or RFSCAT by 7%–11%.

Standard Deviation of Spaceborne GNSS-R Ocean Scatterometry Measurements

This article analyzes the contribution of the delay-Doppler map (DDM) observation noise to the uncertainty of global navigation satellite system reflectometry (GNSS-R) ocean scatterometry observables and the retrieved wind speeds. For this purpose, the parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K^{p}$ </tex-math></inline-formula> , which is commonly used in the traditional microwave scatterometer, is introduced to characterize the relative standard deviation (RSD) of the GNSS-R normalized bistatic radar cross section (NBRCS) measurement. Based on the noise covariance of the DDM measurements, the analytic expressions of RSD are derived for two cases, i.e., the NBRCS computed with one single DDM bin at the specular point and the NBRCS computed from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M\,\,\times \,\,N$ </tex-math></inline-formula> DDM bins around the specular point. By analyzing the dependence of the RSD on different system, geometry, and instrument parameters, the simplified models of RSD are derived empirically. As a simple application of the proposed models, the wind speed retrieval errors are computed using parameters from the Cyclone GNSS (CYGNSS) mission. It shows that the wind speed retrieval error due to thermal noise and speckle can be an important error source for the overall wind speed retrieval performance, especially at high wind speed and with the high incidence angle GNSS-R measurements.

Radar sensing of the sea surface using small spacecraft

The possibility of using a constellation of small spacecraft as receiving satellites, when “highlighting” the sea surface from existing (navigational, communication) or specially created spacecraft—to form a wide-area (about 1 000 km) radar survey zone at a given resolution (about 10 m)—is under consideration. Such a constellation could provide operational monitoring of fast-moving atmospheric cyclones, measuring directly the parameters of storm waves (altitude and orbital velocity)—which would replace the existing constellation of microwave scatterometers, providing operational monitoring of the World Ocean surface in the 3 H (H—altitude of the satellite’s orbit) field of view with a resolution of about 10 km—but with calibration of the received images by wind speed and direction, which leads to huge errors when trying to introduce altitude calibration in the Small spacecraft have many advantages over large satellites. For example, they are relatively inexpensive to build, take minimal time from design to launch, are easily modified to solve a specific problem, and create less radio interference. The approach under consideration consists in redistribution of tasks to be solved between the constellation of satellites in orbit. High orbiting navigation satellites, for example, can be used as transmitter carriers (of the illumination of the surface) that use the necessary broadband signal with acceptable periodicity. Receivers of reflected signals are placed on board small spacecraft, and at formation of wide-band radar image of sea surface with necessary resolution ∼10 m (that only on order exceeds acceptable on small spacecraft size of receiving antennas)—in flight direction is necessary to use synthesized aperture of receiving antenna. This work has the character of “staged” research.

Efficient Correction Method for Polarimetric Distortion Error of Polarimetric Scatterometer

The full-polarization scatterometer is a new type of microwave scatterometer that can simultaneously measure the co-polarization and cross-polarization backscatter of the target. It can solve the wind direction ambiguity problem of the traditional scatterometer in the sea surface wind retrieval and improve the wind measurement accuracy. Moreover, the cross-polarization backscatter can effectively increase the sea surface wind speed measurement range and can be used for typhoon monitoring. High-precision sea surface wind retrieval using full-polarization scatterometer data first needs to correct the polarization distortion error of the full-polarization scatterometer system. To solve this problem, the polarimetric scattering coefficient model influenced by polarimetric distortion error is built based on the signal model of polarimetric scatterometer, and then the influence of polarimetric distortion error in quantitatively analyzed. Based on the polarimetric scattering coefficient model, some assumptions of ocean scattering characteristic and polarimetric calibration are introduced to establish the correction model of polarimetric scattering coefficients. Simulation data based on RADARSAT-2 polarimetric scattering coefficients of different sea region is corrected and results show that the proposed method can effectively correct of polarimetric distortion error and greatly improve the measurement accuracy of polarimetric scattering coefficients.

Examination of the Daily Cycle Wind Vector Modes of Variability from the Constellation of Microwave Scatterometers and Radiometers

Offshore of many coastal regions, the ocean surface wind varies in speed and direction throughout the day, owing to forcing from land/sea temperature differences and orographic effects. Far offshore, both diurnal and semidiurnal wind vector variability has been noted in the Tropical Atmosphere Ocean-TRIangle Trans-Ocean buoy Network (TAO-TRITON) mooring data in the tropical Pacific Ocean. In this manuscript, the tropical diurnal wind variability is examined with microwave radiometer-derived winds from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), merged with RapidScat and other scatterometer data. Since the relationship between wind speed and its zonal and meridional components is nonlinear, this manuscript describes an observationally based methodology to merge the radiometer and scatterometer-based wind estimates as a function of observation time, to generate a multi-year dataset of diurnal wind variability. Compared to TAO-TRITON mooring array data, the merged satellite-derived wind components fairly well replicate the semidiurnal zonal wind variability over the tropical Pacific but generally show more variability in the meridional wind components. The meridional component agrees with the associated mooring location data in some locations better than others, or it shows no clear dominant diurnal or semidiurnal mode. Similar discrepancies are noted between two forecast model reanalysis products. It is hypothesized that the discrepancies amongst the meridional winds are due to interactions between surface convergence and convective precipitation over tropical ocean basins.

An Improved Method of Noise Subtraction for the CFOSAT Scatterometer

The microwave scatterometer onboard the China-France Oceanography Satellite (CFOSAT Scatterometer, CSCAT) is the first rotating fan-beam scatterometer (RFSCAT) in operation. It is used to obtain the ocean surface wind field by measuring the backscattering coefficient over a large observation swath. To estimate the backscattering coefficient, the scatterometer needs to measure the scattered signal accurately. The process of removing noise from the echo signal (scattered signal plus noise) is called noise subtraction. Noise subtraction has a great influence on the measurement of the backscattering coefficient and subsequent wind field retrieval. Therefore, accurate noise subtraction is necessary and important. In this article, an improved noise subtraction method for CSCAT is proposed. According to the distribution characteristics of backscattering coefficients, noise correction factors are introduced to improve the noise subtraction of CSCAT. The ocean surface wind fields are obtained using the improved backscattering coefficient data, and the deviation of wind speed and direction are calculated by comparison with the European Centre for Medium Range Weather Forecasts wind fields. The results show that the improved noise subtraction method can lead to improvement of the quality of wind field effectively. In addition, based on the improved method of noise subtraction, a method of estimating the noise correction factor is proposed at a large incidence angle, furtherly improving the noise subtraction method. The results show that the proposed method is effective.

Experimental study of C-band microwave scattering characteristics during the emulsification process of oil spills

In this study, oil spill experiments were performed in a water tank to determine changes in the surface scattering characteristics during the emulsification of oil spills. A C-band fully-polarimetric microwave scatterometer and a vector network analyzer were used to observe films of the following oils: crude oil with an asphalt content below 3% that is prone to emulsification (type A), fresh crude oil extracted from an oilfield (type B), and industrial crude oil that was dehydrated and purified (type C). The difference in the backscatter results between the emulsified oil film and the calm water surface under C-band microwaves and the influence of the emulsification of the oil film on the backscatter were analyzed in detail. The results demonstrate that under a low-wind and no-waves condition (the maximum wave height was below than 3 mm), the emulsification of crude oil could modulated the backscatter through changes in the surface roughness and the dielectric constant, where the surface roughness had the dominant effect. The surface backscatters of the type B oil were greater than that of the type C oil in both the emulsified and non-emulsified states. In the non-emulsified state, the average differences in the backscatter between the type B and C oils were 2.19 dB, 2.63 dB, and 2.21 dB for the polarization modes of VV, HH, and HV/VH, respectively. Smaller corresponding average differences of 0.98 dB, 1.49 dB, and 1.5 dB were found for the emulsified state with a 20% moisture constant for the oil film. The results demonstrated that the surface roughness of the different oil films could vary due to the differences in the oil compositions and the oil film properties, which in turn affect the backscatter of the oil film surface.

Retrieval of Sea Surface Wind Fields Using Multi-Source Remote Sensing Data

Timely and accurate sea surface wind field (SSWF) information plays an important role in marine environmental monitoring, weather forecasting, and other atmospheric science studies. In this study, a piecewise linear model is proposed to retrieve SSWF information based on the combination of two different satellite sensors (a microwave scatterometer and an infrared scanning radiometer). First, the time series wind speed dataset, extracted from the HY-2A satellite, and the brightness temperature dataset, extracted from the FY-2E satellite, were matched. The piecewise linear regression model with the highest R2 was then selected as the best model to retrieve SSWF information. Finally, experiments were conducted with the Usagi, Fitow, and Nari typhoons in 2013 to evaluate accuracy. The results show that: (1) the piecewise linear model is successfully established for all typhoons with high R2 (greater than 0.61); (2) for all three cases, the root mean square error () and mean bias error (MBE) are smaller than 2.2 m/s and 1.82 m/s, which indicates that it is suitable and reliable for SSWF information retrieval; and (3) it solves the problem of the low temporal resolution of HY-2A data (12 h), and inherits the high temporal resolution of the FY-2E data (0.5 h). It can provide reliable and high temporal SSWF products.

Validation of different global data sets for sea surface wind-stress

ABSTRACT Different surface wind-stress products over the global ocean were validated by comparing with in-situ measurements from moored buoys, and by inter-comparing among them. The products are ones constructed from satellite observations by microwave scatterometers and radiometers, and reanalysed ones by data assimilation and numerical models. Comparisons with buoy measurements revealed that the Cross-Calibrated Multi-Platform Version 2 (CCMP v2.0) has the lowest positive bias, the smallest root mean square error (RMSE) and the largest correlation coefficient in this inter-comparison, meaning that the CCMP v2.0 gives the best performance. Inter-comparisons among the different wind-stress products exhibited that the meridional wind-stress data of the National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) are not different in the subtropical region from those of other ones, and zonal and meridional wind-stress data of the National Centre for Environmental Prediction/Climate Forecast System Reanalysis (NCEP/CFSR) have relatively low values in the westerly region. In the zonal-spectral analysis, we found that the zonal wind-stress/wind-vector data from the Institut Français de Recherche pour la Recherche et l’Exploitation de la Mer (IFREMER) have short wave-like oscillations. Thus, the surface wind and wind-stress data in IFREMER products have been well affected by spatial resolution of background data used in their gridding procedure. We also investigated the spatial characteristics of wind-stress curl fields for the different data sets, and found that there are differences between the CCMP v2.0 and Japanese Ocean Flux Data sets with Use of Remote Sensing Observations Version 3 (J-OFURO3) which are characterized by spike-like features in the equatorial Pacific where there are the Tropical Atmosphere Ocean and the TRIangle Trans-Ocean buoy Network (TAO/TRITON) buoys. These features were spatially correlated with buoy locations, which suggests that the reliability of the CCMP v2.0 products is governed by buoy locations in this region.