Space‐borne synthetic aperture radar (SAR) measurements have already been proven to be invaluable in detecting and quantifying properties of ocean swell systems. With the improved spatial resolution, Sentinel‐1 wave mode measurements can further be extended toward shorter‐scale waves, that is, within the surface wave equilibrium range. In this study, a new parameter is derived from filtering SAR image cross‐spectra around range‐traveling intermediate wind waves (wavelengths 15–20 m). This parameter captures both the radar cross‐section variability and its time evolution. Given the dependence of intermediate waves on local wind field, this parameter is statistically documented to confirm its sensitivity to both wind speed and wind direction. Comparable to Doppler estimate, the signed parameter can be used to reduce the wind direction ambiguity in the inversion of high‐resolution wind fields from SAR imagery. In addition, under complex environmental situations over a polar low event, this parameter is expected to better capture the dynamics of the surface wind. Globally, our analysis demonstrates regional and seasonal variations of this parameter, associated with those of wind/wave patterns. In particular, its directionality reveals the seasonal migration of intertropical convergence zone. This parameter is valuable to various further applications, for example, to help develop wind retrieval scheme from SAR measurements or to map space‐time variability of ocean waves at various wavelengths.

Accurate absolute radiometric calibration of spaceborne synthetic aperture radar (SAR) sensor is of great importance in quantitative oceanic monitoring. Traditionally, the calibration constant is determined by analyzing measurements of man-made calibrators deployed on land at a high expense. In this paper, a technique called numerical weather prediction (NWP)-based ocean calibration method for estimating the calibration constant on the basis of SAR observations over ocean is applied to the first Chinese C-band SAR satellite Gaofen-3 (GF-3). The ocean calibration is performed on GF-3 wave mode SAR images at VV and HH polarizations over a period of one year from September 2017 to August 2018. Verification against the independent scatterometer-derived winds shows that after NWP-based ocean recalibration, the accuracy of wind speed retrieval from GF-3 wave mode imagery could reach the wind speed estimation performance using the state-of-the-art of SAR data. These results indicate that GF-3 wind retrievals are promising for operational oceanic products and applications if the SAR data are appropriately calibrated by the proposed NWP-based ocean calibration approach.

Spaceborne synthetic aperture radar (SAR) has been proven to be a useful technique for observing the sea surface wind and current over the open ocean given its all-weather data-gathering capability and high spatial resolution. In addition to the commonly used radar return magnitude quantified by normalized radar cross section (NRCS), the Doppler centroid anomaly (DCA) has added another dimension of information. In this study, we combine the NRCS and DCA for a joint inversion of wind and surface current information using a Bayesian method. SAR-estimated Doppler is corrected by a series of steps, including the removal of scalloping effect and land correction. The cost function of this inversion scheme is constructed based on NRCS, DCA, and a background model wind. The retrieved wind results show the quality of performance through comparison with the in situ buoy measurements, showing a mean bias and a root-mean-square error (RMSE) of 0.33 m/s and 1.45 m/s for wind speed and 6.94° and 35.74° for wind direction, respectively. The correlation coefficients for wind speed and direction reach 0.931 and 0.661, respectively. Based on the obtained wind field, the line-of-sight velocity of the sea surface current is then derived by removing the wind contribution using the empirical model. The results show a consistent spatial pattern relative to the high-frequency radars, with the comparison relative to the drifter-measured current velocity exhibiting a mean bias of 0.02 m/s and RMSE of 0.32 m/s, demonstrating the reliability of the proposed inversion scheme. Such results will serve as a prototype for future spaceborne sensors to combine the radar return and Doppler information for the joint retrieval of wind vector and surface current velocity. This technique could be readily extended to the radar configuration of rotating beams for monitoring winds and current vectors.