Semi-Empirical Algorithm for Wind Speed Retrieval from Gaofen-3 Quad-Polarization Strip Mode SAR Data

Abstract

Synthetic aperture radar (SAR) is a suitable tool to obtain reliable wind retrievals with high spatial resolution. The geophysical model function (GMF), which is widely employed for wind speed retrieval from SAR data, describes the relationship between the SAR normalized radar cross-section (NRCS) at the copolarization channel (verticalvertical and horizontalhorizontal) and a wind vector. SAR-measured NRCS at cross-polarization channels (horizontalvertical and verticalhorizontal) correlates with wind speed. In this study, a semi-empirical algorithm is presented to retrieve wind speed from the noisy Chinese Gaofen-3 (GF-3) SAR data with noise-equivalent sigma zero correction using an empirical function. GF-3 SAR can acquire data in a quad-polarization strip mode, which includes cross-polarization channels. The semi-empirical algorithm is tuned using acquisitions collocated with winds from the European Center for Medium-Range Weather Forecasts. In particular, the proposed algorithm includes the dependences of wind speed and incidence angle on cross-polarized NRCS. The accuracy of SAR-derived wind speed is around 2.10 m/s root mean square error, which is validated against measurements from the Advanced Scatterometer onboard the Metop-A/B and the buoys from the National Data Buoy Center of the National Oceanic and Atmospheric Administration. The results obtained by the proposed algorithm considering the incidence angle in a GMF are relatively more accurate than those achieved by other algorithms. This work provides an alternative method to generate operational wind products for GF-3 SAR without relying on ancillary data for wind direction.

A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

“…The GF-3, which operates in 12 imaging modes [19], has released the data since Au gust 2016. It is a part of the Dragon Programme, a collaboration project between the large scale scientific and technological cooperation project between the Chinese Ministry of Sci ence and Technology and the European Space Agency in Earth observation.…”

“…Other studies have been conducted for wind retrieval using the SAR-derived azimuthal cut-off wavelength [15,16] and theoretical backscattering model [17]. However, due to the saturation of the co-polarized SAR backscattering signal at the regular sea state [18] and at a strong wind speed of >25 m/s (i.e., cyclonic wind profile), GMF usually inverts cross-polarized vertical-horizontal and horizontal-vertical images [19,20] using a machine learning method [21]. Several algorithms have been developed for co-polarized SAR wave retrieval based on a sea surface mapping mechanism, following the introduction of the Max-Planck Institute Algorithm (MPI) [22] and several co-polarized SAR wave retrieval algorithms based on a sea surface mapping mechanism.…”

A Review for Retrieving Wind Fields by Spaceborne Synthetic Aperture Radar

“…Using the observation data for the RADARSAT-2 satellite and a hurricane, an improved C-3PO model function was proposed. Zhu et al [98] proposed a semiempirical algorithm that considered the influence of the WS and the incident angle on a cross-polarized NRCS. The introduction of cross-polarized SAR observation significantly improved the accuracy of the C-band SAR inversion of an SSWF for a high WS [99][100][101][102][103][104][105].…”

Machine Learning-Based Wind Direction Retrieval From Quad-Polarized Gaofen-3 SAR Images

“…In particular, C-band GF-3 has up to twelve imaging modes that allow observations of various sea surface dynamics, e.g., wind, waves, eddies, internal waves, up-welling, and TCs. Furthermore, the wave (WAV) and quad-polarization strip (QPS) modes are popular for wind and wave monitoring in large-scale seas and coastal regions, respectively, and they have four polarization channels [4], including vertical-vertical (VV), vertical-horizontal (VH), HH, and horizontal-vertical (HV).…”