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



Zhu S, Shao W, Marino A, Sun J & Yuan X (2020) Semi-Empirical Algorithm for Wind Speed Retrieval from Gaofen-3 Quad-Polarization Strip Mode SAR Data. Journal of Ocean University of China, 19 (1), pp. 23-35.

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-1 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.

wind; Gaofen-3 synthetic aperture radar; cross-polarization

Journal of Ocean University of China: Volume 19, Issue 1

FundersEuropean Space Agency
Publication date29/02/2020
Publication date online30/09/2019
Date accepted by journal01/07/2019

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Dr Armando Marino
Dr Armando Marino

Senior Lecturer in Earth Observation, Biological and Environmental Sciences