Intelligent Wind Retrieval from Chinese Gaofen-3 SAR Imagery in Quad Polarization

This study proposes the use of the artificial neural network for wind retrieval with Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) data. More than 10 000 images acquired in wave mode and quad-polarization strip map were collected over global seas throughout the 2-yr mission. The GF-3 operated in a quad-polarization channel-vertical-vertical (VV), vertical-horizontal (VH), horizontal-horizontal (HH), and horizontal-vertical (HV). These images were collocated with winds from the European Centre for Medium-Range Weather Forecasts at a 0.125 degrees grid. The newly released wind retrieval algorithm for copolarization (VV and HH) SAR included CMOD7 and C-SARMOD2. We developed an algorithm based on an artificial neural network method using the SAR-measured normalized radar cross section at quad-polarization channels, herein named QPWIND_GF. Simulations using the QPWIND_GF showed that the correlation coefficient of wind speed was 0.94. We then validated the retrieval wind speeds against the measurements at a 0.25 degrees grid from the Advanced Scatterometer. A comparison showed that the root-mean-square error (RMSE) of wind speed was 0.74 m s(-1), which was better than the wind speed obtained using state-of-the-art methods-including, for example, CMOD7 (RMSE 0.88 m s(-1)) and C-SARMOD2 (RMSE 1.98 m s(-1)). The finding indicated that the accuracy of wind retrieval from GF-3 SAR images was significantly improved. Our work demonstrates the advanced feasibility of an artificial neural network method for SAR marine applications.