Sea Surface Wind Retrieval from Synthetic Aperture Radar Data by Deep Convolutional Neural Networks

The sea surface wind at low and moderate speed ( 20 m/s). In this study, we explore to use a novel deep convolutional neural networks (DCNN) architecture, U-Net, to retrieve the sea surface wind at high speed. The Sentinel-1B SAR cross-polarized (HV and VH) Normalized Radar Cross Section (NRCS), SAR incidence angle and the ancillary Global Forecast System (GFS) model wind directions are used as the U-Net input data. The GFS model wind speed is the ground-truth data. The results suggest that the U-Net wind retrieval model has a capability to retrieve the high wind speed from SAR data with the physical model-based data. The accuracy of physical model data directly affects the U-Net model results. Meanwhile, U-Net model has a better continuity in the joint area of two strips.