ResNet Deep Learning‐Based Inversion Method for Sea Surface Wind Field

Abstract

This paper presents a method to invert the sea surface wind field from Sentinel‐1 synthetic aperture radar (SAR) interferometric wide swath (IW) band mode ground range detected (GRD) vertical polarization (VV) data based on the deep residual network (ResNet). Compared with the traditional algorithm (i.e., using geophysical model functions (GMFs)), the wind field inversion based on the ResNet deep learning method solves the problem of separate wind direction and wind speed inversion. The ResNet model is developed based on the results of 212 VV polarized IW images and cross calibrated multiplatform (CCMP) data collected in 2021 in the offshore waters of China. The root mean square error, standard deviation, and bias of the inverse wind direction of the ResNet model are 12.5°, 9.8°, and 7.4°, respectively, and the root mean square error, standard deviation, and bias of the inverse wind speed are 2.33, 1.495, and −1.375. The ResNet is better than the conventional local gradient and C‐band model models, further confirming the advantages shown by the deep learning algorithm in nonlinear fitting and its potential application in sea surface wind field inversion.