Retrieval of Ocean Wave Heights From Spaceborne SAR in the Arctic Ocean With a Neural Network
Ke Wu, Xiao‐Ming Li, Bingqing Huang
Abstract
Abstract The twin Sentinel‐1 (S1) satellites have been extensively acquiring synthetic aperture radar (SAR) data in the Arctic, providing the unique opportunity to obtain ocean dynamic parameters with both high spatial resolution and wide swath coverage in the Arctic Ocean. In this study, we proposed a method for retrieving the ocean significant wave height (SWH) from S1 SAR data in horizontal‐horizontal (HH) polarization based on a backpropagation neural network (BPNN). A total of 4,273 scenes from S1 extra‐wide swath mode data acquired in the Arctic were collocated with data from four radar altimeters (RA), yielding 126,128 collocated data pairs. These data were separated into training and testing data sets to develop the BPNN model for retrieving SWH. Comparing the S1 retrieved SWH using the testing data set with the RA SWH yielded a bias of 0.17 m, a root‐mean‐square error of 0.71 m and a scatter index (SI) of 23.05% for SWH less than 10 m. The S1 retrieved SWH was further compared with the CFOSAT/SWIM data acquired in the Arctic between August 2019 and May 2020, which suggests that the SWIM has different performances on wave measurements at different beams.