Retrieval of Significant Wave Height from Sentinel-1 Wave Mode Data Under Rainfall Conditions With Dual-Branch CNN

Spaceborne synthetic aperture radar (SAR) is widely used in ocean wave monitoring due to its high resolution and all-weather observation capability. Rainfall distorts sea surface wave spectra and SAR imagery, yet current significant wave height (SWH) retrieval models do not account for the influence of rainfall. To address this gap, we construct a collocated dataset of 9,245 Sentinel-1 WV mode images and Copernicus Marine Environment Monitoring Service (CMEMS) SWH data. A dual-branch convolutional neural network (DB-CNN) is proposed for SWH retrieval. An Inception v3 model is employed to classify rainfall intensity grades from SAR images, which are then fed as auxiliary input to the DB-CNN model. Experimental results show that incorporating rainfall correction reduces the root mean square error (RMSE) of SWH retrieval from 0.48 m to 0.43 m (10% improvement). Independent validation using Jason-3 and SARAL data confirms the robustness of the model, it significantly reduces the estimation bias.