Reconstruction of Wide Swath Significant Wave Height From Quasi-Synchronous Observations of Multisource Satellite Sensors

Global sea wave monitoring is of utmost importance for tasks such as analysis of ocean climate change, offshore fisheries, and early warning of marine disasters. Significant wave height (SWH) is one of the most vital and widely used metrics for measuring sea waves in marine research. Hence, obtaining high precision and extensive coverage measurements of SWH is of great significance for comprehensive sea wave studies. A data set is constructed by combining wave spectrometer and scatterometer data from China-French Ocean Satellite, synthetic aperture radar (SAR) wave mode data from Sentinel-1, and altimeter data from Jason-3 and HY-2B through space-time matching method. The multi-sources integrated data set is used to complete the reconstruction of the wide swath SWH. A model based on stacked autoencoder and deep neural network (SAE-DNN) is developed. The SWH reconstructed by the model is evaluated with the training-independent test set. The results demonstrate that the accuracy of the SAE-DNN model is significantly improved by incorporating SAR joint quasi-synchronous observations and the root mean square error can reach 0.217 m, which is comparable to the SWH measured by altimeter and highlights the effectiveness and reliability of the model in accurately reconstructing SWH. We further examine and analysis the distance variations between SWH reconstruction sites and Surface Wave Investigation and Monitoring observations, the influence of different SAR features, and the influence of sea state, highlighting the benefits of incorporating SAR data into SAE-DNN model. Using remote sensing satellites to observe significant wave height (SWH) with high precision and extensive coverage can provide more valuable data sets for sea wave research. A model for retrieving the total wave height is developed based on a stacked autoencoder and deep neural network (SAE-DNN). Sentinel-1 synthetic aperture radar (SAR) data is introduced to simulate the quasi-synchronous observations with China-French Ocean Satellite and the altimeter observations of Jason-3 and HY-2B are used as the target value for model training. After evaluation and analysis, satisfactory results are found. The results also demonstrate that the addition of joint SAR quasi-synchronous observations can have a highly positive impact on SWH reconstruction. Quasi-synchronous observations of multisource satellite sensors are matched and leveraged to construct the data set A model of autoencoder deep neural network is developed for the reconstruction of wide swath significant wave height The joint synthetic aperture radar enables the model to better learn the swell features and provides effective information for the external swath