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Retrieval and Assessment of Significant Wave Height from CYGNSS Mission Using Neural Network
2022
Remote Sensing
In this study, we investigate sea state estimation from spaceborne GNSS-R. Due to the complex scattering of electromagnetic waves on the rough sea surface, the neural network approach is adopted to develop an algorithm to derive significant wave height (SWH) from CYGNSS data. Eighty-nine million pieces of CYGNSS data from September to November 2020 and the co-located ECMWF data are employed to train a three-hidden-layer neural network. Ten variables are considered as the input parameters of the
doi:10.3390/rs14153666
fatcat:b7sfg3gj6fddhgjgxpkh22li4q