A novel model of estimating sea state bias based on multi-layer neural network and multi-source altimeter data. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Miao, Hongli; Guo, Yingting; Zhong, Guoqiang; Liu, Benxiu; Wang, Guizhong
• Is Part Of: European journal of remote sensing. Volume 51:Issue 1(2018)
• Page Start: 616
• Page End: 626
• Publication Date: 2018-01-01
• Subjects: Sea state bias -- radar altimeter -- neural network; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2018.1465361
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: In this article, we propose a novel model for estimating sea state bias (SSB) based on multi-layer neural network and multi-source altimeter data from the Topex/Poseidon (T/P), Jason-2, and Jason-3 altimeters. Significant wave height (SWH), wind speed (U) and backscatter coefficient (σ0 ) are considered as the inputs of the multi-layer neural network, while the corresponding SSB as outputs. The neural network has four layers, with structure 3-3-6-1. Data from three seasons are employed for the neural network training, and the trained model is applied for the SSB estimation on the HY-2 altimeter data. To show the effectiveness of the adopted model, the correlations between SSB and SWH, U and σ0 are analyzed. Moreover, the explained variance and residual error are compared with a conventional parametric model for SSB estimation. The results demonstrate that multi-layer neural network trained on multi-source altimeter data performs superior to the conventional SSB estimation model.