Nonparametric estimations of the sea state bias for a radar altimeter

作     者：MIAO Hongli JING Yujie JIA Yongjun LIN Mingsen ZHANG Guoshou WANG Guizhong 

作者机构：College of Information Science and Engineering Ocean University of China National Satellite Ocean Application ServiceState Oceanic Administration 

出 版 物：《Acta Oceanologica Sinica》 (海洋学报（英文版）)

年 卷 期：2017年第36卷第9期

页      面：108-113页

核心收录：

学科分类：08[工学] 0816[工学-测绘科学与技术] 

基　　金：The National Key R&D Program of China under contract No.2016YFC1401004 the National Natural Science Foundation of China under contract Nos 41406207,41176157 and 41406197 

主　　题：radar altimeter sea state bias significant wave height wind speed nonparametric model parametric model 

摘      要：To estimate the sea state bias（SSB） for radar altimeter, two nonparametric models, including a Nadaraya-Watson（NW） kernel estimator and a local linear regression（LLR） estimator, are studied based on the Jason-2 altimeter data. Selecting from different combinations of the Gaussian kernel function, spherical Epanechnikov kernel function, a fixed bandwidth and a local adjustable bandwidth, it is observed that the LLR method with the spherical Epanechnikov kernel function and the local adjustable bandwidth is the optimal nonparametric model for the SSB estimation. The comparisons between the nonparametric and parametric models are conducted and the results show that the nonparametric model performs relatively better at high-latitudes of the Northern Hemisphere. This method has been applied to the HY-2A altimeter as well and the same conclusion can be obtained.