A unified C-band and Ku-band geophysical model function determined by neural network approach

作     者：ZOU Juhong LIN Mingsen PAN Delu CHEN Zhenghua YANG Le 

作者机构：State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography State Oceanic Administra-tion Hangzhou 310012 China Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China National Satellite Ocean Application Center National Marine Environmental Forecasting Center State Oceanic Administra-tion Beijing 100081 China 

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

年 卷 期：2008年第27卷第6期

页      面：33-39页

核心收录：

学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 081002[工学-信号与信息处理] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Basic Research and Development Program("973" Program),under contract No.2009CB421202 the National Natural Science Foundation of China under contract No. 40706061 the National High Technology Development Program ("863"Program),under contract Nos 2007AA12Z137 and 2008AA09Z104 

主　　题：GMF neural network CMOD4 QSCAT-1 

摘      要：The geophysical model function （GMF） describes the relationship between backscattering and sea surface wind, so that wind vec- tors can be retrieved from backscattering measurement. The GMF plays an important role in ocean wind vector retrievals, its performance will directly influence the accuracy of the retrieved wind vector. Neural network （NN） approach is used to develop a unified GMF for C-band and Ku-band （NN-GMF）. Empirical GMF CMOIM and QSCAT-1 are used to generate the simulated training data-set, and Gaussian noise at a signal noise ratio of 30 dB is added to the data-set to simulate the noise in the backscat- tering measurement. The NN-GMF employs radio frequency as an additional parameter, so it can be applied for both C-band and Ku-band. Analyses show that the %predicted by the NN-GMF is comparable with the σpredicted by CMOIM and QSCAT-1. Also the wind vectors retrieved from the NN-GMF and empirical GMF CMOIM and QSCAT-1 are comparable, indicating that the NN-GMF is as effective as the empirical GMF, and has the advantages of the universal form.