SMAP卫星的RBF神经网络海表盐度遥感反演  

Remote Sensing Retrieval of Sea Surface Salinity Based on RBF Neural Network From SMAP Satellite

作　　者：赵文杰 李洪平[1] 刘海行 ZHAO Wen-jie;LI Hong-ping;LIU Hai-xing(Division of Information Science and Engineering,Ocean University of China,Qingdao 266100,China;First Institute of Oceanography,MNR,Qingdao 266061,China)

机构地区：[1]中国海洋大学信息科学与工程学部,山东青岛266100 [2]自然资源部第一海洋研究所,山东青岛266061

出　　处：《海洋科学进展》2022年第3期513-522,共10页Advances in Marine Science

基　　金：自然资源部全球变化与海气相互作用专项(二期)资助项目——海洋动力系统多尺度相互作用及其参数化评估;国家自然科学基金委员会-山东省人民政府联合基金项目——海量数据驱动下的高分辨率海洋数值模式关键算法研究(U1806205)。

摘　　要：针对传统海表盐度遥感反演精度不高、影响因素较少等问题,本文基于SMAP(Soil Moisture Active Passive)卫星L2C(Level 2 C)数据、Argo(Array for Real-time Geostrophic Oceanography)数据和其他辅助数据,以太平洋部分海域(160°E~120°W,0°~30°N)为研究区域,综合考虑海面粗糙度以及白冠覆盖率等参量,利用径向基神经网络建立RBF亮温增量模型,并对平静海面亮温进行修正,然后基于Meissner-Wentz介电常数模型得到反演后的盐度值。验证结果表明:模型预测盐度和SMAP卫星盐度相对于Argo实测盐度的均方根误差分别为0.4和0.5,平均绝对误差分别为0.3和0.4。实验证明,利用RBF神经网络建立的亮温增量模型可以提高海表盐度反演的精度,对海表盐度反演具有实用意义。In order to address the problems of traditional Sea Surface Salinity remote sensing inversion methods,which have poor inversion accuracy and few influencing factors,this paper proposes a new retrieval method using SMAP L2C data,Argo data,and auxiliary data,with a focus on(160°E-120°W,0°-30°N)in the Pacific Ocean.The proposed method considering more influence factors such as sea surface roughness and whitecap coverage,and firstly establishes an RBF bright temperature increment model using RBF neural network and modifies the brightness temperature of the flat sea surface,and then retrieves the SSS based on the Meissner-Wentz dielectric constant model.The validation results showed that the root mean square error of model predicted salinity and SMAP satellite salinity relative to Argo measured salinity were 0.4 and 0.5,respectively,and the mean absolute error were 0.3 and 0.4,respectively.Experiments show that the brightness temperature increment model established by RBF neural network can improve the accuracy of Sea Surface Salinity inversion,and has practical significance for Sea Surface Salinity inversion.

关 键 词：海表盐度 SMAP卫星 RBF神经网络 盐度反演 

分 类 号：P731.12[天文地球—海洋科学] TP79[自动化与计算机技术—检测技术与自动化装置]