摘要
盐度是描述海洋的关键变量,对海表面盐度进行观测可以推进对全球水循环的理解。本文的主要目的是在中国近海海域对SMOS卫星盐度数据进行准确度评估。主要方法是将SMOS卫星L2海洋盐度数据产品(V317)与实测ARGO数据和走航数据进行匹配,并采用统计学的方法对SMOS卫星数据准确度进行评估。结果表明:匹配数据的线性关系不显著,SMOS卫星盐度数据(V317)在南海和东海的均方根误差分别约为1.2和0.7,应用海表面粗糙度修正模型得到的3组海表盐度数据准确度都相对较低,尤其在近岸强风场区域,海表盐度卫星数据相对于实测数据偏高,这可能是由于海表粗糙度和陆地射频干扰(RFI)作用影响的结果;SMOS卫星数据在东海的均方根误差比南海高0.5左右,这可能是由于东海海域为相对开阔海域,受陆地RFI影响相对南海较小;在中国近岸海域,应用SSS1和SSS3模型得到的盐度数据准确度相对较高,可以对模型进行地球物理参数修正,进行局地化改进,预计可以提高近岸海域盐度反演的准确度。
The ocean salinity is a key variable to describe the ocean. Observing sea surface salinity （SSS） can promote the understanding of global water cycle. This paper main aims to carry out the accuracy assessment of SMOS satellite SSS data in the coastal waters of China. The main methods of this paper is that matching SMOS satellite L2 ocean salinity data products （V317） with the in-situ ARGO data and navigation data, evaluating SMOS satellite data using statistics method. The results show that the linear relationship of the matched data is not significant, and the RMSE of the SMOS SSS in South China Sea and East China Sea are 1.2 and 0.7 respectively. The three sets of SSS data, which are acquired applying three models in correcting the sea surface roughness are all with rela tively low accuracy, especially in coastal strong wind areas,the SSS value observed by satellite is overestimated, and SMOS SSS retrieve may be seriously influenced by the sea surface roughness and land radio frequency. The RMSE of SMOS SSS data in South China Sea is higher than the East China Sea around 0.5. the degree of accuracy of salinity data using SSS1 and SSS3 are relatively high in the China seas. In further application, higher accuracy can be purchased by amending geophysical parametersand applying local improvement in China seas.
出处
《海洋学报》
CAS
CSCD
北大核心
2013年第5期169-176,共8页
基金
海洋公益性行业科研专项经费项目(20090592)
关键词
卫星遥感
海表盐度
ARGO
粗糙度修正模型
近岸影响
satellite remote sensing
sea surface salinity
ARGO
roughness correction model
coastal effect
