基于SMOS卫星数据的海表面盐度模型

Study on the Sea Surface Salinity Model Based on SMOS Data

海表面盐度是研究海洋对全球气候影响以及大洋环流的重要参量之一,而卫星遥感技术是获取海表面盐度数据的最有效方法。目前,L波段的SMOS和Aquarius/SAC-D遥感卫星正在用于探测海表面盐度,并根据卫星观测数据和物理机制反演出海表面盐度的产品。但在某些近陆地区域,由于淡水流入及陆地射频(RFI)等因素影响,卫星反演盐度的产品精度较低。文中利用"东方红2号"科学考察船的实测数据、SMOS卫星数据,首次针对中国南海海域提出了用贝叶斯网络模型计算海表面盐度,并用验证数据集(实测Argo盐度)对模型进行适应性评估。经过计算,模型误差和验证误差分别为0.47 psu和0.45 psu,而相应的SMOS Level 2产品的精度分别为1.90 psu和1.82 psu。此模型为海表面盐度的计算提供了一个新方法。

Sea surface salinity（SSS） is a key parameter for studying the effects of the ocean on global climate and ocean circulation, and satellite remote sensing detection functions as the most effective means to obtain SSS data.Currently, L-band SMOS and Aquarius / SAC-D satellites are being used to detect SSS based on observing data and the physical mechanism. However, in some near-shore areas, due to the inflow of freshwater and terrestrial radio frequency interference, the precision of salinity satellite products is relatively low. This paper uses the measured data from the ＂Dong Fang Hong 2＂ scientific expedition ship and SMOS data to predict SSS by the Bayesian network model for the first time in the South China Sea, and assesses the model with validation data sets（measured Argo salinity）. Analysis results show that the model error and validation error is 0.47 psu and 0.45 psu, respectively, while the precision of SMOS Level 2 products is 1.90 psu and 1.82 psu, respectively. This model provides a new method to predict SSS.