利用SMOS卫星数据反演海洋盐度方法研究
Research of the Sea Surface Salinity Retrieval Method based on SMOS Data
查看参考文献13篇
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文摘
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以星载微波遥感的辐射传输方程为基础,利用SMOS(土壤湿度海洋盐度)卫星的L1C级亮温数据,通过与辐射传输模型模拟的亮温进行对比,评估及验证亮温的数据质量,建立了海洋盐度反演算法。通过分析2012年7月东南太平洋区域(45°~5°S,140°~90°W)的下降轨道数据,发现MIRAS亮温与模型模拟亮温之间总是存在几K的系统偏差,即OTT,因此提出了两种反演盐度的方法:一种是修正OTT偏差,使用入射角0°~55°的数据反演盐度;另一种是不修正OTT偏差,使用大入射角范围35°~55°的数据进行盐度反演。再通过利用MIRAS多角度信息,对亮温作二阶多项式拟合,减少随机噪声对反演的影响。最后采用最小二乘法,使得MIRAS的二阶拟合亮温与模型仿真亮温最接近,迭代反演盐度值。并将反演结果分别与欧空局的L2级盐度数据产品和Argo盐度数据进行比较,来验证反演算法。结果表明:修正OTT之后全角度数据反演的盐度值在50 km × 50 km范围内、卫星过境前后5 d,与Argo浮标盐度匹配比较的均值为1. 38 pss,标准差为0. 35 pss;不修正OTT,直接利用大入射角范围35°~55°的MIRAS亮温反演盐度,与Argo盐度误差均值为0. 03 pss,标准差为0. 33 pss;同时欧空局的L2级盐度与Argo盐度误差均值为0. 26 pss,标准差为0.38 pss。可见利用大入射角范围的反演方法很好地反演了海洋盐度。 |
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其他语种文摘
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Sea surface salinity is one of the most important parameters in the ocean system, and it is also critical to the research of the global climate system and ocean circulation. The quality of the SMOS (Soil Moisture and Ocean Salinity) brightness temperature (MIRAS TB) is assessed and validated by comparing with simulated brightness temperature, which is computed using the default radiative transfer model implemented in the ESA (European Space Agency) L20S processor, with auxiliary data from ECMWF (European Centre for Medium Range Weather Forecasts). Then the retrieval algorithm of sea surface salinity is built up based on the radiation transfer equation for spaceborne microwave remote sensing. The descending SMOS data of the Southeast Pacific Ocean region (45°?5°S, 140°?90°W) during July 2012 is analyzed,and biases of several Kelvins are observed between average MIRAS TB and simulated TB, which are called OTT (Ocean Target Transformation) and heavily affect the accuracy of SSS retrieval. Two methods are proposed in the paper to deal with the OTT. The first is that the OTT is corrected, and the whole angular data is used for retrieval. The second is that the OTT is not corrected, and only the incidence angle range of 35 to 55 degrees are used. Then all measurements of a given Stokes parameter in a pixel are fitted to incidence angles in two order polynomial by using MIRAS multi-angular property, in order to reduce the effect of random noise. At last, the simulated TBs computed by the guessed SSS and the forward model are compared to MIRAS TBs, and then the guessed SSS is modified in an least square iterative process until reaching the maximum similarity between both TB values. The retrieved SSS of the two methods is compared with Argo SSS and ESA L2 SSS to be validated, and SMOS SSS is collocated with Argo SSS using collocation radii of ±5 days and 50 kmX 50 km. The comparisons show that, the mean of the difference between the first method SSS, which is that the OTT is corrected before retrieval, and Argo SSS is 1. 38 pss, and the standard deviation of the difference is 0. 35 pss ; The mean of the difference between the second method SSS, which is that the OTT is not corrected, and the incidence angle range of 35 to 55 degrees are used, and the Argo SSS is 0. 03 pss, and the standard deviation is 0. 33 pss ; The mean of the difference between ESA L2 SSS and Argo SSS is 0. 26 pss, and the standard deviation is 0. 38 pss. So the second method can be used to retrieve SSS more accurately. |
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来源
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遥感技术与应用
,2014,29(3):401-409 【核心库】
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关键词
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海洋遥感
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SMOS
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综合孔径辐射计
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盐度反演
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地址
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中国科学院空间科学与应用研究中心, 中国科学院微波遥感技术重点实验室, 北京, 100190
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1004-0323 |
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学科
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海洋学;自动化技术、计算机技术 |
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基金
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国家自然科学基金项目
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文献收藏号
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CSCD:5174903
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参考文献 共
13
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