基于多源参数合并的海洋数据分辨率优化分析

Analysis of Ocean Data Merge Based on Multi⁃source Parameters

精准的高分辨率海洋环境数据,尤其是在低密度海面测量装置的海域中,对于渔业资源的规划和管理至关重要。为此以2009年1月至2010年12月收集的南海海域海表温度(Sea surface temperature,SST)数据为样本,采用逐次校正法(Successive correction method,SCM)对卫星遥感和海洋浮标所测得的海洋环境数据进行合并,以提高大尺度下的海洋环境数据分辨率,合并结果与最优插值(Optimal interpolation,OI)方法进行了比较。结果表明,SCM和OI方法的地图检测结果虽有相似的空间分布,但由空间统计分析可知,SCM方法的平均误差和平均绝对误差更接近;留一法交叉验证表明,SCM的均方根误差更小。因此,相比传统的OI方法,SCM通用性更强,可以增加校正的迭代平滑性,具备实时提供高分辨率精准的海洋环境数据的能力。

It is essential for the planning and management of fishery resources that accurate and highresolution marine environmental data in the sea area of low-density sea surface measurement devices.Hence,the sea environmental data measured by satellite remote sensing and marine buoys are merged to improve the resolution of sea environmental data at a large scale by the successive correction method(SCM).The South China Sea surface temperature(SST)data collected from January 2009 to December 2010 are taken as a sample.The SCM algorithm is more versatile with fast calculation speed.The iterative smoothness of the correction can be improved,compared with the traditional optimal interpolation(OI)method.