一种遥感土壤湿度误差空间协方差的估算方法

An Approach to Estimating Spatially Correlated Error Covariance of Remote Sensing Retrieved Soil Moisture

  • 摘要: 针对遥感反演土壤湿度空间相关的误差协方差难以估计的问题,提出了一种遥感反演数据误差空间协方差估算方法——3类数据集成分析误差协方差(triple collocation covariance,TC_Cov),将土壤湿度场的每个单元(像元)看作一个空间随机变量,用两个随机变量表示的土壤湿度值的时间序列作为样本进行空间协方差估计,由任何两个随机变量的协方差形成多个随机变量(随机场)的协方差矩阵。利用先进散射计(ad-vanced scatterometer,ASCAT)和热带降雨测量卫星(tropical rainfall measuring mission,TRMM)的遥感土壤湿度数据以及ERA-Interim土壤湿度再分析数据作为TC_Cov方法的输入数据,分别估算了ERA-Interim、AS-CAT和TRMM在澳大利亚Murrumbidgee流域的土壤湿度误差协方差矩阵,验证了估算方法的合理性和可行性。

     

    Abstract: We have designed an approach TC_Cov based on triple collocation covariance, which can be used to estimate spatially correlated error covariance of three soil moisture data sets retrieved from satellite data. In theory, we formalize each grid into one random variable and soil moisture time series of two grids into samples of two random variables, then the covariance of two random variables are estimated. An element of multivariate covariance matrix takes bivariate covariance. The experimental results are analyzed in a qualitative way. In the experimental region of Murrumbidgee catchment located in Australia, the error covariances of soil moisture data sets retrieved from ASCAT scatterometer, TRMM radiometer and ERA-Interim reanalysis data are estimated, and experimental results obey the Tobler's first law well and explain real soil moisture error covariance estimation effectively.

     

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