Reconstruction of incomplete satellite oceanographic data sets based on EOF and Kriging methods

Abstract

A complete data set is crucial for many applications of satellite images. Therefore, this paper tries to reconstruct the missing data sets by combining Empirical Orthogonal Functions(EOF) decomposition with Kriging methods. The EOF-based method is an effective way of reconstructing missing data for large gappiness and can maintain the macro-scale and middle-scale information of oceanographic variables. As for sparse data area (area without data or with little data all the time), EOF-based method breaks down, while Kriging interpolation turns effective. Here are the main procedures of EOF-Kriging(EOF-K) method: firstly, the data sets are processed by the EOF decomposition and the spatial EOFs and temporal EOFs are obtained; then the temporal EOFs are analyzed with Singular Spectrum Analysis(SSA); thirdly, the sparse data area is interpolated in the spatial EOFs by using Kriging interpolation; lastly, the missing data is reconstructed by using the modified spatial-temporal EOFs. Furthermore, the EOF-K method has been applied to a large data set, i.e. 151 daily Sea Surface Temperature satellite images of the East China Sea and its adjacent areas. After reconstruction with EOF-K, comparing with original data sets, the root mean square error (RMSE) of cross-validation is 0.58 °C, and comparing with in-situ Argo data, the RMSE is 0.68 °C. Thus, it has been proved that EOF-K reconstruction method is robust for reconstructing satellite missing data.