Interpolation of Geophysical Data using Spatio- temporal (3D) Block Singular Value Decomposition Anish C. Turlapaty 1, 2 , Nicolas H. Younan 1, , and Valentine G. Anantharaj 2 1 Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762 2 Geosystems Research Institute, Mississippi State University, Mississippi State, MS 39762, USA Background: Spectral analysis of geophysical data is a popular tool for understanding the spatio-temporal signals underlying the data. These frequency domain methods need complete data with uniform sampling for a thorough analysis. In general, geophysical data obtained through satellites have several gaps. Several alternative techniques have been developed to analyze such datasets. However, interpolation of the missing values is the best solution to facilitate an effective analysis of geophysical data. For instance, in a given region over a period of time the soil moisture products available from the Advanced Microwave Scanning Radiometer-Earth Observation System (AMSR-E) onboard the National Aeronautic and Space Administration’s (NASA) Aqua satellite has many inherent gaps due to the orbital coverage of the satellite. For a region in the Southeast United States, data is collected for years 2005 and 2006. This dataset has nearly 30% missing data due to sampling patterns, radio interference, retrieval issues and instrument errors. Interpolation of these missing values can be invaluable in hydrological applications. Proposed Method: To address this issue, we have developed and implemented an iterative block SVD interpolation scheme. Our methodology is a generalization of Becker and Rixen’s SVD method [1]. This revised technique approaches the SVD method when the spatio-temporal block equals the entire dataset and it is similar to the M-SSA (Multi channel Singular spectral analysis) method with M = 1 [2]. Our SVD-based interpolation scheme consists of two stages: decomposition and reconstruction. In step one, SVD analysis is performed on the full covariance matrix of a spatio-temporal data (3D) block to generate a set of eigenvectors and eigenvalues. From the set of eigenvectors, the dominant modes that contribute to a major portion of the variance of the dataset are selected. Then, projections of the dataset on each of the mode or eigenvector are computed. In step two, using these projections and the selected SVD modes, the