Interpolation of geophysical data using spatio-temporal (3D) block singular value decomposition
Soil moisture data 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. 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 radio interference, instrument errors, just to mention a few. To address this issue, an improved singular spectral analysis (SSA) -based interpolation scheme is presented. Our approach improves the existing method by utilizing the local variations in the observations for approximation of missing values and thus significantly improving the computational efficiency of the algorithm. For the validation of our interpolation scheme, a subset of SST from GODAE's high resolution sea surface temperature pilot project (GHRSST-PP) is considered. Finally, the presented scheme is also validated and tested on satellite based soil moisture retrievals from AMSR-E. Optimization of the method is based on minimizing the mean square error (MSE) and it is found to be dependent on the nature of the data. The top thirteen dominant SSA modes are usually sufficient for interpolation of missing values.