Iterative Empirical Orthogonal Function in Gap Filling of GPS and InSAR Data

Missing values or gaps in observational datasets are a common problem. Data gaps may lead to inaccurate inference about the underlying process. In Synthetic Aperture Radar (SAR) images, the reason could be interference suppression technical limitations, whereas in Global Positioning System (GPS) time series data, the gaps may occur due to equipment changes or connectivity failure. This study proposes a flexible, iterative, and easy-to-implement Empirical Orthogonal Function (EOF) based algorithm to fix data-gaps in geodetic time series data. Two datasets are considered for illustration. The performance of the algorithm is found to be satisfactory for both datasets.