Investigating the applicability of using the DINEOF algorithm for gap-filling in satellite data

Abstract

Earth observation data from satellites is crucial for monitoring environmental changes and supporting sustainability initiatives. However, gaps in satellite data can significantly undermine the data’s usefulness and reliability. The primary objective of this research is to address these gaps using the Data Interpolating Empirical Orthogonal Functions (DINEOF) technique. The study specifically targets chlorophyll-a data from the SENTINEL-2 satellites, covering the western coast of South Africa and the Maltese Islands. To evaluate the effectiveness of the DINEOF algorithm in reconstructing missing data, the study introduces artificial gaps into the dataset. It then assesses the algorithm’s performance through calculating the root mean square error (RMSE). The findings indicates that the DINEOF algorithm effectively fills gaps of varying sizes with low RMSE values, thereby preserving the continuity and integrity of the data. This work advances the application of DINEOF in remote sensing and environmental research, bolstering more effective disaster response strategies and enhanced global sustainability management.