NDVI Time Series Reconstruction Using Morphological Filtering

Abstract

Time series of Normalized Difference of Vegetation Index (NDVI) values, derived from satellite data, are useful for monitoring the vegetation status and can form a basis for more advanced analysis. However, data in these time series is affected by noise caused primarily by atmospheric conditions and acquisition errors, resulting in glitches and prompting the need for developing reconstruction techniques that can efficiently remove the noise. A multitude of approaches have been developed so far, including a variety of temporal-based methods that include filtering techniques. In this letter, a morphological filter with a non-flat structuring element is proposed for NDVI time series reconstruction. This method is applied on two time series obtained from the Copernicus Global Land Service 300 m NDVI product. The experimental results prove the effectiveness of the proposed approach in producing high-quality NDVI reconstructions, highlighted by the significantly better root mean square error (RMSE) values obtained on the considered time series in comparison with three well-established techniques.