Development of a simplified technique for gap filling of Normalize Difference Vegetation Index (NDVI) time series data

Abstract

The presence of gaps or missing values in time series prevents the practical use of such data. The current research aims at developing a simplified, straightforward technique for gap-filling the time series data of the Normalize Difference Vegetation Index (NDVI) generated using Moderate Resolution Imaging Spectroradiometer (MODIS). This research assumes that a relationship exists between the pixel location, date of acquisition and its NDVI value within a defined timeline. Therefore, two relatively simple methods were tested: the Multiple Linear Regression (MLR) analysis and the Artificial Neural Networks (ANN)to fill the NDVI missing values. While MLR is a well-known simple statistical method, the ANN has been successfully applied for the analysis of various scientific data, including the gap-filling of time series data. Nevertheless, ANN proved its supremacy in such approach. The accuracy of estimation utilizing the developed ANN model reached an average of r2 of 0.8, while the average accuracy of MLR was about 0.3. Nevertheless, the developed model could only be applied within the same timeframe of the images used for developing the model. Otherwise, the accuracy of determination was reduced significantly. The results showed that according to its performance, ANN are promising for filling missing data of NDVI time series and could be applied to any other vegetation indices as well.