A deep learning approach for forecasting non-stationary big remote sensing time series

Abstract

Remote sensing (RS) data are undergoing an explosive growth. In fact, RS data are regarded as RS big data which generates several challenges such as data storage, analysis, applications, and methodologies. In this paper, a suitable method to forecast the Normalized Difference Vegetation Index (NDVI) time series (TS) from RS big data is introduced. In fact, we propose a non-stationary NDVI TS forecasting model by combining big data system, wavelet transform (WT), long short-term memory (LSTM) neural network. In the first step, the MapReduce algorithm was investigated for RS data storage and NDVI TS extraction. Then, the WT was used to decompose the TS into different components. Finally, LSTM was used for NDVI TS forecasting. Additionally, we have compared the forecasting results using only LSTM, recurrent neural network (RNN), and WT-RNN. Our results show that the proposed methodology using WT-LSTM model provides us an efficient method for forecasting NDVI TS in terms of root mean square error (RMSE) and Pearson correlation coefficient (R). Finally, we have evaluated the performance of the big data model.