NDVI forecasting model based on the combination of Time series decomposition and CNN - LSTM

Abstract

Abstract Normalized difference vegetation index(NDVI) is the most commonly used factor to reflect vegetation growth status, and improving the prediction accuracy of NDVI is of great significance to the development of regional ecology. In this study, a new NDVI forecasting model based on the combination of time series decomposition(TSD), convolutional neural network (CNN) and long short-term memory (LSTM) was proposed. In order to verify the performance of TSD-CNN-LSTM model and explore the response of NDVI to climatic factors, two forecasting models of temperature and precipitation based on its own historical information and four NDVI forecasting models that based on Temperature, precipitation and its own historical information were established. The results show that TSD-CNN-LSTM model based on its own historical information had the best forecasting performance, with the RMSE, NSE, MAE to be 0.4996, 0.9981, 0.4169 for temperature, 5.6941, 0.9822, 3.9855 for precipitation and 0.0573, 0.9617, 0.0447 for NDVI, respectively. Meanwhile, the NDVI forecasting models based on climatic factors show that the model based on the combination of temperature and precipitation has the better effect than that based on single temperature and single precipitation. Combined with the results of correlation analysis it can be inferred that NDVI changes are most significantly influenced by the combination of temperature and precipitation, followed by temperature, and least influenced by precipitation. The above findings can provide a meaningful reference and guidance for the study of vegetation growth with climate changes.