Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering

NDVI (Normalized difference vegetation index) is a critical variable for monitoring climate change, studying ecological balance, and exploring the pattern of regional phenology. Traditional neural network models only consider image features in time series prediction, while historical data and its changes play an important role in time series forecasting. For this study, we proposed convolutional neural networks (CNN) combined feature engineering forecasting model (SF-CNN), which integrated both the advantages of image characteristics learned from CNN and statistic characteristics calculated by historical data related to the forecast period to improve the accuracy of NDVI predictions in the next 3 months with 30-day interval at multiple complex areas. To intuitively show the performance of SF-CNN, it was compared with CNN using the same parameters. Results mainly showed that (1) in terms of visual analysis, the texture, pattern, and structure of predicted NDVI using SF-CNN are similar to the observed NDVI, and SF-CNN exhibits strong generalization ability; (2) in terms of quantitative assessment, SF-CNN generally outperforms CNN, and it can improve the reliability and robustness for predicting NDVI through simple statistical characteristics while reducing the uncertainties; (3) SF-CNN can learn seasonal and sudden changes in four different and complex study areas with considerable accuracy and without extra data.