Phenology as accuracy metrics for vegetation index forecasting over Tunisian forest and cereal cover types

Abstract

ABSTRACT Different studies on predicting future green cover changes exist with various success levels. Each one focuses on a different story about which model is more appropriate. Therefore, finding a suitable model remains a difficult task due to the remotely sensed data issues and the complexity of the vegetation cover change process. Despite the unicity of vegetation indices time series, the forecasting assessment relies basically on the commonly used forecast error measurements (e.g. Mean Square Error (MSE), Root MSE (RMSE), and the symmetric Mean Absolute Percentage Error (sMAPE), etc.) which may not reflect the real potential of the fitted forecasting model. Herein, the experimentation of forecasting vegetation indices employing two univariate time series models and new accuracy metrics is discussed. Box Jenkins models (Seasonal AutoRegressive Integrated Moving Average (ARIMA) model) and neural network model (nonlinear autoregressive (NAR) model) are applied individually and coupled (NAR-NAR and NAR-ARIMA) based on a multi-resolution analysis-wavelet transform. These models’ forecasting ability is evaluated using 16-days Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index (NDVI) time series data of two different vegetation cover types of northwestern area in Tunisia. The major finding highlights firstly the importance of integration of the decomposition step to show off the hidden components of vegetation indices. The commonly used accuracy measures show that the coupled neural networks model outperforms other models. Then, interesting conclusions were drawn when phenological metrics are used as performance measures. Herein, Box Jenkins forecasting model generates a better NDVI curve shape than hybrid models despite their low RMSE measures. This is mainly due to good estimation of some phenological events, namely, the amplitude, the peak and the season’s length. Generally, Box Jenkin model excels at handling quick variations. By contrast, combined models show better phenological metrics’ estimation when the observations are complex or describe long time periods. Based on these findings, we suggest that the choice of the evaluation metric must be related to the future forecaster interest.