Prediction of Plant Growth Based on Statistical Measurements Using Satellite Image Time Series

This paper presents new approaches to forecast the plant growth based on statistical methods: autoregressive and Markov chain models, using a time series of the normalized different vegetation index. Here, a monthly normalized different vegetation index time series was derived from Sentinel-2 over Limaya olive tree fields from January 2016 to November 2019. To ensure consistent prediction, processing is done over homogeneous clusters of vegetation. Finally, the performance of our approach is evaluated by means of the root mean square error between the predicted and true values.