NDVI Short-Term Forecasting Using Recurrent Neural Networks

Arthur Stepchenko, Jurij Chizhov


In this paper predictions of the Normalized Difference Vegetation Index (NDVI) data recorded by satellites over Ventspils Municipality in Courland, Latvia are discussed. NDVI is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. Artificial Neural Networks (ANN) are computational models and universal approximators, which are widely used for nonlinear, non-stationary and dynamical process modeling and forecasting. In this paper Elman Recurrent Neural Networks (ERNN) are used to make one-step-ahead prediction of univariate NDVI time series.


Artificial Neural Networks; Elman Recurrent Neural Networks; Normalized Difference Vegetation Index

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DOI: https://doi.org/10.17770/etr2015vol3.167


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