Forecasting vegetation indices represents a hot topic nowadays because of the climate change impacts challenges. Several time series forecasting models are proposed in the literature. However, researchers are continuously looking for an accurate and more performing one. This paper proposes a novel way to combine forecasting models results. Particularly interested in vegetation changes, our study relies on NDVI time series extracted from MODIS DATA. These temporal profiles describe a forest cover located in the northwestern of Tunisia, an African country. To this aim, normally fitted weights and evidence theory were selected to build an accurate combination model. Our findings highlight: on one hand, the importance of studying the statistical properties of the analyzed data. On the other hand, they shed the light on the ability of increasing the accuracy of forecasting models by combining them.