We applied the Normalized Difference Vegetation Index (NDVI) as an indicator reflecting dynamics of vegetation cover growing. NDVI time series were derived from the Terra/MODIS satellite imagery (MOD09A1 product was used, available from the open archive of the US Geological survey). Data for 2006-2016 were used. The ground test plate is located in the Karelia Republic, Russia (61°,7218 N, 34°,3689 E). Moving average method with three inflection points was applied to ensure a piecewise monotone smoothing of NDVI time series. We also applied meteorological data (surface air temperature, average daily precipitation, average snow cover heightcollected at Petrozavodsk meteorological station in 2006-2016). Used meteorological data were derived from the website of the Russian Federal Service for Hydrometeorology and Environmental Monitoring -ROSHYDROMET (http://meteo.ru). As a result, we designed a methodology for selection and processing (including initial compilation, smoothing and interpolation of time series) of remote sensing and meteorological observations to generate training data and control datasets demanded by artificial neural network. Obtained results demonstrate that neural network trained using meteorological data can predict NDVI values. Less than 15% relative error of the NDVI prediction was gained at our ground test plate.