Analysis and forecasting of the life cycle of vegetation are essential in planning agricultural work as well as monitoring of agricultural crops and forecasting their productivity. In practice, vegetation indices are often used that are calculated from the values of satellite image pixels like normalized difference vegetation index (NDVI). Forecasting of this index in precision agriculture allows indicating problems which are related to agricultural crop growth in time and make timely decisions about necessary measures to fix these problems. In the paper transferring approach for data preprocessing parameters and forecasting model is developed that provides forecasting of other normalized vegetation index time series without new preprocessing and training if the Euclidean distance between the time series used in the training and the time series using the data processing parameters and the forecasting model is small enough.