The use of high spatial resolution Satellite Image Time Series (SITS) provides an opportunity for a wide spectrum of Earth surface monitoring applications such as Land Use/Land Cover (LULC) mapping. Whereas the majority of Time Series (TS) classification literature concentrates on the analysis of raw 1D signals, here, we investigate a framework for LULC mapping based on 2D encoded multivariate SITS data to enhance their classification performances. In this novel approach, multivariate SITS data are transformed from 1D signals to 2D images using several encoding techniques namely Gramian Angular Summation field (GASF), Gramian angular difference field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP). Successively, a new multi-band image is derived and it is used as input to a state-of-the-art convolutional neural network (CNN) classification model. The possibility to effectively encode multivariate TS data into 2D representations paves the way to reuse the huge amount of research findings coming from the general field of computer vision and build on reliable and robust methods that have been demonstrated their quality in a multitude of downstream applications. Experiments carried out on three real-world benchmarks covering large spatial areas with contrasted land cover features, namely: Dordogne department in France, Reunion Island an oversee French territory and Koumbia municipality in Burkina Faso, underline the quality of the proposed framework when compared to standard approaches for land cover mapping from SITS and recent methods for multivariate TS classification. Matter of fact, our new framework outperforms the classification performances of standard land cover classification strategies based on the raw TS information achieving an average F1-score of 89.34%, 90.26% and 78.94% for the Reunion Island, Dordogne and Koumbia study site, respectively with an increasing of at least 2.5 points w.r.t. the best competing approach.