The regular monitoring of agricultural areas is extremely important for mitigating food insecurity risks and for planning government interventions. In the literature, several deep learning algorithms have been recently proposed to perform land cover/ land use classification by using multispectral optical images. However, most of the considered deep learning models, such as the standard Convolutional Neural Networks (CNN), rely on mono-temporal images, focusing on spectral and textural features while discarding the temporal component, which is crucial for the accurate crop type mapping. In this work, we exploit a Long Short Term Memory (LSTM) deep learning classification architecture to characterize agricultural area dynamics by using the multitemporal multispectral information provided by satellite multispectral sensor Sentinel 2. Instead of considering a pre-trained network and applying to it a fine-tuning, the proposed architecture is trained from scratch in order to be tailored to the specific properties of the long time series of Sentinel 2 multispectral images. To face the lack of labeled training database, existing crop type maps available at the country level are used to generate a large set of weak reference data. First, the proposed method automatically extracts a large training dataset from existing crop type maps, by detecting those samples having the highest probability of being correctly classified. Then, the weak labeled samples extracted are used to train the deep LSTM architecture on a time series of Sentinel 2 images acquired over an entire year. The preliminary results obtained demonstrate the effectiveness of the proposed approach, which is promising at large scale.