## 1 Introduction  
Producing more (food) with less (consumption of natural resources) is one of the biggest challenges our society is facing to guarantee food security globally and it is, therefore, one of the priorities of UN SDG goals. To achieve this, agricultural production must be supported by sustainable planning and one of the first pieces of information needed is \"where and when\" crops are cultivated. Satellite Remote Sensing is the best candidate as an information source to perform crop monitoring worldwide, but powerful and robust algorithm to provide temporal and spatially explicit information on crop presence are needed. Sentinel-2 (S2) is a Copernicus Earth observation mission that systematically acquires high spatial resolution optical images of Earth. This mission introduces a paradigm shift in the quality and quantity of open access data, opening a new era for land monitoring systems especially for the agricultural sector. S2 provides multi-spectral data with a spatial resolution from 10 m up to 60 m able to cover 290 km per acquisition with a revising time of 5 days thanks the two satellites constellation. This, however, introduces the notion of big data and therefore we need models that have the ability to exploit this enormous amount of information. In this framework, we want to contribute to research innovation devoted to perform crop identification from the analysis of time series of Sentinel-2 satellite imagery. The goal is to identify an approach that is able to reliably identify (classify) the different crops that are growing in a given area using an end-to-end (3+2)D convolutional neural network (CNN) for semantic segmentation, the method also has the ambition to predict the period in which a given crop is actually growing in a given season.We develop a model based on Features Pyramid Networks (FPN), adapted to process the time series with 3D kernels of a small area associated with each input sample, and which is able to provide as output a segmentation map using 2D kernels. Furthermore, we investigate and propose a solution to understand how CNN identify the time intervals that contribute to the determination of the output class-Class Activation Interval (CAI). This solution allows us to interpret the reasoning made by CNN in the classification of a single pixel. We demonstrate in a variety of experiments that our network is able to identify discriminatory time intervals in the input features domain despite the CNN has been trained only to solve a classification task (i.e., spatial solution). Therefore, with our CAI method we are able to provide information on \"when\" the class associated with a pixel is present in the time series of Earth Observation (EO) data. CAI method becomes useful to discriminate when crops are the only one cultivated (e.g., summer maize) or represent the second crop of the season (e.g., winter wheat followed by maize). In the latter case, maize is generally sown later in the season following the grain harvest and soil preparation. The ability to provide such information will help characterize crop systems (single or double crops) alongside the class of simple crops. Thanks to CAI, the information on the sowing period will provide an idea of the cultivated variety and the destination of crops (e.g., corn for silage or forage). Moreover, the proposed approach has a two-fold importance: demonstrate the capacity of the network to correctly interpret the physical process investigated and provide additional information to the end-user (i.e., crop presence and its temporal dynamics. The provision of CAI output is a way to assess robustness of model for each semantic class because provide an explicit representation of the time period in which crop is likely to be cultivated, such information for a specific study area can be confirmed by expert knowledge or provide added value information for final user. Figure 1 provides a graphical representation of CAI information for a spatial subset of the analysed S2 data where maize is cultivated.