## 5 Conclusions  
The proposed model represents an efficient way to produce crop maps by exploiting spatio-temporal information of S2 data. The proposed (3+2)D FPN performs very well when applied to Sentinel-2 data, over-performing the proposed state of the art solution. The work carried out opens the door to new studies in the field of the comprehensibility of deep learning algorithms in agricultural and environmental applications. We consider that the method described represents a step forward to understand the behavior of deep learning models in agricultural applications. The provision of CAI values at pixel level is a way to assess the robustness of network interpretation of the considered semantic  
Figure 8: Mean CAIs calculated for all patches of the Munich test set that contain at least 100 pixels labeled with the winter wheat class. The CAI values for each patch were aggregated using the mean. The X axis reports the integer values from 1 up to the number of images used in the time series and therefore is a sampling of the time period analyzed on each series (one year in this paper). The Y axis represents the activation of the output predicted class (CAI) at the date it saw in the input time series.  
class. If the temporal period (i.e., subset of image time series), considered as importance from the network, agree with known agro-practices (i.e., sowing period of cultivated crops) we can be confident of exportability of the method in other context. Furthermore, the proposed technique for interpreting the activation interval of a class in the time domain is very innovative and can be adapted to other domains that make use of fully convolutive deep models. Moreover, beside the innovative contribution in pattern recognition domain we consider that this encouraging results can be of extreme help for final user. In particular automatic detection of \"where and when\" crop are cultivated is a fundamental support territorial planner and policy makers. In particular in the European context, Common Agricultural Policy (CAP) is devoted to support farmers by providing subsidies according to cultivated crop and accomplishment of proper agro-practices. Paying Agencies, that have to decide about the farmers' declaration compliance, need automatic external information to support their checking activities (see the Sen4 Cap program, [http://esa-sen4 cap.org/](http://esa-sen4 cap.org/), accessed on 11 July 2021). The proposed segmentation model provides very interesting spatial (where is the crop) and temporal information (when it is cultivated) as a support for user interested in monitoring crop dynamics for a given geographical area. To further contribute to crop monitoring in view of sustainable agriculture and climate friendly practices, a potential next step of the study will be to test the possibility of providing indication on land status (what and for how long) before and after crop cultivation to identify soil management such as presence of cover crops and or residues that are indicator of agricultural conservation practices. In this way the model should also become multi-labeled, as well as multi-classed, for each pixel of the output segmentation map.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]; software, [PERSON], [PERSON] and [PERSON]; validation, formal analysis, investigation, writing, [PERSON], [PERSON], [PERSON] and [PERSON]; data curation, [PERSON]. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.