## 3 Study Area and Data  
The study site near Carpi, Emilia-Romagna, situated in the center-north part of Italy with central coordinates 44\({}^{\circ}\)47\({}^{\prime\prime}\)01\({}^{\prime\prime}\) N, 10\({}^{\circ}\)59\({}^{\prime}\)37\({}^{\prime\prime}\) E was considered for LC& CC shown in Figure 1. The Emilia-Romagna region is one of the most fertile plains of Italy. An area almost 2640 km\({}^{2}\) was considered, which covers diverse cropland. The major crop fields in this region are maize, lucerne, barley, wheat, and vineyards. The yearly averaged temperature and precipitation are 14 \({}^{\circ}\)C and 843 mm for this region. Most of the farmers practice single cropping in this area.  
To know about the spatial distribution of crops, we deeply studied Land Use Cover Area frame statistical Survey (LUCAS) and extracted all the information we need for ground truth data. LUCAS was carried out by Eurostat to be able to monitor agriculture, climate change, biodiversity, forest, and water for almost all over Europe [67].  
The technical reference document of LUCAS-2015 was used to prepare the ground truth data. Microdata that contains spatial information of crops and several land cover types along with the geo-coordinates for the considered region was imported in Quantum Geographic information system (QGIS) software, an Open-source software used for visualization, editing, analysis of geographical data. The selection of pixel was made manually by overlapping images and LUCAS data, so a proper amount of ground truth pixels were extracted for training and testing the algorithm. Several examples of ground truth fields parcels are illustrated in Figure 2. The sentinel-2 mission consists of twin polar-orbiting satellites launched by European Space Agency (ESA) in 2015 and can be used in various application areas such as land cover change detection, natural disaster monitoring, forest monitoring, and most importantly in agricultural monitoring and management [68].  
It is equipped with multi-spectral optical sensors that capture 13 bands of different wavelengths. We used only high-resolution bands that have 10 m/pixel resolution shown in Table 1. It also has a high revisit time of ten days at the equator and five days with twin satellites (Sentinel-2A, Sentinel-2B). It became more popular in remote sensing community due to fact that it possesses various key features such as, free access to data products available at ESA Sentinel Scientific Data Hub with reasonable spatial resolution (which is 10 m for Red, Green, Blue, and Near Infrared bands), high revisit time and reasonable spectral resolution among other available free data sources. In our study, we used ten multitemporal sentinel-2 images reported in Table 2, which are well co-registered from July-2015 to July-2016 with close to zero cloud coverage. The initial image selection was performed based on the cloudy pixel contribution at the granule level. This pre-screening was followed by further visual inspection of scenes and resulted in a multi-temporal layer stack of ten images. Sentinel Application Platform (SNAP) v5.0 along with sen2 core v 2.5.1 were used to apply radiometric and geometric  
Figure 1: The study site is located in Carpi, region Emilia-Romagna is shown with the geo-coordinates (WGS84). RGB image composite derived from sentinel-2 imagery acquired in August-2015 is shown and the yellow marker showing geo-locations of ground truth land cover extracted from the Land Use and Coverage Area frame Survey (LUCAS-2015).  
Figure 2: Several examples of zoomed in parts of crop classes considered as ground truth. Shape files are used to extract pixels for reference data.  
corrections to acquire Bottom of Atmosphere (BOA) Level 2A images from Top of Atmosphere (TOA) Level 1C. Further details about geometric, radiometric correction algorithms used in sen2 cor can be found in [69]. Bands with 10 meters/pixel along with the derived Normalized Difference Vegetation Index (NDVI) were used for experiments, as shown in Table 1.