## 1 Introduction  
Significant increases in populations around the globe, increase demand in agricultural productivity, and, thus, precise land cover and crop classification and spatial distribution of various crops are becoming significant for governments, policymakers, and farmers to improve decision-making processes to manage agricultural practices and needs [1]. Crop maps are produced relatively at large scale, ranging from global [2], countrywide [3], and local level [4, 5]. The growing need for agriculture in the management of sustainable natural resources becomes essential for the development of effective cropland mapping and monitoring [6]. Group on Earth Observations (GEO), with its Integrated Global Observing Strategy (IGOS), also emphases on an operational system for monitoring global land covers and mapping spatial distribution of crops by using remote sensing imagery. Spatial information of the crop maps has been the main source for crop growth monitoring [7; 8; 9], water resources management [10], and decision making for policy makers to ensure food security [11]. Satellite and Geographic Information System (GIS) data have been an important source factor in establishing and improving the current systems that are responsible for developing and maintaining land cover and agricultural maps [12]. Freely available satellite data offers one of the most applied sources for mapping agricultural land and assessing important indices that describe conditions of crop fields [13]. Recently launched sentinel-2 is equipped with a multispectral imager that can provide up to 10 m per pixel spatial resolution with the revisit time of 5 days, which offers a great opportunity to be exploited in the remote sensing domain. Multispectral time series data acquired from MODIS and LANDSAT have been widely used in many agricultural applications such as crop yield prediction [14], landcover and crop classification [15], leaf area index estimation [16], plant height estimation [17], vegetation variability assessment [18], and many more. Two different data sources can also be used together to extract more features that lead to improving results. For example, Landsat-8 and sentinel-1 used together for LC&CC [19]. There are some supervised or unsupervised algorithms for mapping cropland using mono or multi-temporal images [20; 21]. Multi-temporal images have already proven to gain better performance than mono temporal mapping methods [22]. The imagery used for only key phenological stages s proved to be sufficient for crop area estimation [23; 24]. It has also found in [25], that reducing time-series length affects the average accuracy of the classifier. Crop patterns were established using the Enhanced Vegetation Index derived from 250 meters MODIS-Terra time series data and used to classify some major crops like corn, cotton, and soybean in Brazil [26]. Centimetric resolution imagery is available at the cost of the high price of commercial satellite imagery or with the extensive UAV flight campaigns to cover large area during the whole crop cycle to get better spatial and temporal details. However, most of the studies used moderate spatial resolution (10-30 m) freely available satellite imagery for land cover mapping due to their high spectral and temporal resolution which is difficult in the case of UAV and high-resolution satellite imagery. Other than multispectral time series data, several vegetation indices (VIs) derived from different spectral bands have been exploited and used to enrich the feature space for vegetation assessment and monitoring [27; 28]. VIs such as the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation indexes (EVI), textural features, such as grey level co-occurrence matrix (GLCM), statistical features, such as mean, standard deviation, inertial moment are the features more frequently used for crop classification. It is possible to increase the accuracy of the algorithms also using ancillary data such as elevation, census data, road density, or coverage.