###### Abstract  
Knowledge about crop type distribution is valuable information for effective management of agricultural productivity, food security estimation, and natural resources protection. Algorithms for automatic crop type detection have great potential to positively influence these aspects as well as speed up the process of crop type mapping in larger areas. In the presented study, we used 14 Sentinel-2 images to calculate 12 widely used spectral vegetation indices. Further, to evaluate the effect of reduced dimensionality on the accuracy of crop type mapping, we utilized principal component analysis (PCA). For this purpose, random forest (RF)-supervised classifications were tested for each index separately, as well as for the combinations of various indices and the four initial PCA components. Additionally, for each RF classification feature importance was assessed, which enabled identification of the most relevant period of the year for the differentiation of crop types. We used 34.6% of the ground truth field data to train the classifier and calculate various accuracy measures such as the overall accuracy (\(OA\)) or Kappa index. The study showed a high effectiveness of the Modified Chlorophyll Absorption in Reflectance Index (MCARI) (\(OA\) = 86%, Kappa = 0.81), Normalized Difference Index 45 (NDI45) (\(OA\) = 85%, Kappa = 0.81), and Weighted Difference Vegetation Index (WDVI) (\(OA\) = 85%, Kappa = 0.80) in crop type mapping. However, utilization of all of them together did not increase the classification accuracy (\(OA\) = 78%, Kappa = 0.72). Additionally, the application of the initial three components of PCA allowed us to achieve an \(OA\) of 78% and Kappa of 0.72, which was unfortunately lower than the single-index classification (e.g., based on only NDVI45). This shows that dimensionality reductions did not increase the classification accuracy. Moreover, feature importance from RF indicated that images captured from June and July are the most relevant for differentiating crop types. This shows that this period of the year is crucial to effectively differentiate crop types and should be undeniably used in crop type mapping. crop type mapping; spectral indices; principal component analysis; random forest; agriculture 20222022029 May 202217 May 202217 May 2022217  
data from the Copernicus Programme has opened new avenues for studying automatic approaches for remote and automatic crop type mapping. This automatic detection, which will provide quantitative and spatial information on the type of crop cultivated in specific regions, can help to increase agricultural productivity and to monitor food production to a great extent [2]. In the literature, numerous examples of remote sensing applications can be found for crop type detection, including optical satellite data [3; 4; 5], SAR data [6; 7], and data captured from unmanned aerial vehicles [8; 9]. Moreover, some authors have utilized integrated approaches based on SAR as well as optical satellite images [2; 10; 11] or even Google Street View photographs for crop type detection [12]. However, optical time series data (OTSD) have been proven to be efficient for crop mapping over many years, because the phenological evolution of each crop produces a unique temporal profile of reflectance and spectral indexes [13]. For crop type mapping, some researchers have utilized a specific spectral index calculated from multitemporal satellite data [14; 15], whereas others utilized many spectral indexes calculated from one image [16] or many spectral indices calculated from many multitemporal images [5; 17]. In the literature, there are also studies based on reflectance information without the calculation of any spectral indices [13]. For all of these various input data, different methods have been utilized, including machine learning and deep learning techniques, which have been widely applied recently [18; 19; 20].