## 2 Automatic Crop Type Mapping Using Spectral Indices  
The Normalized Difference Vegetation Index (NDVI) is widely used for crop type detection. For example, [PERSON] et al., (2020) [15] evaluated the mapping of winter croplands and corresponding cropping patterns by utilizing NDVI and a spectral matching technique (SMT) for 16 multi-temporal Sentinel-2 data values and unsupervised classification. They achieved Kappa coefficients between 0.64 and 0.74 for these three various districts. Similarly, [PERSON] et al., (2018) [14] used a progressive classification algorithm based on the phenological development of plants and the corresponding reflectance characteristics and NDVI. They processed 36 and 47 multitemporal satellite images for the year of 2015 and 2016 from four sensors (Landsat-7 and -8, Sentinel-2A, and RapidEye) and achieved overall accuracy (\(OA\)) of 89.49% and 77.19% for 2015 and 2016, respectively. The lower performance for the year 2016 was mostly associated with adverse weather conditions. In contrast, [PERSON] et al., (2020) [5] used eight multi-temporal Sentinel-2 data and five various spectral indices to analyze the performance of the temporal and spectral features used in machine-learning-based classification methods. Among the spectral indexes, they utilized NDVI as the well as the Perpendicular Moisture Index (PMI), Normalized Differential Senescent Vegetation Index (NDSVI), Normalized Difference Residue Index (NDRI), and Normalized Differential Village Index (NDTI). The resulting \(OA\) was between 95.92% and 97.85% and the Kappa value was between 0.93 and 0.96 for various machine learning methods. Additionally, [PERSON] et al., (2020) [17] used three vegetation indices that incorporated the NIR band (NDVI, GDVI: Green Normalized Difference Vegetation Index; NDRE: Normalized Difference Red Edge Index) derived from RapidEye images for crop type classification. Their goal was to investigate the potential use of these indices on crop type classification and the effect of each index on the accuracy of the Support Vector Machine classification accuracy. The use of three vegetation indices resulted in a classification accuracy of 87.46% and a Kappa coefficient of 0.85, which proved to be higher than the classification accuracy of any dual combination of these three vegetation indices. Additionally, single-band classification of each index provided \(OA\)s between 57.84% and 63.42% and Kappa coefficients between 0.47 and 0.58, which clearly shows that the utilization of many spectral indices increases the final accuracy. Furthermore, [PERSON] et al., (2020) [16] calculated 91 spectral indices from one cloudless Sentinel-2A MSI image and evaluated their performance in identifying each type of crop. For image classification, a stratified random sampling approach was applied to select the fields used for modeling (50%), hyperparameter tuning (25%), and evaluation (25%) of the classification to prevent overfitting. The study showed that the use of spectral indices improved the classification accuracy (\(OA\) = 93.0%), whereas the integrated use of spectral indices and reflectance improved the negative effects associated with large sets of correlated variables and decreased the accuracy (\(OA\) = 92.4%). It was also shown that classifications based on reflectance including Bands 2, 4, 11, and 12, and eight various spectral indices achieve an accuracy of 93.1%. This indicates, on the contrary to [17], that the utilization of abundant numbers of spectral bands does not necessarily increase the accuracy; using only 8 indexes achieved the same accuracy level as using 91 indexes. [PERSON] et al., (2020) [25] mapped five main crop types for large-scale mapping based on the decision tree and Sentinel-2 data.