## 1 Introduction
In another study the sensitivity of the RF classification to select training sample points, including sample size, spatial autocorrelation, and proportions of classes within the training sample were investigated [24]. The effect of the time series length on crop mapping is investigated. In that study, RF is used to calculate the important scores for VIs and the Jeffries-Matusita (JM) distance is used to measure class separability for each time series [27]. In a comprehensive study, five classifiers that can also be applied to high-resolution multi-temporal optical imagery to produce accurate crop type maps have been selected and benchmarked at the global scale [28]. These classifiers are RF, support vector machine (SVM) with a radial basis function kernel, Dempster-Shafer fusion of the previous approaches, best classifier with a mean-shift smoothing, and best classifier with a temporal regular resampling. The obtained classification results on the 12 test sites around the globe demonstrated that RF achieved superior results. The RF is also used for winter wheat mapping by multi-temporal images of LDCM and Gao Fen 1 satellites [29]. [PERSON] et al. [29] prepared the wheat map by combining multi-temporal and multi-source data using the random forest classifier. They used six images during the wheat growth stages and two NDVI and Soil Adjusted Vegetation Index (SAVI) for each image. The input features of the RF were 36 spectral bands and 12 vegetation indices in a total of 48 features. They classified two classes and separated the wheat from non-wheat. They performed classification with an overall accuracy of 92.9% and a kappa coefficient of 85.8%. However, they did not report in their study whether they considered barley in the wheat class or non-wheat class. [PERSON] et al. ([PERSON] et al., 2018) also classified two wheat and non-wheat classes by analyzing Landsat images in Pakistan's Punjab region with the supervised bagged classification tree approach and reached an overall accuracy of 76%. They did not also mention the barley class. [PERSON] et al. ([PERSON] et al., 2018) using Landsat time-series images and Synthesis Aperture Radar (SAR), conducting early crop type identification and considering two classes of wheat and barley in one class and, in the best case, using seven Sentinel-1 images and nine Landsat-8 images, the kappa coefficient \(\kappa\) = 0.73 was obtained. However, the VI information was not used in their study. [PERSON] ([PERSON], 2018) introduced vegetation indices as new features in the random forest for the classification of soybeans and weeds and confirmed the positive effect of vegetation indices, and the overall accuracy of 90.8% and the kappa coefficient of 0.878 were obtained using the image (30 June 2014). [PERSON] et al. ([PERSON] et al., 2014) classified various types of crops by using a support vector machine classifier using the NDVI time series data that was extracted from Landsat images. They considered the wheat and barley classes separately and reported an overall accuracy of 90% and a kappa coefficient of 85%. They also reported that the producer's accuracy of the wheat was 67% and the user's accuracy was 53%. [PERSON] et al. ([PERSON] et al., 2018) compared four types of support vector machine, artificial intelligence network, random forest, and maximum likelihood in order to classify various types of crops and defined two barley and wheat classes separately, and with the use of only spectral band information from three images, the producer's accuracy for wheat and barley of 79.7% and 70.8%, respectively, were obtained. [PERSON] et al. ([PERSON] et al., 2018) classified the crops of the United States using multi-source data by the supervised decision tree classifier and obtained an overall accuracy and kappa coefficient of 91.65 and 0.8618, respectively. They used only the spectral band of images and the producer' accuracy of the wheat and barley were reported to be, respectively, 90.72% and 15.73%.