## 4 Conclusions  
In this study, multi-temporal images were used to differentiate between wheat, barley, and other classes using the RF classifier. The goal was to acquire a few images in the early stages of the growing season rather than images from throughout the growing season. In the study area, 10 images from an overlapping area between paths 162 and 163 captured over the course of six months were examined. The experimental results demonstrated that 30% of the data and 50 trees were the optimum training sample size and forest size, respectively, for the study area. The missing data caused by cloudy pixels in multi-temporal images were retrieved using the average of the k-nearest cloudless pixels. The results showed that the OA increased by 2.2% after retrieval of the missing data. When the missing data from the cloudy pixels are retrieved and classified by RF, the certainty of obtaining labels for these pixels was less than the certainty of the labels for the cloudless pixels. To determine the appropriate image acquisition times with which to identify the classes, different image acquisition times were generated and evaluated. Superior results were achieved when six images were used, but acceptable accuracy was achieved early in the season when only the first three images taken were utilized. To demonstrate the effect of increasing the temporal resolution, images from path 163 were incorporated into the experiment. The experimental results showed that if the temporal resolution of Landsat 8 was increased to one week, the classification task could be conducted earlier with slightly better OA and kappa values. The VIs, TGVI, and TGSB were generated as informative features to differentiate between wheat and barley. The effect of incorporating these features was assessed with the aim of decreasing the period of the time series and improving the accuracy. The results show that these new features improved the OA by 3.1% and kappa value by 6.6% on average. The results showed that TGVI and TGSB play a major role in the separation of wheat from barley by using only three images. According to the analysis of the importance variable in the RF classifier, and also the accuracy of the results of the classification, the most important images were three images of 15 January, 17 April, and 3 May. Also, results showed that STVI3, the subsequent EVI and MSAVI are the most important VIs. Based on the knowledge gained from this research, to test this area for the rest of the years, it is enough to only use the most important ones. The results also showed that if wheat and barley are considered as a single class versus other classes, with the use of images associated with paths 162 and 163, both crops can be classified in March (at the beginning of the growth stage) with an overall accuracy of 97.1% and a kappa coefficient of 93.5%. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**1** & \(\surd\) & - & \(\surd\) & - & \(\surd\) & - & - & - & - & - & \(\surd\) & 95.9 & 90.7 & 92.1 & 97.8 \\
**2** & \(\surd\) & - & - & - & - & \(\surd\) & \(\surd\) & - & - & - & \(\surd\) & 99.6 & 99.0 & 99.0 & 99.8 \\
**3** & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & - & - & - & - & - & - & \(\surd\) & 97.1 & 93.5 & 94.9 & 98.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The overall accuracy (OA), kappa, and producers’ accuracy (PA) when wheat and barley are merged into one class by the RF classifier. The results of this study are promising for the use of images with high temporal resolution, like Sentinel-2A and Sentinel-2B, for accurate discrimination in the early stages of the growing season. Although the use of a constellation of satellites with high spatial and temporal resolution increases the accuracy, a balance between factors, such as ground sampling size, processing time, accuracy, and early season crop classification should certainly be considered. All the authors listed contributed equally to the work presented in this paper. This research received no external funding.