## 3 Results and Discussion
### Experiment 4: Appropriate Image Acquisition Times and Temporal Resolution  
One of the challenges in using multi-temporal data is to provide early information about the cultivation area and crop types for decision-makers with a few images. Hence, at first, only different sequential times of path 162 were selected to assess which image acquisition times were suitable for early classification. Then classification was done on case 1 using different combinations of acquisition time sequences. The summary of using eight combinations of image acquisition times that obtain acceptable results are given in Table 8.  
The best combination, with regards to wheat and barley separation in terms of early detection and high accuracy, as well as optimum images, is the use of T1 (planting period of wheat and barley), T6 (highest greenness of wheat and barley), and T7 (barley tends to become yellowish but wheat is still green) images (Table 8). However, if only the parameter \"minimum number of images\", regardless of the producer accuracy of the barley, is considered, combination 7 represents the best results. In order to show the effect of the temporal resolution on the accuracy since, after 16 March, the image without cloud on path 163 was not available, it is proper to examine the effect of increasing the temporal resolution to the extent of this date because the impact of the use of the May images will be high due to the closeness to the high crop greenness and will affect the temporal resolution effects.  
Some of the pervious researches used MODIS time-series for temporal optimization of image acquisition [26]. In this research to demonstrate the effect of increasing the temporal resolution of the LDCM to one week, the images of path 163 (i.e., dates 03-FEB-15 and 07-MAR-15) are incorporated into the experiment. Therefore, to compare the effects of the temporal resolution, combinations 6 and 10 were compared. As shown in Table 8, if the T1, T3, and T5 images (i.e., Comb. 6) are used, the overall accuracy and kappa coefficients will be 86.8% and 71.5%, respectively. If the Landsat  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Forest Size** & **10** & **50** & **100** & **200** & **300** & **400** & **500** & **600** & **700** & **800** & **900** & **1000** \\ \hline
**Processing time (s)** & 88.5 & 165 & 268 & 477 & 688 & 907 & 1117 & 1329 & 1551 & 1758 & 1963 & 2176 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Computation time for each implementation of the RF classifier with different forest sizes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Missing Data Retrieval** & **OA** & **Kappa** & **Producer’s Accuracy** & **Producer’s Accuracy** & **Producer’s Accuracy** \\  & & & **Wheat** & **Barley** & **Others** \\ \hline
**No** & 93.2 & 85.3 & 91.5 & 51.1 & 97.7 \\
**Yes** & 95.4 & 90.3 & 96.5 & 62.6 & 98.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The effect of missing data on the overall accuracy in the RF classifier.  
temporal resolution is reduced to one week, then the T2 and T4 images can be used and, according to combination 10, better results are obtained in less time. In other words, if the time resolution of Landsat's images is increased to one week, it can have better results. Of course, it is necessary to consider that by increasing the temporal resolution of the images to a week, especially when image acquisition times are near the peak of greenness of the crop, the probability of availability and obtaining images without cloud will be higher.