# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
## 4 Discussion  
If we want to correctly classify crops in a region, we need to concentrate on the uniformity of all available inputs. If the data is related to Sentinel 1 data, 12-day return mosaics can be created. It is crucial to be cautious when it comes to reducing the impact of angles on the output data. We need to use a filter to get high-quality and high-resolution radar images. The Lee filter is one of the most comprehensive filters for this situation. The sharpness of radar images is reduced due to blurry effects. Twelve-day return mosaics and time series can be used to compensate for this flaw, and this annoying effect can be easily removed or reduced using this technique. However, because the classification was not the main criterion in terms of time, this disorder will not cause a primary problem, according to the method used in this study. [PERSON] et al. [30] used a special time filter in their study that could be considered a new method, but using their method was not a priority in this study. For Sentinel 2 data, this method was used to smooth out all cloud effects in NDVI  
Figure 7: Classification confidence defined as random forest predicted class probability of majority class for each pixel at end of August 2020.  
images, and 10-day NDVI mosaics were considered without cloud and fog coverage. When all predicting classes in one step lead to a 1.5 percent increase in the OA index in Tarom for mapping crops using random forest hierarchical and time-series model inputs, the results showed that the method of two-step hierarchical use of the method is nonhierarchical. The first classification step was completed in August, with an OA index of around 84 percent due to differences in radar and optical effects for three different crops (forest, construction, water). The random forest method can be used in the second stage to identify more specialized differences between classes. During rejection, significant increases in both kappa and OA were reported, though by the end of May, summer crops (potatoes and corn) had grown significantly, but winter crops had also grown significantly. Increased classification accuracy will result from increased awareness of crop phenological development stages. Previous research has demonstrated that the use of optical time series improves the principles of classification by allowing for the separation of crops [31]. Separation of crops, such as cereals and vegetable crops, as well as winter and summer crops, is strongly recommended to improve the quality of classification work, despite the difficulties that such separation poses due to the similarity of crops such as winter wheat and winter barley. To solve the problem, it is suggested that winter crops be classified alongside summer crops. It is difficult to separate grasslands from winter crops (cereals) and winter crops from summer crops. Making a classification error between grassland and winter crops can be explained by the fact that both of these crops grow well in the months of April and May, and the green mass is visible on both crops, making it difficult to distinguish between them. Furthermore, vegetable crops such as potatoes and corn are very similar to one another until the end of plant growth, making it difficult to separate them by the end of April in practice. This can be thought of as a drawback to using remote sensing for such purposes. Given the high values of the two kappa and OA indices during the study period (growth season), the items obtained as a result become more citational over time, but the results cannot be obtained in a specific time frame. As a result, it is important to remember that we will have to wait a certain amount of time to get the desired and required results from the crops for classification, because the variables in this study are crops that require time, and a set of observations takes time to evolve.