# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
## 4 Discussion
One of the study's most important findings is that the difference in the OA variable for the classification process at the start of the growing season using two sensors, Sentinel 1 and Sentinel 2, revealed that this index (OA) was higher in studies using Sentinel 1. The difference between the use of Sentinel 1 and Sentinel 2 sensors will be determined after 30 days of the growing season, so that the data as predictor variables from these two sensors will be significantly different. The characteristics of the input data can be attributed to the greater validity of radar data for the crop classification process. The values of the OA variable were 35 percent lower when the analyses were done solely with VH backscatter, which was even lower than when the analyses were done with Sentinel 2 NDVI. On the basis of this evidence, it is impossible to say with certainty that the radar results differ from the optical classification of the first months of the growing season. One of the issues with classifications that rely solely on optical methods is the use of a predictor variable, which is frequently the NDVI predictor. Previous research [31] focused on this factor, but it was later determined that NDVI could not provide a complete view of Sentinel 2 optical images, so it is recommended that more bands be used to achieve a more comprehensive view of optical images [32]. The method described in Section 2.5 does not apply to the NDVI index or single spectral bands. Although the use of different optical indices is recommended for future research, the focus of this study is on the use of a valid vegetation index (10-m resolution NDVI). The use of radar data, such as interferometry coherence stacks [33], can improve the validity of the classification process. Future research should consider whether classification based on radar data is more accurate than classification based on optical data. In the growing season, this case can answer the research hypothesis that radar data is more efficient than cloud position. This study found that two periods are more important for classification when using the NDVI predictor variable. Winter crops grew the most longitudinally in May and April, and it was during this time that summer crops began to grow, making it possible to classify bare soil and other plants with summer crops. Due to the clear sky and lack of clouds during this time, optical images can be obtained in greater detail. August and July are the two most important months for making more use of sunlight. The winter grain harvest season and greater access to summer crops in the summer are two other factors that aid in the sorting process. According to [PERSON] et al. [18], the VV index takes precedence over the HV index when classifying crops, but we found no difference in the classification process between the two variables in this study. NDVI is the most important predictor when considering the characteristics of Sentinel 1 and Sentinel 2, as well as their ability to estimate forecasts. The most important crop classification predictions are made using a combination of radar and optical features. The accuracy of classification in areas close to the center is greater than the accuracy of classification at pixel borders when it comes to crop classification at the pixel level. The data are all qualitative, indicating a close relationship between classification accuracy and classification reliability, but statistical probability cannot be used to estimate the percentage of reliability, and statistics was ineffective in this study when it came to crop classification. The results of this study showed that using Sentinel 2 optical data as a supplement to Sentinel 1 SAR data provided comprehensive information on plant structure [34]. Furthermore, by using time-series images rather than single images, the problem of determining the date of the images is eliminated [35]. High-accuracy results were reported in several studies that used a combined method of optical and radar images.