# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
## 3 Result  
The trend of changes in the two variables kappa and OA in the classification methods in this study is shown in the following table (Table 2). The table shows that as the number of images used as input values grows, so do the values of the two variables kappa and OA. Controlling the degree of resolution of the categories and the differences between the types of crops can be used to investigate this increase. The differences between Sentinel 1 and Sentinel 2 and optical classification versus SAR classification can be analyzed by examining the results. Sentinel 1 classification performed better than Sentinel 2 classification in March. Moreover, the optical-only classification performed better than the SAR-only classification throughout the growing season (kappa of 0.69 vs. 0.67, OA of 77 percent vs. 75 percent). According to these findings, different crops in early growth have different characteristics that can be detected and compared with the light spectrum, but this conclusion can only be applied to the crops studied in this study, such as winter cereals. Optical and radar signatures for crops grown in April and May, such as potatoes and corn, reveal the type of management method used and reflect the winter plant cover, which is difficult to distinguish between optical and radar studies. A combination of Sentinel 1 and Sentinel 2 images performs better than using a single sensor for classification. The maximum accuracy obtained in the last days of July was 81 percent, which could not be increased by combining with August images. Figure 5 depicts the final classification as of the last day of August 2020. For this purpose, no filtration was used, and the image's recognizability is the result of the classification packages in the crop area's landscape. Due to the similarity between alfalfa and potatoes at the start of the growing season, all crops were classified as potatoes at first, but with time and growth evolution, this error was eliminated in the last days of \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Sentinel 2**} & \multicolumn{6}{c}{**Sentinel 1**} \\ \hline \# & **March** & **April** & **May June** & **July** & **August** & **March** & **April** & **May June** & **July** & **August** & **OA** & \(\kappa\) \\ \hline  
& & & & X & X & X & X & X & & 0.83 & 0.80 \\
18 & X & X & X & X & X & X & X & X & X & X & X & X & X & 0.84 & 0.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall accuracy (OA) and kappa coefficient (k) of various categorization plans. Figure 6 shows the results, where certain within-field zones in the center field, which is part of the validation dataset, were incorrectly classified as alfalfa in the early mapping stage (taken here in June 2020) but were correctly identified as potato by the end of August. Figure 6 was specified to eliminate this ambiguity with two letters (a) and (b). At the beginning of June 2020, the whole crop was identified as alfalfa in the study area due to the phenological similarities between the two crops of alfalfa and potatoes and the lack of development of plant growth, but at the end of August, due to full plant growth and obvious phenological differences, it was possible to distinguish between the two crops (purple represents alfalfa, and green represents potatoes). Figure 7 depicts the classification result's dependability. Because the side pixels are due to the integration with the signals of the surrounding terrain, the data have low validity along the considered boundaries. Figure 7 shows the results for the high data invalidity at the pixel boundary, which is close to the central packet. Wheats were the crops grown in this package, but the data uncertainty can be seen in the central portion of the packages, which can be attributed to the inconsistency created in the pixels' background. Figure 5: Final categorization result based on Sentinel 1 and 2 inputs through August 2020.