# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
###### Abstract  
Crop identification is key to global food security. Due to the large scale of crop estimation, the science of remote sensing was able to do well in this field. The purpose of this study is to study the shortcomings and strengths of combined radar data and optical images to identify the type of crops in Tarom region (Iran). For this purpose, Sentinel 1 and Sentinel 2 images were used to create a map in the study area. The Sentinel 1 data came from Google Earth Engine's (GEE) Level-1 Ground Range Detected (GRD) Interferometric Wide Swath (IW) product. Sentinel 1 radar observations were projected onto a standard 10-m grid in GRD output. The Sen2 Cor method was used to mask for clouds and cloud shadows, and the Sentinel 2 Level-1C data was sourced from the Copernicus Open Access Hub. To estimate the purpose of classification, stochastic forest classification method was used to predict classification accuracy. Using seven types of crops, the classification map of the 2020 growth season in Tarom was prepared using 10-day Sentinel 2 smooth mosaic NDVI and 12-day Sentinel 1 back mosaic. Kappa coefficient of 0.75 and a maximum accuracy of 85% were reported in this study. To achieve maximum classification accuracy, it is recommended to use a combination of radar and optical data, as this combination increases the chances of examining the details compared to the single-sensor classification method and achieves more reliable information. Sentinel 1 and 2; Copernicus Sentinels; crop classification; food security; agricultural monitoring; remote sensing; data analysis; SAR; random forest +
Footnote †: journal: Medicalinto orbit on 23 June 2015 as part of the Copernicus Sentinels mission. Sentinel 2 is made up of two satellites, Sentinel 2A and Sentinel 2B, which are very similar to each other. Sentinel 2 has a revisit time of 5 days, rather than 10 days, because of these 2 satellites (2A and 2B). Short-infrared, near-infrared, and visible wavelengths are among the electromagnetic spectrum parties of 13 bands of Sentinel 2's multispectral device. Sentinel 2 was used in a variety of research fields for product classification due to its unique and advanced specifications [5]. However, the presence of clouds is one of the drawbacks of using optical sensors. Because light rays cannot penetrate the cloud, there is a gap in visual images due to the presence of clouds and cloud shadows, and this lack of influence is a significant problem in classification and monitoring of crops. The technique of combining multiple sensors is used to solve the problem of the cloud and its shadow; this technique can effectively use different parts of the electromagnetic spectrum [6]. The size of cloud particles, for example, is smaller than the wavelength of microwave radiation in the C band, allowing it to influence the cloud. With radar sensors, satellites emit energy and measure its reflection, allowing them to benefit from various parts of the electromagnetic spectrum due to these sensors. A synthetic aperture radar (SAR) [7] can be defined as a system that uses tool movement to achieve acceptable ground resolution. Despite the fact that SAR data from space is now widely available to the public, it used to necessitate special procedures [8]. Following the launch of the Sentinel 1 mission, SAR data became freely available for a limited time [9]. Sentinel 1A and Sentinel 1B satellites have a six-day recurring frequency. Because of the overlap and combination of ascending and descending orbits, this period is repeated every two days for Sentinel 1 in Europe. Because SAR images determine plant structure and moisture content, and visual images specify vegetation biophysical processes, the combination of optical and SAR images provides a source of supplementary data. Figure 1 shows the radar backscatter and NDVI (Normal Difference Vegetation Index) profiles from the Sentinel 1 and Sentinel 2 satellites.