# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
###### Abstract
Radar data will be used to determine the structural development of the wheat plant, and it will show a significant decrease in VV during the vertical increase stage of the plant stem. Useful information is used to examine crops that exist in SAR backscatter amplitudes [10] to achieve classification, especially for rice and forest mapping. The merging of data from optical and radar references, as well as the development of software' ability to perform classification methods, is what makes SAR data so important in integrated land classification [11]. [PERSON] et al. [12] reported successful results by integrating optical and SAR images to provide annual crop inventories. [PERSON] et al. [13] applied radar and optical imagery in cloudy areas of Mexico to provide acceptable accuracy for land use classification. Figure 1: Example of profiles of (upper panel) Sentinel 2 normalized difference vegetation index (NDVI) and (lower panel) Sigma\({}^{0}\) VV and VH backscatter intensities for a winter wheat field. [PERSON] et al. [14] shared the use of high-resolution optical image and SAR time series; using Landsat 8 and Sentinel 1 combined data to improve early detection of crop type, they proposed the integration of Sentinel 2 images for initial crop identification. The results of a recent study using Sentinel 1 and Sentinel 2 data to assess groundwater and identify irrigation crops in southern India, that mainly use Sentinel 1 data, showed that when used in the monsoon season, they have a good ability to identify a variety of irrigated crops [15]. [PERSON] et al. [6] used real-time close-up images of the Sentinel 1, Sentinel 2, and Landsat 8 by combining intermediate-resolution ground observations to map seasonal crop types in the United States. [PERSON] et al. [16] in a study of 112 different land use areas to investigate the integration of optical and radar data concluded that optical and radar data as complementary data are also effective in determining the details of land use map with high accuracy. Aiming to evaluate different methods of integrating optical and multipolar radar data for land mapping in Brazil, [PERSON] et al. [17] concluded that radar information improves user accuracy, while the polarization data of HH (horizontal transmission and reception) more than horizontal polarization (HV) (horizontal transmission and vertical reception) leads to the differentiation of different land use classes, but the integration of radar and optical data had the best statistical results for land mapping. [PERSON] et al. [18] used SAR images, optical images and the integration of both data types to evaluate the possibility of winter wheat mapping. The classification map was performed using a combination of Sentinel 1 information and optical images using a random forest method. The best results (F1 = 98%) were obtained by combining SAR and optical images for winter wheat mapping. [PERSON] et al. [19] used a multitemporal algorithm to combine Sentinel 2 and Landsat 8 data. Their results showed that there is a high consistency between ground estimates and measurements, and a high correlation and accuracy ((RMSE < 0.83, RMSEm < 23.6% and RMSEm < 16.6%)) as a result of the performance of Sentinel 2 and Landsat 8 images were reported. As mentioned above, many studies examined the performance of the optical and radar image combination method to identify the type of crop. So far, these studies were limited to the following:  
1. Combine Sentinel 1 and Sentinel 2 time series data;
2. Classification of the majority of strategic national crops;
3. Provide crop classification data with acceptable accuracy. The aim of this research is to scrutinize the deficiency and strength of the combined radar data and optical images to identify the type of crops.