# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
###### Abstract  
Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat mapping and investigating potential classification improvement by using SAR (Synthetic Aperture Radar) images, optical images, and the integration of both types of data in urban agricultural regions with complex planting structures in Southern China. Both SAR (Sentinel-1A) and optical (Landsat-8) data were acquired, and classification using different combinations of Sentinel-1A-derived information and optical images was performed using a support vector machine (SVM) and a random forest (RF) method. The interference coherence and texture images were obtained and used to assess the effect of adding them to the backscatter intensity images on the classification accuracy. The results showed that the use of four Sentinel-1A images acquired before the jointing period of winter wheat can provide satisfactory winter wheat classification accuracy, with an F1 measure of 87.89%. The combination of SAR and optical images for winter wheat mapping achieved the best F1 measure--up to 98.06%. The SVM was superior to RF in terms of the overall accuracy and the kappa coefficient, and was faster than RF, while the RF classifier was slightly better than SVM in terms of the F1 measure. In addition, the classification accuracy can be effectively improved by adding the texture and coherence images to the backscatter intensity data. winter wheat classification; Sentinel-1A; multi-temporal; Landsat-8; urban agricultural region +
Footnote †: journal: Sensors
Although the classification of SAR images is more difficult than that of optical images [10], several studies have used SAR data for crop monitoring and mapping [11; 12; 13]. [PERSON] et al. [14] used multi-temporal RADARSAT-1 data to identify rice in the Zhaoqing area of Guangdong Province, China, with a classification accuracy of 91%. [PERSON] et al. [15] compared the use of VV polarization, HV polarization, and HH polarization for crop classification; the results showed that the classification accuracy of HH polarization was better than VV polarization and HV polarization. [PERSON] et al. [16] used C-band SAR data to classify wheat, maize, soybean, and other crops, and found that multi-polarization SAR data classification accuracy was higher than that of single-polarization SAR data. [PERSON] et al. [17] also suggested that increasing the polarization mode can increase the classification accuracy. Several researchers have also found that, compared with single-frequency SAR data, multi-frequency SAR data can effectively improve the classification accuracy [18; 19; 20]. It has also been demonstrated that the addition of texture and coherence features can improve classification accuracy compared to backscatter intensity images alone in crop classification [20; 21; 22; 23]. [PERSON] et al. [20] reported that the use of texture features can improve the classification accuracy of SAR data. Similar results were reported by [PERSON] and [PERSON] [23], who used a gray-level co-occurrence matrix (GLCM) to extract SAR texture features for crop classification and found that texture features were useful for crop classification. In the work of [PERSON] et al. [21], the classification accuracy was improved by adding coherence information to the backscatter intensity data compared to the use of backscatter intensity data alone. [PERSON] and [PERSON]'s [24] research showed that when the texture and coherence information were added, the overall accuracy was improved. Therefore, this study evaluated the classification accuracy of winter wheat using texture and coherence features. Due to the influence of SAR image speckle effects, resulting in inter-class confusion [25], many researchers have studied the combination of optical and SAR images to improve classification accuracy [26; 27].