# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
###### Abstract
The classification accuracy of maize, soybean, and sunflower increased by 6.2%, 16.2%, and 25.9%, respectively, when multi-temporal SAR images were added to optical images [26]. [PERSON] et al. [28] used the RADARSAT-2 and EO-1 to classify Ukrainian crops, and found that the combination of SAR and optical images gave better accuracy. In this paper, the combination of SAR and optical images for improving crop classification accuracy was also evaluated. The selection of classification algorithms directly affects the classification results. Support vector machine (SVM) [29], random forest (RF) [30], and neural network (NN) [31] are the most commonly-used classifiers for SAR data classification. [PERSON] et al. [32] used SAR and optical images to classify land cover and found that the RF algorithm was better than SVM when classifying various land covers using pixel-based image analysis. Other studies have also demonstrated the effectiveness of SVM for SAR data classification [3; 33]. However, few reported studies have involved crop classification at the early stages of agriculture; particularly there is a lack of use of multi-temporal SAR data for such studies. The use of remote sensing data at the end of the season, or later, to obtain crop acreages is not useful for supporting in-season crop management [34], such as yield forecasting and irrigation management [11]. Some studies have used the combination of SAR and optical images for crop classification. [PERSON] et al. [9] studied the contribution of SAR images to crop classification when used as a supplement to high-resolution optical images. [PERSON] et al. [34] combined multi-temporal SAR and optical data for in-season crop mapping. However, they used the combination of SAR and optical images to classify and did not investigate the ability of SAR data alone for its use in in-season crop mapping. The above studies demonstrated that SAR data produced good classification accuracy. However, SAR data have rarely been studied in the classification of winter wheat in urban agriculture regions with complex planting structures and fragmental planting parcels, which are more difficult to classify than those with simple planting structures or less fragmented parcels. Particularly, there is a lack of such studies using multi-temporal Sentinel-1A images for in-season winter wheat mapping. The purpose of this paper was to explore the feasibility of in-season winter wheat mapping and to improve classification accuracy by using SAR data, optical images, and the combination of both types of images to classify winter wheat in an urban agricultural region with a complex planting structure in SouthernChina. For this objective, interference coherence, texture, and backscatter intensity images were first extracted from SAR data during winter wheat growth. Then, the satisfactory classification results were successfully identified based on these Sentinel-1A-derived information and optical images, and using the RF and SVM classifiers. The results of this study are important not only in the urban agriculture region, but also in other areas of simple planting structures. This study is particularly important for in-season winter wheat mapping of SAR data and compares the effects of the addition of coherence and texture images on winter wheat classification.