# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
###### Abstract  
Wheat plays an important role in China's and the world's food supply, and it is closely related to economy, culture and life. The spatial distribution of wheat is of great significance to the rational planning of wheat cultivation areas and the improvement of wheat yield and quality. The current rapid development of remote sensing technology has greatly improved the efficiency of traditional agricultural surveys. The extraction of crop planting structure based on remote sensing images and technology is a popular topic in many researches. In response to the shortcomings of traditional methods, this research proposed a method based on the fusion of the pixel-based and object-oriented methods to map the spatial distribution of winter wheat. This method was experimented and achieved good results within Shandong Province. The resulting spatial distribution map of winter wheat has an overall accuracy of 92.2% with a kappa coefficient of 0.84. The comparison with the actual situation shows that the accuracy of the actual recognition of winter wheat is higher and better than the traditional pixel-based classification method. On this basis, the spatial pattern of winter wheat in Shandong was analyzed, and it was found that the topographic undulations had a great influence on the spatial distribution of wheat. This study vividly demonstrates the advantages and possibilities of combining pixel-based and object-oriented approaches through experiments, and also provides a reference for the next related research. Moreover, the winter wheat map of Shandong produced in this research is important for yield assessment, crop planting structure adjustment and the rational use of land resources. winter wheat; Sentinel-2; fusion method; Google Earth Engine; Shandong Province 2024 14 3940 3
Remote sensing data are widely used in research related to wheat and other crop identification due to its wide coverage, fast update time and good spatial and temporal continuity [7; 9; 10]. The traditional extraction of information on cropland or crops is mainly based on the statistical method of field survey, which requires a large amount of human and material resources [11]. In contrast, agricultural information extraction based on remote sensing technology can achieve near real-time results and can be implemented on a large scale, which greatly improves efficiency and reduces costs [12]. Most of the current remote sensing-based wheat identification methods are implemented from the single pixel-based or object-oriented perspective [13; 14]. Both methods have their own advantages, but crop extraction results based on a single perspective often have more obvious defects. For example, although pixel-based methods are more efficient, the classification results inevitably show \"salt-and-pepper\" noise [15; 16], which requires post-processing, and it is not easy to smooth the results while maintaining quality. Conversely, object-oriented methods can obtain relatively smooth classification results, and better accuracy has been obtained in many previous studies [16; 17]. However, it is difficult to obtain high accuracy results for crops with similar spectra, especially for some areas with a complex surface environment, such as lack-classification [18]. With the respective characteristics of the two methods, this research proposed a fusion method that combines the advantages of pixel-based and object-oriented classification methods to obtain better classification accuracy. The fusion strategy has been mentioned in some previous studies [19; 20], but in related studies, the final result was to constrain the pixel-based classification with the image segmentation objects in terms of majority voting [18; 21]. In this paper, finer constraint rules were used to optimize the classification results while preserving the fine features obtained from pixel-based classification.