# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
## 4 Discussion  
### Key Points and Strengths of the Fusion Method  
The integration method proposed in this study is mainly focused on pixel-based classification, image segmentation, and the organic combination of these two. Image segmentation is in fact the aggregation of neighboring homogeneous pixels into a computing  
Figure 6: Finer comparison of winter wheat extraction in different maps, from (**a**–**d**): the Sentinel-2 images, ChinaWheat30 in 2020, ChinaWheat10 in 2020, winter wheat map of this research.  
unit to reduce the \"pretzel effect\" in the pixel-based classification process [63]. Then, the aggregation effect of this computational unit will directly affect the results of object-oriented classification, so the reasonableness of the image segmentation parameter settings is particularly important [60]. In this study, the setting of image segmentation parameters is optimized and adjusted in repeated experiments. As can be seen in Figure 7b, the image segmentation results under the control of this parameter combination well segmented most of the boundaries of the cultivated land. Whether it is flat farmland or irregular cropland parcels in the hills, it can be effectively separated from villages, woodlands and major roads, etc., which also provided a good basis for the next fusion method.  
In Figure 7a, the ultra-high resolution RGB image from the MAPWORLD of Shandong Province is specifically employed to show the real surface situation more clearly. Figure 7b is the Sentinel-2 image during the peak growing season of winter wheat, and the image object layer is segmented based on it. Figure 7c is the pixel-based classification result. Figure 7d is the final winter wheat recognition under the implementation of the fusion method. From the accuracy assessment, the overall accuracy of the pixel-based and object-oriented classification results was 89.5% and 90.6%, while the final winter wheat map obtained by the fusion method is 92.2%, which was improved by 2.7% and 1.6%. Although the improvement is not very significant from the data, the fusion method is very advantageous by observing the actual classification results. From Figure 7c, it is obvious that the pixel-based classification result has a large number of fragmented pixels, most of which are misclassified. Meanwhile, more regular and accurate plots of cultivation were formed  
Figure 7: Ultra-high-resolution image from MAPWORLD of Shandong in April 2020 (**a1,a2,a3,a4**), image object layer generated based on Sentinel-2 by SNIC (**b1,b2,b3,b4**), the pixel-based classification (**c1,c2,c3,c4**) and final result produced by the integration method (**d1,d2,d3,d4**).  
under the constraints of image segmentation objects. It has greatly reduced the pretzel effect produced by pixel-based classification and optimized the classification results. Especially in the hilly areas of south-central Shandong, the advantages of this method are more significant. In addition, the comparison showed that the winter wheat acreage derived from the fusion method was closer to the official statistical yearbook data, with a difference of only 3%, while the overestimation of the pixel-based method was 12%. This also indicates in another way that the fusion method gives more accurate results.