# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
## 2 Materials and Methods
### Methodology
#### 2.3.2 Pixel-Based and Object-Oriented Fusion Methods  
In this research, winter wheat was identified based on temporal-spectral information. Firstly, 10 bands of 9 monthly Sentinel-2 images, which are B2, B3, B4, B8, B5, B6, B7, B8A, B11 and B12, were selected to construct the temporal-spectral feature set. In addition, the following vegetation indices which are sensitive to crop identification in previous related experiments were added [29; 48; 49]: the Normalized Difference Vegetation Index (NDVI), the Land Surface Water Index (LSWI), the Soil-Adjusted Vegetation Index (SAVI) and the Normalized Differential Phenology Index (NDPI), and detailed information is shown in the Table 1.  
Many previous studies have shown that the random forest is one of the most important and efficient supervised classification methods [54; 55], so this algorithm was also used in this study to extract winter wheat. The random forest is a robust voting decision algorithm by integrating multiple independent decision trees trained on samples to obtain predictions [43; 56]. It is well integrated on the GEE platform now, and is available through 'ee.Classifier.SmileRandomForest'. In the GEE, a total of six parameters were set for this algorithm, which are \"numberOfrees\", \"variablesPerSplit\", \"minLeafPopulation\", \"bagFraction\", \"maxNodes\" and \"seed\". In general, the number of decision trees in the random forest can be decided through multiple tests to achieve higher accuracy while ensuring classification efficiency [54]. In this research, the optimal number of decision \begin{table}
\begin{tabular}{c c c} \hline \hline
**Indictors** & **Expressions** & **References** \\ \hline NDVI & NDVI = \(\frac{\rho_{NIR}-\rho_{R}}{\rho_{NIR}+\rho_{R}}\) & [50] \\ LSWI & LSWI = \(\frac{\rho_{NIR}-\rho_{NIR2}}{\rho_{NIR}+\rho_{NIR2}}\) & [51] \\ SAVI & SAVI = \(\frac{(\rho_{NIR}-\rho_{R})}{(\rho_{NIR}+\rho_{R}+0.5)}\times(1+0.5)\) & [52] \\ NDPI & NDPI = \(\frac{\rho_{NIR}-(0.74\cdot\rho_{R}+0.26\cdot\rho_{NIR})}{\rho_{NIR}+(0.74 \cdot\rho_{R}+0.26\cdot\rho_{NIR})}\) & [53] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The selected spectral indices in this research and their expressions. trees was selected by constructing a tree determining function, setting the range and split interval to repeat the operation, cycling the operation and outputting the accuracy change in the random forest classification. The other parameters in the classifier are generally set as default values. The identification of winter wheat in this study was based on 9 views of high-quality monthly imagery obtained from the data pre-processing in the previous section. Overall, 10 spectral bands and 4 important vegetation indices were collected in the research, resulting in a feature set of 126 spectral-temporal features. This feature set was input into the random forest classifier. After repeated experiments, the number of trees in the random forest was set to 200, and finally resulted in the initial winter wheat spatial distribution of Shandong. The object-oriented classification method is a popular classification method for many researchers nowadays, and it is also widely used in crop information extraction [55; 57]. The object-oriented approach is based on the image objects clustered by homogeneous pixels for classification, which can eliminate the \"salt and pepper\" effects by reducing the fragmentation of classification units [58]. Moreover, it can make full use of the spectral, spatial and textural information of the images, and improve the classification accuracy. Considering the respective advantages of pixel-based and object-oriented approaches, this research proposed to integrate pixel-based classification with object-oriented image segmentation in order to achieve a complementary effect. Specifically, this research generated image objects through the image segmentation algorithm provided by the GEE, and optimized the pixel-based classification results by the image objects.