## 2 Materials
### Winter Wheat Planting Area Dataset  
The identification of the winter wheat area was important and necessary for the model development. The extraction accuracy of the winter wheat directly affected the accuracy of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Year** & **Location** & \multicolumn{2}{c}{**Measurement**} \\ \cline{4-4}  & & & **Content** & **Date** \\ \hline \multirow{6}{*}{exp1} & \multirow{6}{*}{2011} & & & 28 March 2011 \\  & & & & 7 April 2011 \\  & & & Soil moisture, & 14 April 2011 \\  & & & leaf water content, & 19 April 2011 \\  & & & spectral reflectance, & 24 April 2011 \\  & & & chlorophyll content, and & 7 May 2011 \\  & & & total nitrogen content. & 12 May 2011 \\  & & & & 19 May 2011 \\  & & & & 24 May 2011 \\ \hline \multirow{2}{*}{exp2} & \multirow{2}{*}{2017} & & Soil moisture, & 29 March 2017 \\  & & & spectral reflectance, & 5 May 2017 \\ \cline{1-1}  & & chlorophyll content, and & 31 May 2017 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Field experimental design in experiment 1 (exp1) and experiment (exp2). yield estimation [42]. For accurately extracting the winter wheat area in Hengshui City, the study used specific phenological characteristics of the winter wheat and Sentinel-2 time-series data with a high spatial resolution of 10 m.  
It was well-known that different crops possessed different phenological characteristics in the same region. In Hengshui City, winter wheat was usually sown in October of the former year, turned green in March as the only crop growing in the field, and was then harvested in June when other crops were all green. The area of wheat, evergreen forest, and grassland was extracted based on the higher NDVI compared with the low-threshold (A) at the tillering stage. The planting area of the winter wheat was then determined based on the lower NDVI compared with the up-threshold (B) at the harvest, when the NDVI of the forest and grassland maintained higher values. On the basis of the phenological difference between the winter wheat and the other crops or vegetation in this area, we built a dual threshold method for winter wheat pixel extraction, Equation (1). The classification principle was as follows:  
\[\text{Pixel}= \begin{cases}1&\text{NDVI}_{1}>\text{A\&NDVI}_{2}<\text{B}\\ 0&\text{otherwise}\end{cases}  \tag{1}\]  
where \(\text{NDVI}_{1}\) is the NDVI value at the tillering stage, \(\text{NDVI}_{2}\) is the NDVI value at harvest, A is the low-threshold at the tillering stage and set to 0.6, and B is the up-threshold at harvest and is set to 0.3. Using Sentinel-2 data that were atmospherically corrected, four images from the same day were mosaicked together to cover the whole study area, and the NDVI was calculated. Using the Sentinel-2 NDVI data, the dual threshold classification method was processed to produce a winter wheat map for Hengshui City at the 10 m resolution. The output produced a winter wheat map for Hengshui City with spatial resolution of 10 m in 2017. Using the winter wheat distribution map from the Worldview2 image, the extraction accuracy of the winter wheat from the Sentinel-2 images was verified. An error matrix for the extraction results was created and is shown in Table 2. We randomly took a total 200 points of the winter wheat field and non-wheat field in the winter wheat map from the Sentinel-2 images. Every point in the winter wheat map from the Sentinel-2 images was the size of 10 m \(\times\) 10 m and 5 \(\times\) 5 pixels, which corresponded to the winter wheat map from the Worldview2 image. If the proportion of the winter wheat area in the 5 \(\times\) 5 pixels was higher than 0.6 in the Worldview2 image, the corresponding pixel in the winter wheat map from the Sentinel-2 images was a point of the winter wheat field. As shown in Table 2, the overall accuracy of the classification was 93.5%.