## 2 Study Sites and Data Sources
### Data Sources
#### 2.2.2 Historical Crop Type Mapping  
Since the ground data collection work has not been carried out in the early stages of crops, which made a great challenge to early crop identification. Previous studies have shown that historical crop-type mapping contains a large amount of a priori knowledge. There has  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Serial Number** & **Sentinel Image Number** & **Acquisition Date** & **Band Name** & **Cloud Coverage** & **Width-Height** \\ \hline
1 & 20200428T023549\_20200428T02414\_T51 UXP & 28 April 2020 & & & & & \\
2 & 20200508T023549\_20200508T023546\_T51 UXP & 8 May 2020 & & & & & \\
3 & 20200513T023551\_20200513T023553\_T51 UXP & 13 May 2020 & & & & & \\
4 & 20200528T023549\_20200528T023954\_T51 UXP & 28 May 2020 & & & & & \\
5 & 20200612T023601\_2000612T023555\_T51 UXP & 12 June 2020 & & & & & \\
6 & 20200707T023549\_20200707T023550\_T51 UXP & 7 July 2020 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Image data was used in this study.  
Figure 1: The geographical location of the region.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Month** & **Apri** & **May** & **June** & **July** & **August** & **September** & **October** \\ \hline
**Ten Days** & **2** & **3** & **1** & **2** & **3** & **1** & **2** & **3** & **1** & **2** & **3** & **1** & **2** & **3** & **1** \\ \hline Rice & Sowing & Emergence/Hooding & Transplanting. & Recurving & filtering & Booting & Hooding & Milky/Mature & Harvest \\ \hline Corn & & Sowing & Emergence & & & & & & & & & \\ \hline \multirow{2}{*}{Soybeans} & \multirow{2}{*}{Sowing} & \multirow{2}{*}{Emergence} & Three & & & & & & & & & \\  & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Phenological periods of main crops in the studied region.  
been a consensus that bulk crops have little difference in crop planting structure over a short period of time [31; 32; 33]. So, we combined this information with remote sensing images to generate training samples to guide early crop identification in the year to be classified. The historical crop-type mapping was derived from the results of Zhao [35]. It included corn, rice, and soybean, with an overall identification accuracy of 93.12% and a Kappa coefficient of 0.90, which meets the requirements of our study. The process of automatically generating samples based on historical crop-type mapping is: (1) Based on the historical crop-type mapping in 2019, and remote sensing imagery in 2020, candidate images of three crops were obtained by segmentation, respectively. (2) K-means clustering was applied to the candidate image areas to obtain clustering results. The number of clusters was 12. (3) We calculated the area of these sub-clustering results and selected the largest class as the \"pure sample\" area for each crop type. (4) The pure sample area was raster vectorized, and training sample points for each crop were randomly generated in the top \(N\) (the number of training samples of each crop type) sub-vector areas with the largest area. Through the above steps, 823, 815, and 618 samples for corn, soybean, and rice were obtained.