## 4 Results
### Corn Map of Hebei Province  
The Hebei Province corn map that integrated the pixel-based results generated with SAR, optical images in GEE, and object-based segmentation is shown in Figure 10. Spatially, corn was concentrated in southeastern Hebei Province, and characterized by a plains landform, which was sparse in the northern and western mountain areas (Figure 1). The detailed map exhibited two representative areas in Xingtai and Baoding. Corn was generally mixed with cotton in Xingtai (Figure 10c) and with sweet potato and forest in Baoding (Figure 10b). Based on the field points that were not involved in model training, 870 field points of other crops and 911 points of corn were employed to validate the corn map. The confusion matrix and accuracy are listed in Table 1. The corn category had a PA of 89.68% and UA of 90.48%, while the F1-Score reached 89.70% and the overall accuracy achieved 89.89% throughout Hebei Province. The results indicate that corn can be accurately identified in a large area with optical and SAR images using the GEE platform. The PA, UA, and F1-Score for all of the prefecture-level cities are listed in Table 2. The F1-Score of corn ranged from 82.93% in Shijiazhuang to 95.11% in Cangzhou, while the other crop category varied from 78.05% in Baoding to 93.20% in Handan. Figure 9: Median of the VV (Single co-polarization, vertical transmit/vertical receive) and VH (Dual-band cross-polarization, vertical transmit/horizontal receive) bands for monthly composites from all of the training points. Each line was filled with a 40–60% percentile deviation for each class. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Crop**} & \multicolumn{2}{c}{**Producer’s Accuracy**} & \multicolumn{1}{c}{**User’s Accuracy**} & \multicolumn{1}{c}{**F1-Score**} \\ \hline \multirow{3}{*}{Baoding} & Other Crops & 82.05\% & 74.42\% & 78.05\% \\  & Corn & 92.57\% & 95.14\% & 93.84\% \\ \multirow{3}{*}{Cangzhou} & Other Crops & 87.72\% & 92.59\% & 90.09\% \\  & Corn & 96.40\% & 93.86\% & 95.11\% \\ \multirow{3}{*}{Chengde} & Other Crops & 86.67\% & 90.28\% & 88.44\% \\  & Corn & 86.27\% & 81.48\% & 83.81\% \\ \multirow{3}{*}{Handan} & Other Crops & 92.31\% & 94.12\% & 93.20\% \\  & Corn & 88.10\% & 84.73\% & 86.38\% \\ \multirow{3}{*}{Hengshui} & Other Crops & 85.81\% & 86.93\% & 86.36\% \\  & Corn & 88.57\% & 87.57\% & 88.07\% \\ \multirow{3}{*}{Langfang} & Other Crops & 97.92\% & 92.16\% & 94.95\% \\  & Corn & 91.49\% & 97.73\% & 94.51\% \\ \multirow{3}{*}{Qinhuangdao} & Other Crops & 80.36\% & 90.00\% & 84.91\% \\  & Corn & 90.20\% & 80.70\% & 85.19\% \\ \multirow{3}{*}{Shijiazhuang} & Other Crops & 84.85\% & 93.33\% & 88.89\% \\  & Corn & 89.47\% & 77.27\% & 82.93\% \\ \multirow{3}{*}{Tangshan} & Other Crops & 90.91\% & 90.91\% & 90.91\% \\  & Corn & 90.00\% & 90.00\% & 90.00\% \\ \multirow{3}{*}{Xingtai} & Other Crops & 95.38\% & 83.22\% & 88.89\% \\  & Corn & 79.51\% & 94.17\% & 86.22\% \\ \multirow{3}{*}{Zhangjiakou} & Other Crops & 90.00\% & 96.43\% & 93.10\% \\  & Corn & 93.75\% & 83.33\% & 88.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Corn map accuracy in each prefecture-level city validated by the testing point. Figure 10: Corn map of Hebei Province, which integrates the pixel-based result generated with synthetic aperture radar (SAR), optical images in Google Earth Engine (GEE) and the object-based segmentation (**a**). The right column represents the detailed maps of Baoding (**b**) and Xingtai (**c**), and the specific location is labeled in (**a**) with two red rectangles.