## 3 Results
### Classification Results in the Illinois Region  
The overall accuracy in the Illinois region decreased as class purity values increased (Figure 8), in contrast to the result of the Anbandegi region. When the patch size was 15 by 15, the overall accuracy decreased by 13.7%p for CP100, compared with that for CP60. This was due to the relatively heterogeneous distributions of crops in the study area, as quantified by the GCH and CV values in Figure 5. When the smallest patch size of 5 by 5 was used, the differences in overall accuracy for different class purity values were lower than those for other patch sizes, but the lowest classification accuracy was still yielded for CP100. Comparing the classification accuracy with respect to the training patch size, the overall accuracy decreased as the training patch size increased, regardless of the class purity values. The lowest accuracy was obtained when a large patch size was used for CP100. As shown in Figure 5, both the GCH and CV values of the Illinois region were smaller than those of Anbandegi, indicating the relatively heterogeneous distributions of crops in the study area and larger variations of class homogeneity in the patch unit. As the patch size increased, many pixels belonging to different classes were contained in the training patch. This increased class heterogeneity resulted in significant misclassifications. Figure 9 presents the classification results generated using the 9 by 9 training patch that produced a significant difference in the overall accuracy with respect to different class purity values. The classification results generated using other patch sizes and different class purity values in Illinois are presented in Figure S2. The distinctive differences between the classification results for different class purity values can be highlighted in two subareas, denoted as A and B in Figure 9. For CP 100, the exaggeration of soybean parcels and the misclassification of small corn parcels as soybean were observed. This occurred because the training patches selected primarily inside the crop parcels could not provide information regarding the discrimination of different crop types located at the boundary of the parcels. By contrast, the misclassification of corn parcels surrounded by the soybean parcels reduced significantly when using training patches with CP60. When a lower class purity value was applied to select the training patches, those located at the boundary between different crop parcels were primarily selected. Consequently, the 2D-CNN model trained using these training patches  
Figure 8: Variations in overall accuracy with respect to three different patch sizes for different class purity values in Illinois. extracted spatial features that were useful to discriminate adjacent crops, thereby achieving increased classification accuracy. However, misclassified pixels were identified inside the crop parcels in subarea A of Figure 9, as observed for CP60 in Anbandegi, which is a limitation of using small training patches. Table 6 summarizes the accuracy statistics of one classification result generated using a 9 by 9 patch shown in Figure 9. The accuracy statistics of the classification results using other patch sizes and different class purity values in Illinois are also listed in Table S2. As indicated in Figure 8, the overall accuracy for CP60 was higher than that for CP100. This improvement in overall accuracy was attributed to the significant increase of approximately 7.3%p in the producer's accuracy of corn, which is one of the major crops in the Illinois region. In the case of CP100, the user's accuracy of soybean decreased significantly by 4.4%p owing to the misclassification of corn as soybean, as shown in Figure 9.  
The Illinois region exhibited lower GCH and higher CV values than the Anbandegi region, as shown in Figure 5. This implies the heterogeneous distributions of crop parcels and the large variations in class homogeneity within a patch across the study area.