## 3 Results
### Classification Results in the Anbandegi Region  
Figure 6 presents the variations in overall accuracy with respect to different patch sizes for each class purity value. As the class purity value increased, the corresponding classification accuracy increased, regardless of the patch size. In particular, when the patch size was small (e.g., 5 by 5 and 9 by 9), the difference in overall accuracy between CP60 and CP100 was larger than it was for large patch sizes (approximately 4.3%p and 3.8%p for patch sizes of 5 by 5 and 9 by 9, respectively). When both the class purity and patch size are small, the LCH within a patch is likely to be large. Consequently, the spatial features extracted from the trained model may fail to accurately reflect the homogeneous spectral patterns of most parcels in the study area, thereby yielding poor classification accuracy. In contrast, as the patch size increases, the training patches have higher LCH values and contain more homogeneous pixels, thereby improving classification accuracy. The classification results generated using a 9 by 9 patch, which achieved the highest classification for CP100 and also demonstrated a significant difference in the overall accuracy with respect to different class purity values, were further analyzed. The classification results generated using other patch sizes and different class purity values in Anbandegi are presented in Figure S1. Figure 7 is one of four classification results generated using a 9 by 9 training patch with the highest overall accuracy. The spatial distributions of classification results are locally different according to the change in class purity. In the case of CP60, the misclassification inside potato and fallow parcels was prominent, and the highland Kimchi cabbage near the parcel boundaries was misclassified as cabbage. In particular, the potato parcels located in the northwestern part were misclassified as highland Kimchi cabbage, and a furrow pattern appeared inside the potato parcels. This misclassification inside the parcels might be owing to the use of training patches selected near the parcel boundaries. By contrast, misclassification in the potato parcels reduced significantly as the class purity increased. Furthermore, the misclassification of fallow as highland Kimchi cabbage in the southeastern part was alleviated significantly for CP100. However, training patches were selected only inside the parcel for CP100. Hence, these patches might not accurately extract spatial features near the parcel boundaries. Consequently, the sporadic misclassification of highland Kimchi cabbage as fallow or cabbage was observed near the boundaries of highland Kimchi cabbage parcels. The two cabbage parcels in the western part could not be correctly identified, regardless of the class purity values. Figure 6: Variations in overall accuracy with respect to five different patch sizes for different class purity values in Anbandegi. This misclassification of cabbage as highland Kimchi cabbage and potato was due to the harvest of cabbage in August, unlike other cabbage parcels in the study area. Table 5 lists the accuracy statistics of one classification result using the 9 by 9 patch shown in Figure 7 that yielded the highest classification accuracy. The accuracy statistics of the classification results using other patch sizes and different class purity values in Abandegi are also listed in Table S1. The significant difference in the producer's accuracy for potato and fallow with respect to the class purity values appeared to result in the difference in the overall accuracy. For CP100, the producer's accuracy values of fallow and potato improved significantly. Improvements in accuracy values of fallow and potato for CP100 over CP60 were approximately 24.1%p and 6.5%p, respectively. However, because the potato and fallow parcels occupied only 30% of the study area, the overall accuracy did not increase substantially.