## 3 Results
### Extraction Accuracy  
First, this study successfully extracted winter vegetation using remote sensing imagery, while all other vegetation types were categorized as \"Other\". The results in Table 3 demonstrate high accuracy for winter vegetation classification models across all regions \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Region** & \multicolumn{2}{c}{**Winter Crops**} & \multicolumn{2}{c}{**Other**} & \multicolumn{2}{c}{**Ground Truth (Pixels)**} & \multicolumn{2}{c}{**Overall**} \\ \hline
**Results** & **User’s** & **Producer’s** & **User’s** & **Producer’s** & **Winter Crops** & **Other** & **Overall Accuracy** & **Kappa** \\ \hline Jinxiang & 99.95 & 99.86 & 99.37 & 99.80 & 3804 & 2979 & 97.85 & 0.961 \\ Laiwu & 98.27 & 97.90 & 97.13 & 92.12 & 1335 & 1332 & 96.06 & 0.921 \\ Lanling & 97.26 & 96.24 & 97.31 & 95.72 & 5361 & 4104 & 98.43 & 0.964 \\ Pizhou & 98.67 & 98.21 & 99.95 & 91.26 & 6487 & 6221 & 94.90 & 0.903 \\ Qixian & 97.62 & 99.28 & 96.26 & 96.12 & 3016 & 3612 & 94.50 & 0.893 \\ Zhongmou & 97.23 & 92.03 & 96.52 & 96.89 & 3648 & 3263 & 92.82 & 0.878 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of accuracy validation for winter vegetation map. Figure 6: Winter crop distribution maps. This figure illustrates the winter vegetation classification results for each county within the study area. Green indicates areas of winter vegetation, while gray represents other land cover types. (all above 92% overall). The producer and user accuracies for winter crops were over 96% (except for Zhongmu County, which had a producer accuracy of 92%), providing a reliable foundation for further garlic classification. Secondly, by classifying the WCM, we generated the GDM for each region. The classification models employed different combinations of classification features, and the resulting GDM classification accuracy is depicted in Tables 4 and 5. The results indicate that all combinations of classification features yielded satisfactory classification results (overall accuracy consistently above 84%). Within each district, multiple feature combinations demonstrated the potential for achieving high classification accuracy. Among the various counties in the study area, Zhongmu and Laning counties attained higher accuracy levels, followed by Jinxiang and Laiwu counties, while Pizhou and Qi counties exhibited more average classification outcomes. Based on Table 4, the Kappa of each classification feature across different regions exhibit a similar trend to the overall accuracy change, with all achieving optimal results in Laning County and lower accuracy in Pizhou and Qixian counties. In most cases, as the number of features within a combination of classification features decreases, both classification accuracy and Kappa coefficients decrease (except in Laning County), with decreases of less than 10%. Among the various feature combinations, the inclusion of DITS in the feature combination offers some advantages over the NDVI, a benefit reflected in most areas (except Pizhou). However, when replacing the NDVI with the EVI in the feature combinations, this advantage becomes negligible, as both the feature combinations with DITS added and E-SM obtained the best results in their respective regions, showing no significant advantage. Nevertheless, this does not discount the utility of DITS, as the feature combination with DITS added was able to achieve better producer accuracy versus user accuracy in extracting garlic in most regions.