# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 4 Results and Discussion  
### Tea Plantation Mapping Based on Multi-Temporal Sentinel-1 Data  
The results of mapping Anji tea plantations based on multi-temporal Sentinel-1 SAR images are shown in Figure 13, with classification accuracy detailed in Table 3. As indicated in Table 3, both validation methods demonstrate that tea plantation mapping based on multi-temporal SAR data achieves high accuracy, underscoring the effectiveness of time-series SAR data for this purpose. Figure 13 further reveals that Anji tea plantations are highly concentrated in the northeastern part of Anji, with only sparse distributions in other areas. This result aligns with the actual distribution of tea plantations in the region, further validating the accuracy of regional-scale tea plantation mapping using only time-series SAR data. Among the three tea plantation mapping methods, those incorporating attention mechanisms, specifically SE-UNet and SWIN-UNet, achieved the highest classification accuracy. The SE-UNet model attained the highest accuracy in cross-validation, with an overall accuracy of 0.946, an F1-score for tea plantations of 0.850, and both precision and recall of 0.850. This indicates that within the sample area, the proportions of misclassified  
Figure 13: Tea classification results based on time-series Sentinel-1 dataset. (**a**) Classification results based on the UNet model. (**b**) Classification results based on the SE-UNet model. (**c**) Classification results based on the SWIN-UNet model. (**d**) Local classification result of SWIN-UNet model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{1}{c}{**Evaluation Method**} & \multirow{2}{*}{**OA**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} \\ \cline{3-3} \cline{5-6}  & Cross-validation & 0.945 & 0.850 & 0.854 & 0.847 \\ \cline{2-6}  & Point-based & 0.937 & 0.754 & 0.906 & 0.646 \\ \hline \multirow{2}{*}{SE-UNet} & Cross-validation & 0.946 & 0.850 & 0.850 & 0.850 \\ \cline{2-6}  & Point-based & 0.944 & 0.778 & 0.955 & 0.656 \\ \hline \multirow{2}{*}{Swin-UNet} & Cross-validation & 0.939 & 0.838 & 0.799 & 0.881 \\ \cline{2-6}  & Point-based & 0.952 & 0.824 & 0.924 & 0.744 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy of tea mapping based on various deep learning models and multi-temporal Sentinel-1 imagery and DEM. and omitted tea plantations are relatively balanced, with approximately 15% of tea plantations misclassified as other land types and about 15% of pixels classified as tea plantations actually being non-tea plantation pixels. Additionally, Table 3 shows that when using randomly distributed sample points covering the entire Anji County for accuracy validation, the classification accuracy decreases, indicating a certain degree of overfitting in the tea classification models based solely on time-series SAR data. Among the three deep learning-based tea plantation mapping methods, Swin-UNet achieved significantly higher classification accuracy than the UNet and SE-UNet models. Its precision and recall for tea plantation reached 0.924 and 0.744, respectively, resulting in an F1-score of 0.824, representing improvements of 9.28% and 5.91% over the UNet and SE-UNet models, respectively. Furthermore, the precision of the tea plantation mapping results from all three methods exceeded 0.9, while recall remained around 0.7, indicating that the models exhibit a weaker generalization capability. Specifically, there are fewer instances of non-tea plantations being misclassified as tea plantations, but a more severe issue of tea plantations being misclassified as non-tea plantations. For example, Figure 14d shows a region where a large number of tea plantations were omitted. This phenomenon is primarily due to the presence of numerous roads, water bodies, and other land types within the tea plantations in this area.