# 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-2 Data  
The classification results of Anji tea plantations based on multi-temporal Sentinel-2 imagery are shown in Figure 14, with corresponding classification accuracy presented in Table 4. A comparison between Tables 3 and 4 reveals that the tea plantation mapping accuracy based on optical imagery is significantly higher than that based on SAR imagery. The cross-validation shows that the three deep learning models have similar high accuracies (e.g., SE-UNet: 0.976 OA, 0.935 F1, 0.932 precision, 0.938 recall), improving by 3.17-10.35% over SAR-based tea plantation mapping.  
Using randomly sampled points covering the entire research area for accuracy verification, the SWIN-UNet model achieves the highest accuracy, with an overall accuracy of 0.993, an F1-score of 0.977, a precision of 0.989, and a recall of 0.964. Compared to the tea plantation mapping based on SAR data, these accuracy metrics have improved by 4.30%, 18.57%, 7.03%, and 29.57%, respectively. This high level of accuracy indicates a significant improvement in the ability to identify tea plantations using optical data over SAR data. The substantial increase in the recall suggests that optical data can significantly reduce the omission of tea plantations. This improvement can also be observed in Figure 14d, where, compared to Figure 13d, the use of multi-temporal optical imagery and the SWIN-UNet model effectively identifies tea plantations in complex backgrounds, reducing the omission of tea plantations.  
Additionally, we selected three typical regions to illustrate the differences in accuracy among different classification models. In Figure 15, Region 1 represents a typical tea plantation in Anji County, characterized by large tea plantation areas and a simple background, making it relatively easy to identify. All three deep learning models perform well in identifying tea plantations in this region. In Region 2, however, the UNet and SE-UNet models exhibit significant omission of tea plantations, whereas the SWIN-UNet model successfully identifies nearly all tea plantations, with its recall value improved by 6.17% compared to the other two models. This demonstrates that the SWIN-UNet model effectively extracts features for tea plantation identification, reducing the probability of omission. In Region 3, it can be observed that all three models have the issue of mistakenly identifying artificially planted landscape seedlings with features somewhat similar to tea gardens as tea gardens. However, looking at the precision metrics of the classification results from the three models, all of them exhibit very high precision rates (all above 0.98), indicating that the probability of non-tea gardens being identified as tea gardens is very low.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Evaluation Method** & **OA** & **F1-Score** & **Precision** & **Recall** \\ \hline \multirow{3}{*}{UNet} & Cross-validation & 0.976 & 0.934 & 0.933 & 0.935 \\ \cline{2-6}  & Point-based validation & 0.985 & 0.947 & 0.989 & 0.908 \\ \hline \multirow{3}{*}{SE-UNet} & Cross-validation & 0.976 & 0.935 & 0.932 & 0.938 \\ \cline{2-6}  & Point-based & 0.984 & 0.944 & 0.983 & 0.908 \\ \hline \multirow{3}{*}{Swin-UNet} & Cross-validation & 0.974 & 0.929 & 0.957 & 0.903 \\ \cline{2-6}  & Point-based & 0.993 & 0.977 & 0.989 & 0.964 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The accuracy of tea mapping based on various deep learning models and time-series Sentinel-2 imagery.