# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 4 Results and Discussion
### The Influence of Terrain Factors on the Accuracy of Tea Plantation Mapping  
Mapping tea plantations using remote sensing is more challenging than identifying traditional crops due to their irregular plot shapes and significant size variations. Addi  
Figure 18: Relationship between distribution of Anji tea gardens and altitudes, aspects, and slopes. (**a**) Percentage of tea gardens distributed at different altitudes; (**b**) percentage of tea gardens distributed at different aspects; (**c**) percentage of tea gardens distributed at different slopes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Evaluation**} & \multirow{2}{*}{**OA**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} \\ \cline{3-3} \cline{5-6}  & & & & & \\ \hline \multirow{2}{*}{UNet} & Cross-validation & 0.976 & 0.935 & 0.933 & 0.937 \\ \cline{2-6}  & Point-based & 0.982 & 0.938 & 0.989 & 0.892 \\ \hline \multirow{2}{*}{SE-UNet} & Cross-validation & 0.976 & 0.934 & 0.931 & 0.938 \\ \cline{2-6}  & Point-based & 0.985 & 0.950 & 0.978 & 0.923 \\ \hline \multirow{2}{*}{Swin-UNet} & Cross-validation & 0.974 & 0.929 & 0.957 & 0.903 \\ \cline{2-6}  & Point-based & 0.984 & 0.944 & 0.978 & 0.913 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The accuracy of tea mapping based on various deep learning models and time-series Sentinel-1 and Sentinel-2 imagery.  
tionally, tea plantations are primarily located in hilly areas. The undulating terrain causes variations in remote sensing characteristics due to differences in elevation, aspect, and slope, potentially affecting the accuracy of tea plantation classification. By statistically analyzing the classification accuracy of tea plantations at different elevations, slopes, and aspects (Figure 19), we can understand how these terrain factors influence classification accuracy and provide guidance for further improving the accuracy of tea plantation mapping.  
Figure 19a-c shows that the classification accuracy of tea plantations located at elevations above 100 m is significantly higher than that of those below 100 m. This improvement is mainly reflected in the precision, indicating a lower probability of non-tea plantation areas being misclassified as tea plantations at higher elevations. This is primarily because areas below 100 m have more complex land cover types with more classes easily confused with tea plantations, resulting in lower classification accuracy. Conversely, areas above 100 m have relatively simpler land cover, leading to higher tea plantation recognition accuracy.  
From the aspect perspective, we find that the classification accuracy of tea plantations on northern slopes is significantly higher than those on southern slopes (Figure 19d-f),  
Figure 19: Tea plantation accuracy under different terrain conditions. (**a–c**) show the impact of elevation on the F1-score, precision, and recall of tea plantation classification, respectively. (**d–f**) depict the effect of aspect on the F1-score, precision, and recall of tea plantation classification. (**g–i**) illustrate the influence of slope on the F1-score, precision, and recall of tea plantation classification.  
with this improvement mainly reflected in the recall rate (Figure 19f). This indicates that tea plantations on northern slopes are less likely to be misclassified as other land cover types. Regarding slope, the trend is more pronounced: as the slope increases, the classification accuracy of tea plantations continuously improves, with both precision and recall rates gradually increasing (Figure 19g-i).  
These results demonstrate that terrain factors significantly impact the accuracy of tea plantation mapping. Addressing the challenge of improving the recognition accuracy of tea plantations in low-elevation, low-slope, and southern slope areas will be the focus of future research.