# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 5 Conclusions  
This study utilized multi-source temporal satellite data, including optical and SAR satellites, and various deep learning methods based on semantic segmentation to accurately identify tea plantations. The research successfully produced a high-precision tea plantation distribution map with a resolution of 10 m for the study area. The main conclusions are as follows:  
(1) Classification Capability of Tea Plantations Using Different Satellite Data  
This study examined the classification capabilities of tea plantations using temporal optical satellite imagery, temporal SAR imagery, and the fusion of optical and SAR satellite imagery. The results show that all three data types have certain capabilities for tea plantation classification. The classification accuracy of tea plantations based on temporal SAR imagery was relatively low, with an overall accuracy and F1-score of 95.2% and 0.824, respectively. In contrast, tea plantation mapping based on optical imagery or the fusion of optical and SAR data achieved high accuracy, with both methods showing similar performance, achieving overall accuracy and F1-scores of 99.3% and 0.977, respectively. Despite the relatively lower accuracy of SAR data, its cloud-penetrating and all-weather imaging capabilities make it indispensable for tea plantation mapping tasks in regions or years lacking clear-sky imagery. (2) Advantages of Deep Learning Methods for Tea Plantation Identification  
The complex remote sensing identification features of tea plantations give deep learning methods certain advantages in tea plantation identification. This study compared three deep learning methods based on semantic segmentation for tea plantation mapping, all of which achieved high classification accuracy. Among the three methods, the SE-Unet and Swin-UNET models, which incorporate attention mechanisms, demonstrated higher accuracy, while the conventional UNET model had relatively lower accuracy. (3) Impact of Topographical Distribution on Tea Plantation Classification Accuracy  
Supported by high-precision tea plantation mapping results, this study evaluated the distribution of tea plantations at different altitudes, slopes, and aspects and assessed the impact of various topographical conditions on classification accuracy. The results indicate that tea plantations in Anji are primarily distributed in hilly and mountainous areas, mostly below 300 m in elevation. Tea plantations are distributed across slopes ranging from 5\({}^{\circ}\) to 30\({}^{\circ}\), with the proportion of tea plantations decreasing as slope increases. North-facing slopes have more tea plantation area compared to south-facing slopes. In terms of accuracy, higher altitudes achieve higher classification accuracy, north-facing slopes have higher classification accuracy than south-facing slopes, and classification accuracy improves with increasing slope. The findings of this study provide technical support for high-precision tea plantation mapping based on deep learning and highlight the significant impact of topographical factors on mapping accuracy. Targeted improvements in classification accuracy under different topographical conditions will be a key focus for future research. Conceptualization, L.Y. ([PERSON]), Y.M. and X.L. ; methodology, Y.Z., L.Y. ([PERSON]), X.L., Y.M. and J.D. ; software, Y.Z. and Z.L. ; validation, L.Y. ([PERSON]), X.L., Y.M., L.Y. ([PERSON]), Z.L. and X.Z. ; formal analysis, Y.Z., X.Z. and Z.L. ; investigation, Y.Z. and L.Y. ([PERSON]); resources, L.Y. ([PERSON]) and L.Y. ([PERSON]); data curation, Y.Z. and J.D. ; writing--original draft preparation, Y.Z. ; writing--review and editing, L.Y. ([PERSON]);visualization, L.Y. ([PERSON]); supervision, L.Y. ([PERSON]); project administration, L.Y. ([PERSON]); funding acquisition, L.Y. ([PERSON]), X.Z. and L.Y. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, Grant No. 42201412, No. 42371385, and No.