# 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 and Sentinel-2 Data  
The mapping results of the Anji tea plantation based on SAR and optical image fusion are shown in Figure 16, and the classification accuracy is presented in Table 5. It can be observed that the accuracy of tea plantation mapping combining optical and SAR data is close to that of tea plantation mapping based solely on optical images. This indicates that optical images play the most significant role in tea plantation mapping when integrating optical and SAR images. Optical data have the advantages of strong ground object recognition ability and high signal-to-noise ratio. However, when using optical images for regional-scale crop mapping, the optical images in some areas may be affected by clouds and fog, resulting in a decrease in the accuracy of tea garden identification. SAR data have the ability to penetrate clouds and rain, allowing for all-weather imaging. It is particularly effective at identifying certain land cover types, such as water bodies and built-up areas. Additionally, it has a strong capability for recognizing tea plantations that are densely distributed with a uniform background. However, SAR data are also limited by a lower signal-to-noise ratio and fewer spectral bands, which means its classification capability is generally weaker than that of optical imagery. By combining optical and SAR images, it is possible to leverage the strong tea plantation recognition capabilities of optical images while further utilizing the high temporal resolution features provided by time-series SAR images to mitigate potential spectral confusion (same object exhibiting different spectra) and spectral variation (different objects exhibiting similar spectra) issues that may occur during the classification based on optical imagery. Although the combined SAR and optical image approach did not significantly improve the accuracy compared to using solely optical images for tea plantation mapping, it was still found to enhance remote sensing mapping capabilities in certain areas. As shown in Figure 17, due to cloud and fog affecting  
Figure 15: Magnified comparative illustration of tea garden classification results based on various deep learning methods and time-series Sentinel-2 imagery. The blue areas in the figure indicate regions where tea gardens were erroneously omitted. The yellow areas indicate regions where non-tea garden pixels were misclassified as tea garden pixels.  
the Sentinel-2 images (Figure 17b), significant tea plantation omission was observed in the tea plantation mapping based solely on optical images (Figure 17d). However, mapping based solely on time-series Sentinel-1 images benefited from SAR's ability to penetrate clouds and successfully identified the tea plantation area, with good agreement between the detected boundaries and actual tea plantation boundaries (Figure 17c). Through the fusion of optical and SAR data, significant improvement in the tea plantation omission issue of this area was observed (Figure 17e). Additionally, the recall rate of the SE-UNET model also indicates that the recall rate of the classification results using the fusion of optical and SAR data increased from 0.908 to 0.923 compared to not incorporating SAR data, indicating a significant improvement in tea plantation omission.  
Figure 16: Tea classification results based on Sentinel-1 + Sentinel-2 dataset and deep learning models.  
Figure 17: Tea plantation mapping results in cloud-affected areas based on different image combinations. (**a**) UAV image, (**b**) Sentinel-2 image, (**c**) Sentinel-1 derived tea plantation map, (**d**) Sentinel-2 derived tea plantation map and (**e**) tea plantation mapping result based on combined Sentinel-1 and Sentinel-2 data.