# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 3 Methods
### Image Preprocessing  
The pre-processing of Sentinel-2 data included clipping, cloud masking, and vegetation index calculation. Firstly, the Sentinel-2 images were clipped to the extent of Anji County to reduce the processing time and to focus on the area of interest. Subsequently, the QA band provided by Sentinel-2 images was used to remove cloud and cloud shadow covered areas to obtain cloud-free images. On this basis, the normalized difference vegetation index (_NDVI_) and land surface water index (_LSWI_) were calculated. In addition, some studies have emphasized the importance of the red edge band in extracting specific vegetation tasks [32; 33], so we added the normalized vegetation red edge index 1 (_NDVI\({}_{re1}\)_) and normalized vegetation red edge index 2 (_NDVI\({}_{re2}\)_) and the transformed chlorophyll absorption ratio index (_TCARI_) [34; 35], which has a good linear relationship with the chlorophyll content of the plant canopy. The calculation formulas of these five index features are defined in Equations (1)-(5).  
\[NDVI=\frac{B_{8}-B_{4}}{B_{8}+B_{4}} \tag{1}\]  
\[LSWI=\frac{B_{8}-B_{11}}{B_{8}+B_{11}} \tag{2}\]  
\[NDVI_{re1}=\frac{B_{8A}-B_{5}}{B_{8A}+B_{5}} \tag{3}\]  
\[NDVI_{re2}=\frac{B_{7}-B_{5}}{B_{7}+B_{5}} \tag{4}\]  
\[TCARI=\frac{3[(B_{6}-B_{5})-0.2(B_{6}-B_{3})(B_{6}/B_{5})]}{(1+0.16)(B_{6}-B_{ 5})/(B_{6}+B_{5}+0.16)} \tag{5}\]  
Figure 6: Overall technical flowchart.  
where \(B_{3}\), \(B_{4}\), \(B_{5}\), \(B_{6}\), \(B_{7}\), \(B_{8}\), and \(B_{8A}\) are the reflectance values of the corresponding bands of the Sentinel-2 images (Table 1).  
In this study, all Sentinel-1 images also underwent preprocessing steps such as thermal noise removal, radiometric calibration, and terrain correction. Eventually, all pixel values were transformed into decibel values through logarithmic scaling. The formula for this transformation is defined in Equation (6).  
\[\sigma=10\times log10(x) \tag{6}\]  
where \(x\) represents the original Sentinel-1 pixel value, and \(\sigma\) represents the converted backscattering coefficient value. Furthermore, the Sentinel-1 data underwent preprocessing steps such as clipping based on administrative boundaries and 10-day mean composites. For detailed information, please refer to Section 2.2.2 of this paper.  
The preprocessing of DEM data includes steps such as clipping based on administrative boundaries, calculation of slope and aspect, and resampling. The final DEM data has a resolution of 10 m and includes three bands: elevation, slope and aspect.  
From the three types of imagery, a total of feature sets was extracted, as shown in Table 2. In order to assess the accuracy of multi-source data in mapping tea plantations, three types of remote sensing image combination datasets were constructed: (1) optical images only, (2) SAR images only, and (3) a combination of optical and SAR images. It is worth noting that each dataset incorporates DEM features as Supplementary Information, which better aligns with practical application scenarios. The first image dataset included 93 features, consisting of 90 Sentinel-2 spectral and index features derived from 6 Sentinel-2 images (with the original ten bands listed in Table 1) and five calculated indices (Equations (1)-(5), along with 3 DEM features (elevation, slope, and aspect). The second image dataset contained 69 bands, comprising 66 Sentinel-1 backscatter coefficient features from 33 Sentinel-1 images, each with VV and VH polarization bands, along with 3 DEM features. The third image dataset contained 159 bands, consisting of 90 Sentinel-2 spectral and index features, 66 Sentinel-1 radar backscatter coefficient features, and 3 DEM features.