## 3 Normalization Algorithms  
We used an approach of hyperspectral library\(-\)based normalization. As satellite sensors differ, the influence of sensor channels is best analyzed using radiometric normalization from hyperspectral library data. Polygon features (pseudo\(-\)invariant polygon features\(-\)based normalization) and point feature normalization (pseudo\(-\)invariant point features\(-\)based normalization) were compared to determine the most effective method. To identify the effects of adjacent bands, multiple\(-\)band fitting and single\(-\)band fitting  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Band** & **Image** & **Landsat 8\(-\)OLI (Resolution)** & **Sentinel\(-\)2A (Resolution)** \\ \hline
1 (Blue) & 0.450–0.510 \(\upmu\)m (30 m) & 0.458–0.523 \(\upmu\)m (10 m) \\
2 (Green) & 0.530–0.590 \(\upmu\)m (30 m) & 0.543–0.578 \(\upmu\)m (10 m) \\
3 (Red) & 0.640–0.670 \(\upmu\)m (30 m) & 0.650–0.680 \(\upmu\)m (10 m) \\
4 (NIR) & 0.850–0.880 \(\upmu\)m (30 m) & 0.785–0.900 \(\upmu\)m (10 m) \\
5 (SWIR\(-\)1) & 1.570–1.650 \(\upmu\)m (30 m) & 1.565–1.655 \(\upmu\)m (20 m) \\
6 (SWIR\(-\)2) & 2.110–2.290 \(\upmu\)m (30 m) & 2.100–2.280 \(\upmu\)m (20 m) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The selected bands of image data.  
Figure 1: Scope and true color images of the study area.  
methods were applied as cross\(-\)integrations. Therefore, pseudo\(-\)invariant point features with the single band (point\(-\)single), pseudo\(-\)invariant point features with multiple bands (point\(-\)multi), hyperspectral features with the single band (SpecLib\(-\)single), hyperspectral features with multiple bands (SpecLib\(-\)multi), pseudo\(-\)invariant polygon features with a single band (polygon\(-\)single), and pseudo\(-\)invariant polygon features with multiple bands (polygon\(-\)multi) were all tested to evaluate data normalization. Histogram matching was another approach we used for comparison.  
The main processing procedures we used were: (1) download valid and contemporaneous images of Sentinel\(-\)2A and Landsat \(8-\)OLI and resample Sentinel\(-\)2A images to have the same spatial resolution as Landsat \(8-\)OLI (30 m per pixel) using nearest neighbor interpolation; (2) perform spatial registration through geographical coordinates and manual processing; (3) select feature points in each ground type based on ground survey information; (4) confirm the best processing window size and calculate the mean reflectance of each polygon feature area; and (5) calculate fitting equations using the polygon features and couple them with single\(-\)band and multiple\(-\)band regression for image radiometric normalization.  
We examined two factors, correlation coefficient (R\({}^{2}\)) and root mean square error (RMSE), to determine the accuracy of both regression equations and normalization results. They are briefly described in Equations (1) and (2):  
\[R^{2}=\frac{Cov(S,\hat{S})}{\sqrt{Var(S)\cdot Var(\hat{S})}} \tag{1}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(S-\hat{S}\right)^{2}} \tag{2}\]  
where \(Cov(X,Y)\) is the covariance of X and Y, and \(Var(X)\) is the variance of \(X\). When the two factors were applied to evaluate linear regression, \(S\) and \(\hat{S}\) are the measured and predicted value, respectively. These represent the reference and normalized subject image reflectance, respectively, when used for normalization evaluation. \(N\) is the image pixel number.