# Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
## 2 Materials and Methods
### Establishment and Validation of the Soil Salinity Model  
In order to identify the influence of soil moisture on the quantitative retrieval of soil salinity, we established 5 soil salinity models according to the different drought levels based on soil relative humidity listed in Table 2. We also established the soil salinity model with the field survey soil salinity data and remote sensing imagery in the whole study area to compare the retrieval accuracy with that of the model considered the spatial variations of the soil moisture. Multiple linear regressions were used to develop the soil salinity models. The canopy response salinity index (CRSI) were included as explanatory variables to highlight the information related to the soil salinity, while the automate water extraction index (AWEI) and the normalized difference vegetation index (NDVI) were included as independent variables to emphasize the impact of soil water on the soil salinity and depress the noise and the vegetation background information at the same time. The CRSI, NDVI, and AWEI (Figure 3) were calculated as follows:  
\[\text{CRSI}=\sqrt{\frac{\left(NIR\times R\right)-\left(G\times B\right)}{\left( NIR\times R\right)+\left(G\times B\right)}} \tag{5}\]  
\[\text{NDVI}=\frac{\left(NIR-R\right)}{\left(NIR+R\right)} \tag{6}\]  
\[\text{AWEI}=B+2.5\times G-1.5\times\left(NIR+SWIR1\right)-0.25\times SWIR2 \tag{7}\]  
where _NIR_ is the near-infrared band reflectance, \(R\) is the red band reflectance, \(G\) is the green band reflectance, \(B\) is the blue band reflectance, and _SWIR_1 and _SWIR_2 are the short-wave infrared reflectance values derived from the Landsat-8 OLI images (Table 1).  
The coefficient of determination (\(R^{2}\)) and root mean square error (RMSE) (Equations (8) and (9)) were used to evaluate the accuracy of the soil salinity estimation. The inversion accuracy of the 5 soil salinity were compared with the soil salinity model of the entire study area, which did not consider the impact of soil moisture to determine the impact of the spatial heterogeneity of soil moisture on the inversion accuracy.  
\[R^{2}=\left(\frac{\sum_{i=1}^{N}\left(X_{i}-\overline{X}\right)\left(Y_{i}- \overline{Y}\right)}{\sqrt{\sum_{i=1}^{N}\left(X_{i}-\overline{X}\right)^{2}} \sqrt{\sum_{i=1}^{N}\left(Y_{i}-\overline{Y}\right)^{2}}}\right)^{2} \tag{8}\]  
\[\text{RMSE}=\left[\sum_{i=1}^{N}\left(X_{i}-Y_{i}\right)^{2}/N\right]^{1/2} \tag{9}\]  
where \(X_{i}\) represents the estimated value of the \(i\) sample, \(X\) is the estimated mean value, \(Y_{i}\) represents the measured value of the \(i\) sample, and \(Y\) represents the mean of the measured values. \(N\) is the number of samples.  
\begin{table}
\begin{tabular}{c c c} \hline
**RH** & **Soil Moisture Conditions** & **Drought Level** \\ \hline RH \textgreater{} 60\% & The surface is moist. & No drought \\
50\% \textless{} RH \textless{} 60\% & The soil moisture is normal and the soil surface is dry. & Slight drought \\
40\% \textless{} RH \textless{} 50\% & The soil surface is dry, and the vegetation lacks water and is yellow. & Moderate drought \\
30\% \textless{} RH \textless{} 40\% & A dry soil layer exists, and the leaves are dry and yellow. & Strong drought \\ RH \textless{} 30\% & Extreme dryness and vegetation death. & Extreme drought \\ \hline \end{tabular}
\end{table}
Table 2: Drought classification based on soil relative humidity.