# Correlation between Soil Moisture Change and Geological Disasters in E'bian Area (Sichuan, China)
## 2 Materials and Methods
### Research Method  
The binary pixel model for remote sensing data is a method for processing remote sensing data. In the binary pixel model, each pixel value is usually encoded into two categories, namely, changed pixels and unchanged pixels. Therefore, this model can be used to process multispectral remote sensing data and detect changes in vegetation coverage. The binary pixel model assumes that the pixel information received by the satellite sensor is composed of vegetation and soil, and the vegetation coverage (FVC) represents the percentage of pixels occupied by vegetation. This model not only has a simple and easy-to-understand theoretical basis, but also is easy to implement and apply. It is also the most widely used method for estimating vegetation coverage at present and can be applied to various types of geographical environments and vegetation types, with high versatility. Therefore, the binary pixel model is combined with remote sensing technology to use satellite image data for large-scale and efficient calculation of vegetation coverage, which can overcome the time and labor restrictions of traditional manual measurement methods, and improve the efficiency and accuracy of vegetation coverage calculation; it is also a new means and effective method for monitoring and predicting geological disasters. In this paper, five remote sensing image data in the study area were preprocessed, the pixel binary model was used to calculate the vegetation coverage (FVC), and the normalized vegetation index (NDVI) was used to study the changes in FVC in the study area. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Band** & **Band Color** & **Wave Length/um** & **Resolution/m** \\ \hline Band 1 & Blue band & 0.45–0.52 & 30 \\ Band 2 & Green band & 0.52–0.60 & 30 \\ Band 3 & Red band & 0.63–0.69 & 30 \\ Band 4 & Near-infrared band & 0.76–0.90 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Information of each band. When analyzing the proportion of pixel vegetation coverage, the confidence degree is taken as the basis for discriminating samples, that is, a 4-5% confidence degree is selected to calculate the NDVI value in a certain period (Equation (2)). When the cumulative percentage is 5%, the NDVI value is \(\text{NDVI}_{\text{soil}}\), indicating bare soil or no vegetation coverage; when the cumulative percentage is greater than or equal to 95%, the NDVI value is \(\text{NDVI}_{\text{veg}}\). At this time, NDVI value represents complete vegetation cover [45; 46]. According to the change rule of vegetation cover area (Table 3) that obtained by calculation and analysis (Equation (3)), the vegetation cover degree of the study area was divided into four levels: lower cover, low cover, medium cover, and high cover. \[\text{NDVI}=\frac{\text{NIR}-\text{R}}{\text{NIR}+\text{R}} \tag{2}\]  
where NIR is the NIR reflectance in the near-infrared band and R is the reflectance in the visible red band. \[\text{FVC}=\frac{\text{NDVI}-\text{NDVI}_{\text{soil}}}{\text{NDVI}_{\text{ veg}}-\text{NDVI}_{\text{soil}}} \tag{3}\]  
Based on the analysis of dynamic monitoring results of vegetation growth in the study area, a correlation analysis system of surface temperature and vegetation cover index was established. This method is called Temperature Vegetation Drought Index analysis (TVDI), which is an important means for retrieving surface soil moisture [47; 48; 49]. The principle is to obtain the dynamic development law of vegetation growth caused by topography, geomorphology, and climate conditions through radiometric calibration, atmospheric correction, and banding of remote sensing images by using full remote sensing data. With the increase in vegetation coverage, surface vegetation will convert part of the absorbed radiant energy into heat energy, while the conversion effect of sensible heat is relatively weakened. Which causes the surface soil temperature to drop.