# Correlation between Soil Moisture Change and Geological Disasters in E'bian Area (Sichuan, China)
## 3 Results  
The binary pixel model inversion method was used to study the change rule of vegetation coverage (FVC), and a trend chart of FVC change was drawn (Figure 9). The results showed that the southern region was mainly a lower and low vegetation coverage area, but the distribution area gradually decreased, and the vegetation coverage decreased slightly or significantly. At this time, NDVI could only reflect slight changes and could not accurately indicate the specific vegetation growth amount. In the western region, the four types of coverage were distributed, and the change in vegetation coverage increased significantly. NDVI showed a significant positive correlation with the increase trend in vegetation coverage, but when the vegetation coverage increased to a certain range, the sensitivity of DVI to the change in vegetation cover decreased. In the northern region, the four kinds of coverage were also distributed, showing a significant improvement in general, and the late treatment effect was better. The eastern region showed alternating phenomena of increasing and decreasing coverage, which belonged to the state of ecological restoration. The increasing area of vegetation cover was mainly in the low sea wave and slow terrain, which was easy for ecological restoration. Figure 8: NDVI\(-\)Ts Feature Space Conceptual Model. In order to more directly observe the spatial distribution characteristics of soil moisture in the study area, a spatial distribution map of soil moisture levels in the study area was drawn based on the analysis results of the dry and wet boundary scatter plots of NDVI-LST feature spaces from 2018 to 2023, which were mainly divided into five levels: wet, normal, slightly arid, arid, and severely arid (Figure 10). By comparing the data of TVDI and measured soil moisture content, the effectiveness and accuracy of TVDI as a soil moisture monitoring indicator can be verified. Therefore, this paper plots the relationship between TVDI and soil moisture content and quantifies the strength of their linear relationship by calculating the correlation coefficient (R\({}^{2}\)) (Figure 11). This provides a quantitative basis for using TVDI for soil moisture retrieval. The experimental results show that when soil moisture content is high, vegetation growth is good and the surface temperature is relatively low, resulting in lower calculated TVDI values. Conversely, when soil moisture content is low, vegetation growth is restricted and the surface temperature is relatively high, resulting in higher calculated TVDI values. The R\({}^{2}\) value is 0.6934, reflecting high consistency between the two, thus proving the authenticity and accuracy of the experimental results. The maximum and minimum values of vegetation coverage change in the study area were statistically analyzed, and a trend chart of mean value change was obtained (Figure 12a). The results showed that the mean value of FVC showed an increasing trend from 2019 to 2020, from 2021 to 2022, and from 2022 to 2023. On the contrary, from 2018 to 2023, the trend of vegetation coverage change is more pronounced. The trend of vegetation coverage change is more pronounced in the study area, which is consistent with the trend of vegetation coverage change. Figure 9: Classification chart of changes from 2018 to 2023. 2019 and from 2020 to 2021, the average FVC showed a downward trend. The comparison between the trends in vegetation cover and soil moisture variation (Figure 12b) shows a high degree of consistency. The increase in soil moisture is primarily attributed to the increase in vegetation cover and sufficient rainfall. However, this conclusion only suggests that the surface water content has not exceeded the critical threshold of negative correlation. When the surface water content is below the critical threshold, increasing it will have positive effects, such as increasing soil moisture and enhancing vegetation growth.