# Correlation between Soil Moisture Change and Geological Disasters in E'bian Area (Sichuan, China)
## 4 Discussion  
The E'bian region is located at the eastern edge of the Sichuan Basin, characterized by complex geological structures with diverse rock types, structural formations, and geological systems. The terrain is rugged, and transportation is inconvenient, posing challenges to both traditional geological exploration and geophysical surveys. Being remote, the area  
Figure 12: (**a**) Trend diagram of the mean value of FVC from 2018 to 2023; (**b**) trend diagram of the mean value of TVDI from 2018 to 2023.  
incurs higher costs for resource development, thereby limiting investments in geological exploration activities. Additionally, insufficient funding further constrains geological exploration efforts in this remote region. Advanced technological support is essential for geological exploration, including geophysical surveys, geochemical exploration, and remote sensing techniques. However, the dissemination and application of such technologies may face limitations in remote areas, leading to gaps in geological exploration data. Consequently, traditional methods dominate geological exploration in this region, often relying on on-site sampling for geological hazard monitoring, which has limited effectiveness. Both traditional geological exploration and geophysical surveys typically require extended durations to yield results, hindering real-time data acquisition and precise disaster prediction. Moreover, enhancing data accuracy demands increased resource inputs, exacerbating the constraints. These combined factors contribute to the scarcity of fundamental geological exploration data in the study area. Currently, there are few studies on geological disasters in the E'bian area. The main methods include susceptibility assessment based on Geographic Information Systems (GISs) and slope units [50; 51; 52; 53; 54], using remote sensing technology to identify geological disasters (such as landslides and debris flows) [55; 56; 57; 58], early identification of geological disasters through integrated sky-ground monitoring [59; 60; 61], and using Landsat technology to identify landslides [62; 63; 64]. Additionally, many experts have conducted extensive research using remote sensing technology combined with the Temperature Vegetation Drought Index (TVDI) in forestry, agriculture, and other fields [65; 66; 67], but there are fewer studies focused on geological disasters. Traditional soil moisture measurement methods include the gravimetric method, tensiometer method, time domain reflectometry (TDR), conductivity method, and neutron scattering method. These methods have several drawbacks in monitoring and predicting geological disasters [68; 69], such as the need for on-site sampling. Since sampling points are typically sparse, they cannot comprehensively cover the entire study area. Due to significant spatial variations in soil moisture, the soil moisture at a single sampling point is difficult to use to represent the soil moisture of the entire region. These methods usually require on-site sampling, so their monitoring range is limited and time-consuming, making it difficult to achieve dynamic monitoring of the monitoring area. Compared to traditional methods, soil moisture measurement methods based on remote sensing technology have several advantages [70; 71; 72]. Firstly, remote sensing technology can cover large areas and provide soil moisture information globally, which is very effective for large-scale geological disaster prediction and monitoring. Remote sensing technology can provide continuous time series data, allowing us to monitor changes in soil moisture. Additionally, remote sensing technology is non-destructive for soil monitoring. Secondly, remote sensing technology can provide multispectral soil moisture information, which is useful for understanding the spatial variability and temporal dynamics of soil moisture. Furthermore, remote sensing technology can quickly obtain large amounts of soil moisture data, which is crucial for real-time geological disaster prediction and monitoring.