# Correlation between Soil Moisture Change and Geological Disasters in E'bian Area (Sichuan, China)
## 4 Discussion
Through detailed soil moisture change analysis and high correlation verification, it provides new methods and perspectives for geological disaster monitoring and prediction, significantly enhancing the scientific capacity and effectiveness of geological disaster prevention and control in the study area. Compared to existing studies, this paper not only expands the application scope of remote sensing technology in geological disaster research but also provides important references for practical disaster prevention and mitigation efforts. Despite the achievements of this study, there are still some limitations. Although Landsat images have relatively high spatial resolution, higher resolution data are needed in certain situations, such as monitoring subtle geological disasters, to achieve more accurate results. Although this study used NDVI-LST feature space and the TVDI to analyze soil moisture, the occurrence of geological disasters is influenced by multiple factors, including rainfall, earthquakes, topography, vegetation, and human activities. Therefore, in practical applications, more factors need to be considered to improve the accuracy of predictions. This study focuses on E'bian Yi Autonomous County, so the results and conclusions may have some regional specificity. When extending the research results to other areas, regional differences and applicability need to be considered. Therefore, to address the limitations of this study, the following methods need to be adopted in future research for improvement and optimization:  
* Increase sample size and diversity: Expand the study area and increase the sample size to cover more types of geological disasters and soil types, which will help improve the generalizability and reliability of the research results. Additionally, consider the impacts of different seasons, climatic conditions, and human activities on geological disasters. By increasing the diversity of samples, the adaptability of the model can be enhanced. * Integrate more data sources: in addition to remote sensing data, it is necessary to integrate multi-source data such as meteorological data, geological survey data, topographic data, and socio-economic data. * Improve models and methods: Combine complex machine learning and deep learning algorithms to construct geological disaster prediction models. Additionally, incorporate spatio-temporal analysis techniques, such as spatio-temporal autocorrelation analysis and spatio-temporal clustering analysis, to more accurately capture the temporal and spatial patterns of geological disasters. * Consider socio-economic factors: In geological disaster monitoring and prediction, it is essential to consider the impact of socio-economic factors on disaster risk, in addition to natural factors. For instance, population density, building distribution, and infrastructure conditions may all affect the severity and extent of losses caused by geological disasters. In summary, through increasing sample size and diversity, integrating more data sources, improving models and methods, and considering socio-economic factors, geological disaster monitoring and prediction research can be further improved and optimized.