An Overview of Machine-Learning Methods for Soil Moisture Estimation

Soil moisture (SM) is crucial for sustainable applications in agriculture, meteorology, and hydrology. While direct measurement provides superior accuracy, it is unfeasible when applied over extensive geographical areas because of its costly and time-intensive nature. On the other hand, parameterization, complexity, and assumptions used in empirical and physical models lead to challenging SM estimations using these models. By handling extensive datasets and identifying complex connections within the data, the machine-learning (ML) approach has become an attractive solution to address the aforementioned limitations. This approach can estimate SM by effectively capturing the complex relationships among environmental variables and soil moisture data. Although the ML approach is a powerful tool for estimating SM, it has several limitations, such as data dependency, scalability, and high dimensionality. This paper aims to present an overview of ML methods used for modeling SM while also discussing their challenges and notable achievements within this field. These models vary in suitability depending on data availability and context. DL models excel in capturing spatiotemporal complexity but require abundant data. SVMs are robust in noisy or sparse datasets, and hybrid models offer improved flexibility and predictive accuracy. Incorporating remote sensing, satellite data, and hybrid physical-AI frameworks can further enhance performance. However, the opaque “black-box” nature of ML remains a barrier to trust and operational use, emphasizing the need for explainable AI (XAI) to improve transparency. The findings underscored the importance of prioritizing the transferability of AI-based models across varied environmental conditions to ensure scalable and dependable soil moisture monitoring.