A Survey on Data-Driven Models for Soil Moisture Regression and Classification

A new survey categorizes AI-based models for soil moisture estimation into five groups: statistical time-series, geostatistical, classical machine learning, deep learning, and probabilistic/Bayesian methods. The study highlights how data-driven approaches overcome limitations of physics-based models by extracting empirical relationships from heterogeneous environmental data.

arXiv:2606.18316v1 Announce Type: new Abstract: Soil Moisture SM modelling constitutes a complex spatiotemporal learning problem characterised by nonlinear environmental interactions, heterogeneous data sources, and limited ground observations. Physics-based approaches, such as water balance models, rely on explicit hydrological equations and high-quality inputs, but their computational cost and scalability limitations restrict large-scale deployment. Data-driven artificial intelligence AI methods have emerged as flexible alternatives, enabling the extraction of empirical relationships between soil moisture and environmental variables with reduced modelling assumptions. This work presents a structured survey of AI-based models for soil moisture estimation and classification. Existing approaches are organized into five categories: a statistical time-series models, b geostatistical methods c classical machine learning ML models, d Deep Learning DL models and e Probabilistic/Bayesian methods. These models leverage historical soil moisture records, meteorological variables, vegetation indices, topography, soil characteristics, and geolocation data to perform regression or classification tasks.