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

• URL: http://arxiv.org/abs/2606.18316v1
• Date: Tue, 16 Jun 2026 11:31:46 GMT
• Title: A Survey on Data-Driven Models for Soil Moisture Regression and Classification
• Abstract summary: This work presents a structured survey of AI-based models for soil moisture estimation and classification.<n>Existing approaches are organized into five categories: statistical time-series models, geostatistical methods, classical machine learning (ML) models, Deep Learning (DL) models and Probabilistic/Bayesian methods.<n>These models leverage historical soil moisture records, meteorological variables, vegetation indices, topography, soil characteristics, and geolocation data to perform regression or classification tasks.
• Score: 12.10955156131371
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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