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

2026-06-16

AI Summary

Soil moisture modeling is hindered by nonlinear environmental interactions, heterogeneous multi-source data, and sparse in situ observations, while traditional physically based models suffer from high computational costs and poor scalability. This study presents a systematic review and, for the first time, categorizes data-driven approaches into five classes: statistical time-series models, geostatistical methods, classical machine learning, deep learning, and probabilistic/Bayesian techniques. These methods integrate multimodal data—including remote sensing, meteorological, soil, and topographic variables—for both regression and classification tasks. By clarifying the technical pathways, applicability, and performance limitations of each approach, this work provides a structured reference for AI-driven soil moisture estimation and advances hydrological modeling toward standardization and reproducibility.

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.

Problem

• Soil Moisture
• Spatiotemporal Modeling
• Data-Driven Models
• Environmental Interactions
• Limited Observations

Innovation

• soil moisture
• data-driven models
• artificial intelligence
• deep learning
• spatiotemporal modeling