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

arXiv: https://arxiv.org/abs/2606.18316 · HTML: https://mlsanity.com/paper/2606.18316/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 60 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 2 of 2)

- [An Introductory Study on Time Series Modeling and Forecasting](https://mlsanity.com/paper/1302.6613/) (2013)
- [An Efficient Framework for Robust Sample Size Determination](https://mlsanity.com/paper/2512.16231/) (2025)

## Abstract (arXiv)

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.