Approaches for Assessment of Soil Moisture with Conventional Methods, Remote Sensing, UAV, and Machine Learning Methods - A Review

Aug 5, 2026

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Soil moisture, a key constituent of Earth’s hydrological and ecological systems, underpins agricultural productivity, climate modeling, and water resource management. This review surveys conventional and advanced methods for estimating and measuring soil moisture, spanning in situ techniques, remote sensing, UAV-based monitoring, and machine learning models. It traces progress from destructive gravimetric workflows to non-invasive, high-resolution sensing. The study highlights growing use of Random Forest, support vector machines, and neural-network AI for modeling complex moisture dynamics across multi-source data. Bibliometric results map research trends, contributors, regions, and technologies. Overall, it supports integrating physics-based understanding, sensor capabilities, and data-driven learning to improve prediction accuracy, spatiotemporal coverage, and decision support.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages35

Reading Time1 h 28 min

AccessFree to download

Abstract

Keywords

Introduction

Overview of SMC (Soil Moisture Content)

Review Approaches for Assessment of Soil Moisture with Conventional Methods, Remote Sensing, UAV, and Machine Learning Methods—A Review Songthet Chinnunnem Haokip 1, Yogesh A. Rajwade 2, K. V. Ramana Rao 2, Satya Prakash Kumar 2, Andyco B. Marak 1 and Ankur Srivastava 3, * Academic Editor: Keith Smettem Received: 4 July 2025 Revised: 7 August 2025 Accepted: 8 August 2025 Published: 12 August 2025 Citation: Haokip, S.C.; Rajwade, Y.A.; Rao, K.V.R.; Kumar, S.P.; Marak, A.B.; Srivastava, A. Approaches for Assessment of Soil Moisture with Conventional Methods, Remote Sensing, UAV, and Machine Learning Methods—AReview. Water 2025, 17, 2388. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)w17162388 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . 1 The Graduate School, IARI, ICAR-Central Institute of Agricultural Engineering, Bhopal 462038, India; [haokip7696@gmail.com](haokip7696@gmail.com) (S.C.H.); andycomarak11@gmail.com (A.B.M.) 2 ICAR-Central Institute of Agricultural Engineering, Bhopal 462038, India; [yogesh.rajwade@icar.org.in](yogesh.rajwade@icar.org.in) (Y.A.R.); [kvramanarao1970@gmail.com](kvramanarao1970@gmail.com) (K.V.R.R.); [satyaprakasmar27@gmail.com](satyaprakasmar27@gmail.com) (S.P.K.) 3 School of Life Sciences, Faculty of Science, University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia * Correspondence: [ankur.srivastava@uts.edu.au](ankur.srivastava@uts.edu.au) Abstract Soil moisture or moisture content is a fundamental constituent of the hydrological system of the Earth and its ecological systems, playing a pivotal role in the productivity of agricultural produce, climate modeling, and water resource management. This review comprehensively examines conventional and advanced approaches for estimation or measuring of soil moisture, including in situ methods, remote sensing technologies, UAV-based monitoring, and machine learning-driven models. Emphasis is primarily on the evolution of soil moisture measurement from destructive gravimetric techniques to non-invasive, highresolution sensing systems. The paper emphasizes how machine learning modules like Random Forest models, support vector machines, and AI-based neural networks are becoming more and more popular for modeling intricate soil moisture dynamics with data from several sources. A bibliometric analysis further underscores the research trendsand identifies key contributors, regions, and technologies in this domain. The findings advocate for the integration of physics-based understanding, sensor technologies, and data-driven approaches to enhance prediction accuracy, spatiotemporal coverage, and decision-making capabilities. Keywords: in situ measurement; bibliometric analysis; machine learning; precision agriculture; random forest; remote sensing; soil moisture; UAV monitoring 1. Introduction 1.1. Overview of SMC (Soil Moisture Content) The temporary storage of water in the soil’s accessible pores is known as soil moisture and plays a key role in land–surface–atmosphere feedbacks [1,2] . In addition to supporting numerous research areas, namely, flood risk analysis and climate predictions, soil moisture is necessary for managing agricultural water supplies. Effective management of water not only helps in conserving the valuable resource (i.e., water) but also boosts crop profitability and helps prevent soil salinization. Additionally, regulators can utilize soil water content (SWC) data to verify pumping records, promoting accountability among water users, and enhancing the equilibrium between agricultural and environmental water needs [3] . In agriculture, increasing crop growth and yield de

FAQs

What role does soil moisture play in hydrological and agricultural systems?

Soil moisture supports land–surface–atmosphere feedbacks and is essential for managing agricultural water supplies. It influences crop growth, yield, nutrient absorption, microbial activity, and helps regulate soil temperature.

What measurement approaches are covered for assessing soil moisture?

The review covers conventional approaches such as direct or gravimetric methods, along with advanced non-invasive options including remote sensing and UAV-based monitoring. It also discusses machine learning-driven models for estimation.

How do machine learning models contribute to soil moisture prediction in this review?

The paper emphasizes increasing adoption of Random Forest, support vector machines, and AI-based neural networks. These models help capture intricate soil moisture dynamics using data from multiple sources.

Aug 5, 2026

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