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

Haokip, SC; Rajwade, YA; Rao, KVR; Kumar, SP; Marak, AB; Srivastava, A

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

Haokip, SC Rajwade, YA Rao, KVR Kumar, SP Marak, AB Srivastava, A

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Water Switzerland, 2025, 17, (16)
Issue Date:: 2025-08-01

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Field	Value	Language
dc.contributor.author	Haokip, SC
dc.contributor.author	Rajwade, YA
dc.contributor.author	Rao, KVR
dc.contributor.author	Kumar, SP
dc.contributor.author	Marak, AB
dc.contributor.author	Srivastava, A https://orcid.org/0000-0002-3963-265X
dc.date.accessioned	2026-01-14T05:04:40Z
dc.date.available	2026-01-14T05:04:40Z
dc.date.issued	2025-08-01
dc.identifier.citation	Water Switzerland, 2025, 17, (16)
dc.identifier.issn	2073-4441
dc.identifier.issn	2073-4441
dc.identifier.uri	http://hdl.handle.net/10453/191822
dc.description.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, high-resolution 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 trends and 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.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Water Switzerland
dc.relation.isbasedon	10.3390/w17162388
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Approaches for Assessment of Soil Moisture with Conventional Methods, Remote Sensing, UAV, and Machine Learning Methods—A Review
dc.type	Journal Article
utslib.citation.volume	17
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-01-14T05:04:36Z
pubs.issue	16
pubs.publication-status	Published
pubs.volume	17
utslib.citation.issue	16

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, high-resolution 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 trends and 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.

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http://hdl.handle.net/10453/191822