Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review

Muhammad Waseem Rasheed, Jialiang Tang (), Abid Sarwar (), Suraj Shah, Naeem Saddique, Muhammad Usman Khan, Muhammad Imran Khan, Shah Nawaz, Redmond R. Shamshiri (), Marjan Aziz and Muhammad Sultan
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Muhammad Waseem Rasheed: Key Laboratory of Mountain Surface Processes and Ecological Regulation, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610229, China
Jialiang Tang: Key Laboratory of Mountain Surface Processes and Ecological Regulation, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610229, China
Abid Sarwar: Department of Irrigation & Drainage, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
Suraj Shah: College of Resources and Environment, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
Naeem Saddique: Department of Irrigation & Drainage, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
Muhammad Usman Khan: Department of Energy Systems Engineering, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
Muhammad Imran Khan: Department of Irrigation & Drainage, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
Shah Nawaz: Institute of Soil and Environmental Science, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
Redmond R. Shamshiri: Department of Engineering for Crop Production, Leibniz Institute for Agricultural Engineering and Bioeconomy, 14469 Potsdam, Germany
Marjan Aziz: Department of Agricultural Engineering, Barani Agricultural Research Institute, Chakwal 48800, Pakistan
Muhammad Sultan: Department of Agricultural Engineering, Bahauddin Zakariya University, Multan 60800, Pakistan

Sustainability, 2022, vol. 14, issue 18, 1-23

Abstract: The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.

Keywords: surface soil moisture; volumetric–tensiometric methods; moisture sensors; remote sensing methods; deep learning methods (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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