Quasi-3D mapping of soil moisture in agricultural fields using electrical conductivity sensing

Shaukat, Hira; Flower, Ken; Leopold, Matthias

doi:10.1016/j.agwat.2021.107246

Cited by 8 publications

(6 citation statements)

References 66 publications

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“…Based on the findings by the above researchers, subsequent studies have developed site-specific relationships between σ a and SWC [151,152,223,224]. The latest research improved the modeling approaches [225][226][227][228] to estimate the SWC from σ a .…”

Section: Soil Water Contentmentioning

confidence: 98%

“…Advanced electromagnetic models, combined with inversion techniques and multioffset measurements, can be used to reconstruct vertical conductivity profiles from the EMI measurements of soil σ a . This method allows for the estimation of the SWC at specific depths using mathematical models applied to the depth-specific σ a values [225][226][227][228]. The spatiotemporal variability of SWC in irrigated maize fields was assessed using the probabilistic inversion of time-lapse σ a data [225].…”

Section: Soil Water Contentmentioning

confidence: 99%

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Ground-Penetrating Radar and Electromagnetic Induction: Challenges and Opportunities in Agriculture

et al. 2023

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Information on the spatiotemporal variability of soil properties and states within the agricultural landscape is vital to identify management zones supporting precision agriculture (PA). Ground-penetrating radar (GPR) and electromagnetic induction (EMI) techniques have been applied to assess soil properties, states, processes, and their spatiotemporal variability. This paper reviews the fundamental operating principles of GPR and EMI, their applications in soil studies, advantages and disadvantages, and knowledge gaps leading to the identification of the difficulties in integrating these two techniques to complement each other in soil data studies. Compared to the traditional methods, GPR and EMI have advantages, such as the ability to take non-destructive repeated measurements, high resolution, being labor-saving, and having more extensive spatial coverage with geo-referenced data within agricultural landscapes. GPR has been widely used to estimate soil water content (SWC) and water dynamics, while EMI has broader applications such as estimating SWC, soil salinity, bulk density, etc. Additionally, GPR can map soil horizons, the groundwater table, and other anomalies. The prospects of GPR and EMI applications in soil studies need to focus on the potential integration of GPR and EMI to overcome the intrinsic limitations of each technique and enhance their applications to support PA. Future advancements in PA can be strengthened by estimating many soil properties, states, and hydrological processes simultaneously to delineate management zones and calculate optimal inputs in the agricultural landscape.

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Section: Soil Water Contentmentioning

confidence: 98%

Section: Soil Water Contentmentioning

confidence: 99%

Ground-Penetrating Radar and Electromagnetic Induction: Challenges and Opportunities in Agriculture

et al. 2023

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“…For the Kukerin site, soil moisture calibration for 0.8-1.6 m depth slice was done using the calibration curve developed from 0.5-0.8 m depth slice soil because this is a duplex soil with similar textural characteristics at these depths (Table 2). The resistivity to soil water calibration curves was developed using the same method as described by Shaukat et al (2022); in brief, a sub-sample of each soil depth was placed in a 10 cm × 6.25 cm × 4 cm plastic container and saturated with deionized water. A bulk density of 1.6 g cm −3 was used for Mooribin and 1.5 g cm −3 for Kukerin.…”

Section: Laboratory Estimation Of Soil Moisture (θ) Using Electrical ...mentioning

confidence: 99%

“…The measured soil Eca is a response to conductive soil parameters such as salt content, soil moisture, organic matter, soil texture, bulk density, stratigraphic layers, or bedrock (van Wesenbeeck and Kachanoski, 1988;Triantafilis et al, 2000;James et al, 2003;Corwin and Lesch, 2013;Doolittle and Brevik, 2014;Dakak et al, 2017). Generally, EMI assessments are used in agriculture (Hedley and Yule, 2009;Hanssens et al, 2019;Arshad et al, 2020;Shaukat et al, 2022) and especially for quantifying the risks related to salinity (Scudiero et al, 2017;Paz et al, 2020;Farzamian et al, 2021). However, Bennett and George (1995) conducted an EMI survey to quantify salinity for the new plantations of Eucalyptus globulus and suggested that this method could be used for assessing soil variations for risk assessments before establishing new plantations.…”

Section: Introductionmentioning

confidence: 99%

Soil Mapping Using Electromagnetic Induction to Assess the Suitability of Land for Growing Leptospermum nitens in Western Australia

Shaukat

Flower

Leopold

2022

Front. Environ. Sci.

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Leptospermum sp. with dihydroxyacetone in their nectar are a source of high-value medicinal honey production and can provide income from agriculturally marginal lands. The current study was from two newly planted Leptospermum nitens sites, one with duplex soil and the other in deep sandy soil, in the low rainfall areas of the south-west of Western Australia, with the aim of identifying key soil parameters influencing the plantation’s survival and growth. Electromagnetic induction (EMI) at different depths was used to investigate the possible impact of soil variability on the Leptospermum nitens plantations. Two EMI surveys were conducted at each site, at different times of the year, to account for soil moisture variability (relatively dry and wet conditions). A least-square inversion algorithm was used to determine true electrical conductivities at three different soil depths (0–0.5, 0.5–0.8, and 0.8–1.6 m) to produce quasi-3D maps of soil inverted electrical conductivity. Corresponding soil samples from each depth were used for the physico-chemical analysis of soil parameters and to develop laboratory-based electrical resistivity to soil volumetric moisture calibrations with R2 values between 0.95 and 0.99. Shrub survival and growth (canopy diameter) were estimated using unmanned aerial vehicle (UAV) images and machine learning. Comparing EMI soil mapping with UAV imagery results showed significantly greater shrub survival and growth (p < 0.001) in areas with higher ECa ranges of 12–24 mS m−1 at the variable textured site and 6–9 mS m−1 at the uniformly sandy site. Overall, the variable textured site, with an 82% survival rate, had a significantly higher shrub count and larger plants than the uniformly sandy site, with a 75% survival rate. A principal component analysis (PCA) identified inverted EC to be strongly correlated with soil moisture > pH > soil texture. Such soil mapping may be a robust and effective method for risk assessment of new shrub plantations.

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“…Soil moisture content (SMC) is a key variable affecting the water and energy balance in arid zones and the most important constraint in research on global and regional climate change, energy balance, and water cycle (Seneviratne et al, 2010). SMC directly affects crop growth and development and thus is an important factor in growth monitoring, yield prediction, and other agricultural applications (Ahmadi et al, 2022; Cao et al, 2022; Shaukat et al, 2022). Therefore, it is essential to collect high‐precision and large‐scale data on soil moisture for agricultural production purposes.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing‐based retrieval of soil moisture content using stacking ensemble learning models

Wang

et al. 2022

Land Degrad Dev

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Machine learning combined with multisource remote sensing data to assess soil moisture content (SMC) has attracted considerable attention in SMC studies, but the retrieval results still remain uncertain. The purpose of this study is to combine multiple single machine learning models with integrated learning algorithms and propose an SMC retrieval method based on multiple differentiated models under a stacking integrated learning architecture. First, 19 factors, including: radar backscattering coefficient, vegetation index, and drought index, that affect SMC were extracted from SENTINEL-1, LANDSAT, and terrain factors. Those with the highest importance scores were selected as retrieval factors using the Boruta algorithm combined with four single machine learning methods-classified regression tree, random forest, gradient boosting decision tree (GBDT), and extreme random tree. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and the generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that radar backscattering coefficient, temperature, vegetation drought index, land surface temperature, enhanced vegetation index, and solar local incident angle were the most important environmental variables for soil moisture retrieval. A comparison of the four machine learning methods in April and August showed that the GBDT model revealed the highest SMC retrieval accuracy, with root mean square error values of 1.87% and 1.64%, respectively. The stacking models were more accurate than the optimal single machine learning model, especially when using GBM. The multifactor integrated model constructed using spectral indices, radar backscatter coefficients, and topographic data exhibited high accuracy in soil surface moisture retrieval in an arid zone, providing a reference for land desertification studies and ecological environment management in the study region.

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Quasi-3D mapping of soil moisture in agricultural fields using electrical conductivity sensing

Cited by 8 publications

References 66 publications

Ground-Penetrating Radar and Electromagnetic Induction: Challenges and Opportunities in Agriculture

Ground-Penetrating Radar and Electromagnetic Induction: Challenges and Opportunities in Agriculture

Soil Mapping Using Electromagnetic Induction to Assess the Suitability of Land for Growing Leptospermum nitens in Western Australia

Remote sensing‐based retrieval of soil moisture content using stacking ensemble learning models

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