Rapid, Accurate Mapping of Soil Salinity by Electromagnetic Ground Conductivity Meters

McNeill, JN

doi:10.2136/sssaspecpub30.c11

Cited by 60 publications

(34 citation statements)

References 7 publications

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“…The field measurement of EMI was carried out in late October of 2017. In each field, the EC a data were measured along crisscrossed grid lines with an interval size of 20 m using an EM38-MK2 (Geonics Ltd., Mississauga, Ontario, Canada) instrument in both vertical (EC av , mS m −1 ) and horizontal (EC ah , mS m −1 ) dipole modes with measuring depths of approximately 1.5 m and 0.75 m [7]. A built-in Global Positioning System (GPS) was used to record spatial information.…”

Section: Emi Measurementsmentioning

confidence: 99%

“…Then, a fraction of the secondary induced electromagnetic field from each loop was intercepted by the receiver. Finally, the sum of these signals was formed into an output voltage which is linearly related to a depth-weighted soil EC a [7]. In this case, the EM38-MK2 sensor was carried out in auto-collecting mode through the fields by an operator on foot.…”

Section: Emi Measurementsmentioning

confidence: 99%

“…In the late 1970s, researchers in the U.S. first applied the theory of EMI technique to measure the apparent electrical conductivity (EC a ) for field-scale soil salinity mapping [6]. Soil properties such as soil salinity, soil moisture, clay content, and temperature are the dominant factors that influence EC a [7]. By assuming relative homogeneity in other soil properties or having prior knowledge of them, the measurement of EC a using EMI has been used extensively to noninvasively characterize and map soil salinity [8].…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

et al. 2019

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Soil salinization is a global issue resulting in soil degradation, arable land loss and ecological environmental deterioration. Over the decades, multispectral and hyperspectral remote sensing have enabled efficient and cost-effective monitoring of salt-affected soils. However, the potential of hyperspectral sensors installed on an unmanned aerial vehicle (UAV) to estimate and map soil salinity has not been thoroughly explored. This study quantitatively characterized and estimated field-scale soil salinity using an electromagnetic induction (EMI) equipment and a hyperspectral camera installed on a UAV platform. In addition, 30 soil samples (0~20 cm) were collected in each field for the lab measurements of electrical conductivity. First, the apparent electrical conductivity (ECa) values measured by EMI were calibrated using the lab measured electrical conductivity derived from soil samples based on empirical line method. Second, the soil salinity was quantitatively estimated using the random forest (RF) regression method based on the reflectance factors of UAV hyperspectral images and satellite multispectral data. The performance of models was assessed by Lin’s concordance coefficient (CC), ratio of performance to deviation (RPD), and root mean square error (RMSE). Finally, the soil salinity of three study fields with different land cover were mapped. The results showed that bare land (field A) exhibited the most severe salinity, followed by dense vegetation area (field C) and sparse vegetation area (field B). The predictive models using UAV data outperformed those derived from GF-2 data with lower RMSE, higher CC and RPD values, and the most accurate UAV-derived model was developed using 62 hyperspectral bands of the image of the field A with the RMSE, CC, and RPD values of 1.40 dS m−1, 0.94, and 2.98, respectively. Our results indicated that UAV-borne hyperspectral imager is a useful tool for field-scale soil salinity monitoring and mapping. With the help of the EMI technique, quantitative estimation of surface soil salinity is critical to decision-making in arid land management and saline soil reclamation.

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Section: Emi Measurementsmentioning

confidence: 99%

Section: Emi Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

et al. 2019

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“…Among the several methods available to measure soil ECa, the methods by electromagnetic induction and by direct contact to the soil stand out. Some precision agriculture equipment was developed with the purpose of measuring soil ECa, thus providing the possibility of creating ECa spatial distribution maps (McNEILL, 1992;FREELAND et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Low cost aparatus for aparent soil electrical conductivity measurment based on direct currente

Barbosa¹,

Zandonadi²

2017

Nativa

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Soil Electrical Conductivity (ECa) has been used as one of the tools to aid decision-making process in the realm of Precision Agriculture. Due to the lack of research regarding ECa in the Northen part of Mato Grosso, the difficulty to access the commercial available ECa tools given the high acquisition cost and the large demand of studies in the region which is the major grain producing state in the country, the objective of this study was to develop a low cost system for ECa measurement based on DC current. The apparatus was based on the classic measurement method known as "four electrode method", and the data acquisition setup was built based on a low microcontroled board utilizing modules for data storage and visualization. The tests for evaluating the setup capacity to measure the necessary parameters for ECa calculations, was accomplished utilizing digital multimeters as references for measuring the voltages differences across the reference resistor and output probes. Simple regression analyses was accomplished to evaluate the dada showing promising results and indicating that the developed setup was able to measure the necessary parameters for soil ECa calculation.

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“…Another sensor-based measurement that can provide an indirect indicator of important soil physical properties is EC a of the soil profile (Sudduth, Kitchen, Myers, Drummond, 2010). Soil salinity, clay content, cation exchange capacity (CEC), clay mineralogy, soil pore size and distribution, and soil water content are some of the factors affecting EC a (McNeill, 1992). In non-saline soils, EC a variation is primarily a function of soil texture, water content, bulk density, and CEC (Corwin & Lesch, 2005).…”

Section: Introductionmentioning

confidence: 99%

Soil physical property estimation from soil strength and apparent electrical conductivity sensor data

Cho

Sudduth

Chung

2016

Biosystems Engineering

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Proximal soil sensing is an attractive approach for quantifying soil properties, but many currently available sensors do not respond to a single soil property. For example, soil strength and apparent electrical conductivity (EC a) sensor measurements are significantly affected by soil texture, bulk density (BD), and water content (WC). The objective of this study was to explore the potential for estimating soil texture, BD, and WC using combinations of sensor-based soil strength and EC a data obtained from sites with varying soil physical properties. Data collected from three research sites in Missouri included on-the-go horizontal soil strength at five depths up to 0.5 m on a 0.1-m interval, cone index measurements at the same depths, EC a measured by a Veris 3100, and depth-dependent, laboratory-determined soil properties. An EC a model inversion approach was used to generate layer EC values corresponding to the depth increments of the other variables. Fits of models using EC to estimate WC were variable (R 2 = 0.31-0.79). Best fitting BD estimation models (R 2 = 0.11-0.55) generally included EC, but soil strength was included in fewer than half of the models. BD model fits were improved considerably by adding lab-measured WC to the model (R 2 = 0.30-0.86), suggesting the need for a WC sensor. Soil clay texture fraction models based on EC and WC fit well (R 2 = 0.80-0.93). This study showed the potential of combining data from multiple mobile proximal sensors to estimate important soil physical properties.

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Rapid, Accurate Mapping of Soil Salinity by Electromagnetic Ground Conductivity Meters

Cited by 60 publications

References 7 publications

Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

Low cost aparatus for aparent soil electrical conductivity measurment based on direct currente

Soil physical property estimation from soil strength and apparent electrical conductivity sensor data

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