Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand

2006 ◽  
Vol 17 (6) ◽  
pp. 677-689 ◽  
Author(s):  
R. P. Shrestha
Keyword(s):  
Remote Sensing ◽  
Electrical Conductivity ◽  
Soil Properties ◽  
Soil Salinity ◽  
Soil Electrical Conductivity ◽  
Northeast Thailand

scholarly journals Spatial and Temporal Variability of Soil Salinity in the Yangtze River Estuary Using Electromagnetic Induction

2021 ◽  
Vol 13 (10) ◽  
pp. 1875
Author(s):  
Wenping Xie ◽  
Jingsong Yang ◽  
Rongjiang Yao ◽  
Xiangping Wang
Keyword(s):  
Electrical Conductivity ◽  
Spatial Distribution ◽  
Soil Salinity ◽  
Yangtze River ◽  
Laboratory Data ◽  
River Estuary ◽  
Yangtze River Estuary ◽  
Soil Electrical Conductivity ◽  
The Yangtze River ◽  
The Yangtze River Estuary

Soil salt-water dynamics in the Yangtze River Estuary (YRE) is complex and soil salinity is an obstacle to regional agricultural production and the ecological environment in the YRE. Runoff into the sea is reduced during the impoundment period as the result of the water-storing process of the Three Gorges Reservoir (TGR) in the upper reaches of the Yangtze River, which causes serious seawater intrusion. Soil salinity is a problem due to shallow and saline groundwater under serious seawater intrusion in the YRE. In this research, we focused on the temporal variation and spatial distribution characteristics of soil salinity in the YRE using geostatistics combined with proximally sensed information obtained by an electromagnetic induction (EM) survey method in typical years under the impoundment of the TGR. The EM survey with proximal sensing method was applied to perform soil salinity survey in field in the Yangtze River Estuary, allowing quick determination and quantitative assessment of spatial and temporal variation of soil salinity from 2006 to 2017. We developed regional soil salinity survey and mapping by coupling limited laboratory data with proximal sensed data obtained from EM. We interpreted the soil electrical conductivity by constructing a linear model between the apparent electrical conductivity data measured by an EM 38 device and the soil electrical conductivity (EC) of soil samples measured in laboratory. Then, soil electrical conductivity was converted to soil salt content (soil salinity g kg−1) through established linear regression model based on the laboratory data of soil salinity and soil EC. Semivariograms of regional soil salinity in the survey years were fitted and ordinary kriging interpolation was applied in interpolation and mapping of regional soil salinity. The cross-validation results showed that the prediction results were acceptable. The soil salinity distribution under different survey years was presented and the area of salt affected soil was calculated using geostatistics method. The results of spatial distribution of soil salinity showed that soil salinity near the riverbanks and coastlines was higher than that of inland. The spatial distribution of groundwater depth and salinity revealed that shallow groundwater and high groundwater salinity influenced the spatial distribution characteristics of soil salinity. Under long-term impoundment of the Three Gorges Reservoir, the variation of soil salinity in different hydrological years was analyzed. Results showed that the area affected by soil salinity gradually increased in different hydrological year types under the impoundment of the TGR.

scholarly journals Using Multivariate Geostatistics to Assess Patterns of Spatial Dependence of Apparent Soil Electrical Conductivity and Selected Soil Properties

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Glécio Machado Siqueira ◽  
Jorge Dafonte Dafonte ◽  
Montserrat Valcárcel Armesto ◽  
Ênio Farias França e Silva
Keyword(s):  
Electrical Conductivity ◽  
Soil Properties ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Clay Content ◽  
Data Sets ◽  
Soil Electrical Conductivity ◽  
Multivariate Geostatistics ◽  
Northwestern Spain

The apparent soil electrical conductivity (ECa) was continuously recorded in three successive dates using electromagnetic induction in horizontal (ECa-H) and vertical (ECa-V) dipole modes at a 6 ha plot located in Northwestern Spain. One of the ECadata sets was used to devise an optimized sampling scheme consisting of 40 points. Soil was sampled at the 0.0–0.3 m depth, in these 40 points, and analyzed for sand, silt, and clay content; gravimetric water content; and electrical conductivity of saturated soil paste. Coefficients of correlation between ECaand gravimetric soil water content (0.685 for ECa-V and 0.649 for ECa-H) were higher than those between ECaand clay content (ranging from 0.197 to 0.495, when different ECarecording dates were taken into account). Ordinary and universal kriging have been used to assess the patterns of spatial variability of the ECadata sets recorded at successive dates and the analyzed soil properties. Ordinary and universal cokriging methods have improved the estimation of gravimetric soil water content using the data of ECaas secondary variable with respect to the use of ordinary kriging.

Identifying Soil Properties that Influence Cotton Yield Using Soil Sampling Directed by Apparent Soil Electrical Conductivity

2003 ◽  
Vol 95 (2) ◽  
pp. 352-364 ◽  
Author(s):  
D. L. Corwin ◽  
S. M. Lesch ◽  
P. J. Shouse ◽  
R. Soppe ◽  
J. E. Ayars
Keyword(s):  
Electrical Conductivity ◽  
Soil Properties ◽  
Soil Sampling ◽  
Soil Electrical Conductivity ◽  
Cotton Yield

Soybean Root Distribution Related to Claypan Soil Properties and Apparent Soil Electrical Conductivity

Crop Science ◽  
2007 ◽  
Vol 47 (4) ◽  
pp. 1498-1509 ◽  
Author(s):  
D. Brenton Myers ◽  
Newell R. Kitchen ◽  
Kenneth A. Sudduth ◽  
Robert E. Sharp ◽  
Randall J. Miles
Keyword(s):  
Electrical Conductivity ◽  
Soil Properties ◽  
Root Distribution ◽  
Soil Electrical Conductivity ◽  
Soybean Root ◽  
Claypan Soil

Mapping within-field soil drainage using remote sensing, DEM and apparent soil electrical conductivity

Geoderma ◽  
2008 ◽  
Vol 143 (3-4) ◽  
pp. 261-272 ◽  
Author(s):  
Jiangui Liu ◽  
Elizabeth Pattey ◽  
Michel C. Nolin ◽  
John R. Miller ◽  
Oumar Ka
Keyword(s):  
Remote Sensing ◽  
Electrical Conductivity ◽  
Soil Electrical Conductivity ◽  
Field Soil ◽  
Soil Drainage

A RAPID METHOD FOR ESTIMATING WEIGHTED SOIL SALINITY FROM APPARENT SOIL ELECTRICAL CONDUCTIVITY MEASURED WITH AN ABOVEGROUND ELECTROMAGNETIC INDUCTION METER

1986 ◽  
Vol 66 (2) ◽  
pp. 315-321 ◽  
Author(s):  
N. C. WOLLENHAUPT ◽  
J. L. RICHARDSON ◽  
J. E. FOSS ◽  
E. C. DOLL
Keyword(s):  
Electrical Conductivity ◽  
Soil Salinity ◽  
Electrical Resistance ◽  
Electromagnetic Induction ◽  
Soil Depth ◽  
Soil Survey ◽  
Soil Electrical Conductivity ◽  
Simple Linear Regression ◽  
Survey Area ◽  
Parent Materials

This study presents a method for calibrating electromagnetic induction instrument readings with saturated paste electrical conductivity (ECe) for field mapping purposes. Each meter reading represents an integration of the apparent soil electrical conductivity (ECa) over the meter’s response depth. To correlate the meter readings with measured ECe within soil depth increments, several pedons representing a range of soil salinity for the survey area were sampled in 30-cm increments to a depth corresponding to the meter response. A weighting procedure based on the meter response-depth function was developed to condense the multiple ECe by depth measurements into a single weighted area specific value. These values were correlated with the apparent soil electric conductivity from the electromagnetic induction instrument by simple linear regression. This technique is designed for soil association of similar parent materials. The resulting regression equation yields a quick reliable equation that avoids complex mathematics and converts the instrument readings into weighted forms of commonly used saturated paste electrical conductivity values. Key words: Soil survey, electrical resistance

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