Aslam Latif Pathan

Central Soil Salinity Research Institute

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John Triantafilis

Manaaki Whenua – Landcare Research

Triven Koganti

Aarhus University

Ehsan Zare

UNSW Sydney

Jingyi Huang

Sun Yat-sen University

Bhaskar Narjary

Central Soil Salinity Research Institute

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Environmental Earth Sciences

UNSW Sydney

Manaaki Whenua – Landcare Research

University of Sydney

University of Lisbon

Commonwealth Scientific and Industrial Research Organisation

Cotton (United States)

Aarhus University

Indian Institute of Technology Kharagpur

University of Neyshabur

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Quantitative mapping of soil salinity using the DUALEM‐21S instrument and EM inversion software

Land Degradation and Development (2018)

Triven Koganti Bhaskar Narjary Ehsan Zare Aslam Latif Pathan Jingyi Huang

Abstract To generate baseline data for the purpose of monitoring the efficacy of remediation of a degraded landscape, we demonstrate a method for 3‐dimensional mapping of electrical conductivity of saturated soil paste extract (EC e ) across a study field in central Haryana, India. This is achieved by establishing a linear relationship between calculated true electrical conductivity (σ) and laboratory measured EC e at various depths (0–0.3, 0.3–0.6, 0.6–0.9, and 0.9–1.2 m). We estimate σ by inverting DUALEM‐21S apparent electrical conductivity (EC a ) data using a quasi‐3‐dimensional inversion algorithm (EM4Soil‐V302). The best linear relationship (EC e = −11.814 + 0.043 × σ) was achieved using full solution (FS), S1 inversion algorithm, and a damping factor (λ) of 0.6 that had a large coefficient of determination ( R 2 = 0.84). A cross‐validation technique was used to validate the model, and given the high accuracy (RMSE = 8.31 dS m −1 ), small bias (mean error = −0.0628 dS m −1 ), large R 2 = 0.82, and Lin's concordance (0.93), between measured and predicted EC e , we were well able to predict the EC e distribution at all the four depths. However, the predictions made in the topsoil (0–0.3 m) at a few locations were poor due to limited data availability in areas where EC a changed rapidly. In this regard, improvements in prediction can be achieved by collection of EC a in more closely spaced transects, particularly in areas where EC a varies over short spatial scales. Also, equivalent results can be achieved using smaller combinations of EC a data (i.e., DAULEM‐1S, DUALEM‐2S), although with some loss in precision, bias, and concordance.

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Concordance correlation coefficient

10.1002/ldr.2973

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