Mapping soil salinity in 3‐dimensions using an EM38 and EM4Soil inversion modelling at the reconnaissance scale in central Morocco

et al. 2019

Land Degrad Dev

Self Cite

Soil salinization limits agricultural productivity and can ultimately cause desertification and land abandonment. One approach to assess soil salinity over large areas efficiently is to use electromagnetic instruments to measure the soil apparent electrical conductivity (ECa, mS m−1). ECa data can be then inverted to generate electromagnetic conductivity images (EMCIs), which provide the vertical distribution of the soil electrical conductivity (σ, mS m−1). In this study, we collected ECa data using an EM38 instrument across four locations with different levels of salinity in an important agricultural area of alluvial origin in Portugal. Using an inversion algorithm, we generated EMCIs and evaluated the potential for prediction of the electrical conductivity of the saturated soil paste extract (ECe). The main aim of our study is to compare regional and location‐specific calibrations in terms of ability to predict ECe from EMCIs. The results showed that the regional calibration predicted ECe unbiased, precisely (root mean square error [RMSE] = 2.54 dS m−1), and with strong concordance (0.93). The location‐specific calibration also predicted ECe unbiased, precisely (RMSE = 1.67 dS m−1), and with strong concordance (0.97). We conclude that the location‐specific calibration has slightly better prediction results, but the regional calibration is more practical for mapping soil salinity in the study area because it can be used at any new location without the need for new calibration. The prediction results at locations 3 and 4 show high to severely high soil salinity, which can compromise agricultural productivity. Therefore, monitoring soil salinity is required to conserve and improve agricultural productivity in the south of the study area.

Section: Introductionmentioning

confidence: 99%

Mapping soil salinity using electromagnetic conductivity imaging—A comparison of regional and location‐specific calibrations

Farzamian

et al. 2019

Land Degrad Dev

Self Cite

“…In order to search for an adequate λ value, the range from 0.07 to 3 is usually tested (Dakak et al, 2017; Huang, Kilminster, Barrett‐Lennard, & Triantafilis, 2017). In this work the 0‐to‐2 interval was explored instead, where a λ = 0 transforms the objective function Φ A into a least‐squares one.…”

Section: Model Applicationmentioning

confidence: 99%

A semi‐empirical model to predict the EM38 electromagnetic induction measurements of soils from basic ground properties

Visconti

European J Soil Science

2020

Electromagnetic induction (EMI) measurements (σb*) are widely used for the survey of several soil attributes, among which basic properties such as salinity (σe), water content (θw), clay (wc), organic matter (wom) and bulk density (ρb) stand out. In usual practice, purely empirical models relating one of these properties to σb* are calibrated at selected sites. However, this calibration is site and time specific and has to be repeated time and again. In order to understand where the variability of the EMI empirical models comes from, it is necessary to know how the different soil properties contribute to them and, with this aim, a more physically based relationship between σb* and, at least, σe, θw, wc, wom and ρb was developed in this work, additionally including soil temperature (t). It was calibrated and cross‐validated with the data from one survey carried out in a wide agricultural irrigation area in SE Spain, taking σb* measurements with the Geonics EM38 in the horizontal and vertical dipole modes and at various heights over the ground. Then, it was externally validated with the data from a second survey carried out 4 years later in the same area but in a different season. In the calibration, R2 and root mean square error (RMSE) were 0.84 and 0.18 dS m−1 (41%), respectively, for the vertical dipole orientation and 0.90 and 0.11 dS m−1 (39%) for the horizontal one. In the external validation, R2 and RMSE were 0.80 and 0.24 dS m−1 (44%), respectively, for the vertical dipole orientation and 0.90 and 0.13 dS m−1 (38%) for the horizontal one. Therefore, because the performance of the model barely worsened as time passed by, it can be considered to represent the underlying physical process and, therefore, to increase our understanding of how the soil EMI signals are generated, with potential benefits for the planning and comparability of EMI soil measurements, specifically with the EM38, among different areas.Highlights A semi‐empirical model was developed to predict soil EMI measurements from basic ground properties. Salinity, water content, clay, organic matter, bulk density and temperature were used as predictors. The model was able to explain between 80 and 90% of the variance in EMI measurements in the validation. This model helps us understand how the basic soil properties contribute to the EMI measurements.

“…Other sensors for σ b measurement are based on reflectometry, remarkably in the time domain (TDR) [12,23,24]. Then, electromagnetic induction (EMI) is another important technique used in many instruments [25,26], which notably does not need soil contact, and therefore is ideal for data-intensive works [27][28][29][30]. Finally, in more recent years the reflectometry technique has been expanded to the frequency domain with the development of frequency domain reflectometry (FDR) sensors for detection and reading of σ b in addition to soil relative bulk dielectric permittivity (ε b ) [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Field Comparison of Electrical Resistance, Electromagnetic Induction, and Frequency Domain Reflectometry for Soil Salinity Appraisal

Visconti

2020

Soil Systems

By using different physical foundations and technologies, many probes have been developed for on-site soil salinity appraisal in the last forty years. In order to better understand their respective technical and practical advantages and constraints, comparisons among probes are needed. In this study, three different probes, based on electrical resistance (ER), electromagnetic induction (EMI), and frequency domain reflectometry (FDR), were compared during a field survey carried out in a large salt-threatened agricultural area. Information about the soil bulk electrical conductivity (σb) at different depths was obtained with each of the probes and, additionally, other soil properties were also measured depending on the specifications of each instrument and, moreover, determined in samples. On average, the EMI and FDR techniques could be regarded as equivalent for σb measurement, whereas ER gave higher σb values. Whatever the case, EMI, and also ER, had to be supplemented with information about soil clay, organic matter, and water mass fractions to attain, despite this effort, poor soil salinity estimations by means of multiple linear regression models (R2 < 0.5). On the contrary, FDR needed only probe data to achieve R2 of 0.7, though root mean standard error (RMSE) was still 1.5 dS m−1. The extra measurements and calculations that modern electrical conductivity contact probes integrate, specifically, those based on FDR, remarkably increase their ability for soil salinity appraisal, although there is still room for improvement.