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Electromagnetic induction (EMI) and electrical resistivity tomography (ERT) are geophysical techniques measuring soil electrical conductivity and providing insights into properties correlated with it to depths of several meters. EMI measures the apparent electrical conductivity (ECa, dS m−1) without physical contact, while ERT acquires apparent electrical resistivity (ERa, ohm m) using electrodes. Both involve mathematical inversion to obtain models of spatial distribution for soil electrical conductivity (σ, mS m−1) and electrical resistivity (ρ, ohm m), respectively, where ρ is the reciprocal of σ. Soil salinity can be assessed from σ over large areas using a calibration process consisting of a regression between σ and the electrical conductivity of the saturated soil paste extract (ECe, dS m−1), used as a proxy for soil salinity. This research aims to compare the prediction abilities of the faster EMI to the more reliable ERT for estimating σ and predicting soil salinity. The study conducted surveys and sampling at four locations with distinct salinity levels in Portugal, analysing the agreement between the techniques, and obtained 2D vertical soil salinity maps. In our case study, the agreement between EMI and ERT models was fairly good in three locations, with σ varying between 50 and 500 mS m−1. However, this was not the case at location 4, where σ exceeded 1000 mS m−1 and EMI significantly underestimated σ when compared to ERT. As for soil salinity prediction, both techniques generally provided satisfactory and comparable regional-level predictions of ECe, and the observed underestimation in EMI models did not significantly affect the overall estimation of soil salinity. Consequently, EMI demonstrated an acceptable level of accuracy in comparison to ERT in our case studies, supporting confidence in utilizing this faster and more practical technique for measuring soil salinity over large areas. [ABSTRACT FROM AUTHOR]

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Lezíria Grande de Vila Franca de Xira, located in Portugal, is an important agricultural system where soil faces the risk of salinization due to climate change, as the level and salinity of groundwater are likely to increase as a result of the rise of the sea water level and consequently of the estuary. These changes can also affect the salinity of the irrigation water which is collected upstream of the estuary. Soil salinity can be assessed over large areas by the following rationale: (1) use of electromagnetic induction (EMI) to measure the soil apparent electrical conductivity (EC a , mS m -1); (2) inversion of EC a to obtain electromagnetic conductivity imaging (EMCI) which provides the spatial distribution of the soil electrical conductivity (σ , mS m -1); (3) calibration process consisting of a regression between σ and the electrical conductivity of the saturated soil paste extract (EC e , dS m -1), used as a proxy for soil salinity; and (4) conversion of EMCI into salinity cross sections using the obtained calibration equation. In this study, EMI surveys and soil sampling were carried out between May 2017 and October 2018 at four locations with different salinity levels across the study area of Lezíria de Vila Franca. A previously developed regional calibration was used for predicting EC e from EMCI. Using time-lapse EMCI data, this study aims (1) to evaluate the ability of the regional calibration to predict soil salinity and (2) to perform a preliminary qualitative analysis of soil salinity dynamics in the study area. The validation analysis showed that EC e was predicted with a root mean square error (RMSE) of 3.14 dS m -1 in a range of 52.35 dS m -1 , slightly overestimated (- 1.23 dS m -1), with a strong Lin's concordance correlation coefficient (CCC) of 0.94 and high linearity between measured and predicted data (R2=0.88). It was also observed that the prediction ability of the regional calibration is more influenced by spatial variability of data than temporal variability of data. Soil salinity cross sections were generated for each date and location of data collection, revealing qualitative salinity fluctuations related to the input of salts and water either through irrigation, precipitation, or level and salinity of groundwater. Time-lapse EMCI is developing into a valid methodology for evaluating the risk of soil salinization, so it can further support the evaluation and adoption of proper agricultural management strategies, especially in irrigated areas, where continuous monitoring of soil salinity dynamics is required. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Regression models were developed for soil salinity assessment in Lezíria Grande, Portugal; • Multi-year maxima of 9 vegetation indices were correlated to rootzone salinity; • The CRSI, vegetation cover and soil type were the inputs selected by stepwise regression; • Remote assessment of soil salinity is a viable option in future soil monitoring programs. Lezíria Grande is an important agricultural area in Portugal, prone to waterlogging and salinity problems due to the influence of estuarine tides on groundwater dynamics. Simple, non-invasive, practical approaches are need for monitoring soil salinity in the region and preventing further degradation of soil resources. The objective of this study was to develop regression models for soil salinity assessment in Lezíria Grande based on the relationship between multi-year crop reflectance data derived from Sentinel-2 multispectral imagery and rootzone salinity. Nine vegetation indices (VI), computed from the annual averages of the spectral bands, were tested between 2017 and 2019. The multi-year maximum from each pixel was then used for correlating the VI with the ground-truth dataset. This dataset was composed of average values of the electrical conductivity of the soil saturation paste extract (EC e mean) measured in 80 sampling sites (0–1.5 m depth) located in four agricultural fields representative of the salinity gradient in the region. The Canopy Response Salinity Index (CRSI), which uses the blue (490 nm), green (560 nm), red (665 nm), and infrared (842 nm) bands, provided the strongest correlation with measured data (r=−0.787). Regression models further considered vegetation cover and soil type as explanatory variables, with predictions resulting in a coefficient of determination (R2) ranging from 0.63 to 0.91 and a root mean square error (RMSE) varying from 1.63 to 3.26 dS m−1. The use of remote sensing data for soil salinity assessment showed to be an interesting option to consider in future soil monitoring programs. Nevertheless, more detailed covariates are needed for improving salinity assessment models at the regional scale. [ABSTRACT FROM AUTHOR]

• EC e , SAR and ESP are highly correlated with the true electrical conductivity, σ. • σ is able to predict the in-depth soil salinity and sodicity from EC e , SAR and ESP. • 2-D maps show the vertical variation of the soil salinity and sodicity. • Electromagnetic conductivity imaging can be used for efficient soil monitoring. Salt related problems in soils can refer to an excess of soluble salts (saline soils), a dominance of exchangeable sodium in the soil exchange complex (sodic soils), or a mixture of both situations (saline-sodic soils). These categories are important because the impacts and management vary accordingly. Electromagnetic induction (EMI) methods and inversion techniques have been used to obtain electromagnetic conductivity images of the soil true soil electrical conductivity (σ) which can be used to estimate soil salinity and other soil properties in-depth and over large areas. However the potential to predict both soil salinity and sodicity with these methods has not been fully investigated. In this study, data collected with an EMI instrument (EM38) at two modes and heights and an inversion algorithm were used to obtain σ. Soil samples were collected at five layers to a depth of 1.35 m, at sampling sites along the EMI transects, and used for laboratory determination of the soil physico-chemical properties – electrical conductivity of the soil saturation paste extract (EC e), sodium adsorption ratio (SAR), pH, cation exchange capacity (CEC), exchangeable sodium percentage (ESP), volumetric water content (θ), and particle size distribution. A principal component analysis (PCA) was performed to analyze the correlation between σ and the soil physico-chemical properties. Correlations between σ and EC e , ESP and SAR could be established and prediction results were evaluated using the leave-one-out cross validation method and calculating the root mean square error of prediction (RMSEP). It was possible to predict EC e (RMSEP = 2.03 dS·m−1), SAR (RMSEP = 4.68 (mmol c ·L−1)0.5), and ESP (RMSEP = 3.83%) from σ and to classify the soil according to salinity and sodicity. The results show that it is possible to use EMI to monitor soil salinity and sodicity in risk areas rapid and efficiently, which is required to conserve and improve the soil functions. [ABSTRACT FROM AUTHOR]

Secondary salinization has long been reported in the Roxo irrigation district (RID), southern Portugal, due to the use of saline-prone irrigation water and the existence of poorly structured soils. This study assessed the soil water and salt budgets in nine commercial orchards located in the RID using the multiple ion chemistry module available in the HYDRUS-1D model during the 2019 and 2020 growing seasons. The studied crops were almond, olive, citrus (orange, mandarin, and clementine), and pomegranate. The model successfully simulated soil water contents measured in the different fields but there was a clear underestimation of the electrical conductivity of the soil saturation paste extract (EC e) in some locations, while simulations of the sodium adsorption ratio (SAR) were generally acceptable. Modeling errors were mostly associated with missing information on fertigation events rather than related to the effects of irrigation water quality. The water and salt balances were also computed for the 1979–2020 period. Considering the probability of non-exceedance of salt accumulation during this period, the risk of salinity build-up was high to very high for the very dry years in most fields, except in the citrus sites. The factors influencing the salt accumulation were the irrigation strategy, the seasonal irrigation and rainfall depths, the duration of the crop growth period, the rainfall distribution in the late and non-growing stages, the soil drainage conditions, and the irrigation water quality. For the current climate conditions and irrigation water quality, the risk of soil salinity levels affecting crop development and yields was found to be minor. This means that, despite salts tended to accumulate in the rootzone over a season, under current conditions the salinity stress did not reach harmful levels for plants. Only in two of the study sites, there was a need to promote salt leaching. Hence, this study shows that soil salinization risks in the study area are low but, for given locations during drier seasons, there is a need for tailored irrigation solutions aimed at the conservation of soil and water resources. • Soil salinization risks in nine commercial orchards were assessed with HYDRUS-1D. • The probability of non-exceedance of salt accumulation was computed for 1979–2020. • The risk of salinity build-up was high to very high for the very dry years in most fields. • For current conditions, the risk of soil salinity levels affecting crop development was minor. • Tailored irrigation solutions should be considered in some cases in dry and very-dry years. [ABSTRACT FROM AUTHOR]