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Monitoring of Soil Salinity for Precision Management using Electromagnetic Induction Method

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25 January 2024

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26 January 2024

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Abstract
In this study, the temporal variation in soil salinity dynamics was monitored and analyzed using Electromagnetic Induction (EMI) in an agricultural area in Port Said, Egypt, which is at risk of soil salinization. To assess soil salinity, repeated CMD2 soil apparent electrical conductivity (ECa) measurements were taken and inverted to generate electromagnetic conductivity imaging (EMCI), representing soil electrical conductivity (σ) distribution through time-lapse inversion. This process involved converting EMCI data into salinity cross sections using a site-specific calibration equation that correlates σ with the electrical conductivity of saturated soil paste extract (ECe) for the collected soil samples. The study was performed from August 2021 to April 2023, involving six surveys during two agriculture seasons. The results demonstrated the accurate prediction ability of soil salinity with R2 value of 0.81. The soil salinity cross sections generated on different dates observed changes in the soil salinity distribution. These changes can be attributed to shifts in irrigation water salinity resulting from canal lining, winter rainfall events, and variations in groundwater salinity. This approach is effective for evaluating agricultural management strategies in irrigated areas where it is necessary to continuously track soil salinity to avoid soil fertility degradation and a decrease in agricultural production and farmer’s income.
Keywords: 
Subject: Environmental and Earth Sciences  -   Soil Science

1. Introduction

Soil salinization, caused by soluble salts in the soil and/or irrigation water, is a leading factor in soil degradation (Stavi et al., 2021). The 2021 Food and Agriculture Organization (FAO) report estimates that globally, 833 million ha of agricultural land is salt-affected, including saline and sodic soils, with most of these areas located in arid and semi-arid regions (FAO, 2022). This salinity poses a severe threat to agricultural production and food security (Shrivastava and Kumar, 2015). Approximately 20% of cultivated lands and 33% of irrigated agricultural lands worldwide are affected by soil salinity, with an annual expansion rate of 10%, driven by many factors such as low precipitation and the use of saline irrigation water (Machado and Serralheiro, 2017).
Soil salinity is a significant driver of land degradation, negatively impacting crop growth and quality due to osmotic stress (Gorji et al., 2017). To mitigate the spread of soil salinization, efficient and time-saving methods are required to monitor soil salinity in agricultural plots. This necessity arises because conventional methods for measuring salinity are considered time-consuming and labor-intensive.
One method for quickly and reliably assessing soil salinity across different locations is through electromagnetic induction (EMI) (Bouksila et al., 2013; Paz et al., 2020a). Electromagnetic instruments measure the apparent electrical conductivity of soil (ECa; mS m−1), which is often strongly correlated with the electrical conductivity of saturated soil paste extract (ECe) (Bouksila et al., 2012). Recent advancements include the use of electromagnetic inversion software to account for vertical variation in ECe, which involves the creation of electromagnetic conductivity images (EMCIs) for salinity mapping. Many researchers (e.g., Corwin and Yemoto, 2019; Wang et al., 2021; Xie et al., 2021; Flores et al., 2022) developed a linear regression (LR) relationship between soil electrical conductivity (σ), inverted from various combinations of ECa data, and soil salinity.
Although EM38 and CMD mini-explorer devices are commonly employed for soil salinity mapping across various global locations (e.g., Brogi et al., 2019; Farzamian et al., 2019, 2021; Ben Slimane et al., 2022), the use of the CMD2 device has been comparatively limited. Utili (2020) employed the CMD2 device to establish a calibration relationship between water content and ECa, to monitor water content in earthen embankments along the river Irvine in Galston, UK. Apostolopoulos and Kapetanios (2021) integrated CMD2 and CMD4 devices into an archaeological study to map the distribution of loose sediments in Lavreotiki, Greece, aiming to predict the path of ancient rivers. Koganti et al. (2018) established 3-dimensional maps of the electrical conductivity (ECe) across an agricultural field in central Haryana, India. They used the DUALEM-2S, with a configuration (2mHcon) comparable to the CMD2 instrument in its vertical mode of operation, as the effective depth of exploration (DOE) is 3 m.
Yao et al. (2016) used repeated electromagnetic induction (EMI) measurements and linear mixed-effects models to map soil salinity in a coastal agricultural landscape in Jiangsu Province, China. Results demonstrated that EMI measurements were effective in calibrating and identifying the spatial distribution of soil salinity in the predominantly moderately and highly saline areas of the landscape. Yao and Yang (2010) used EM38 and EM31 instruments in a mobile EMI system to address soil salinity issues in the Lower Yellow River Delta. Their study aimed to assess soil salinity patterns by analyzing the relationship between apparent soil electrical conductivity (ECa) measured by EM38h and EM31h and corresponding salinity levels through linear regression models. EM38h was found to sense shallow depths (0 to 40 cm), while EM31h detected deeper layers (40 to 100 cm) within the 0 to 100 cm range. Notably, the effective depth of investigation for EM38h is generally shallower (0.75 to 1.5 m), whereas EM31h has deeper penetration, reaching 6 to 9 m beneath the soil surface.
Since the inversion of ECa for soil characterization using EMI is a relatively recent development, the lack of validation using independent datasets currently limits a more widespread use in salinity monitoring (Corwin and Scudiero, 2019). Moreover, a critical research question in the context of employing EMI for soil salinity monitoring, particularly using LR, is whether the LR developed within one survey can be applied to subsequent surveys without the necessity of establishing a new LR for each survey which requires further investigations.
The main objectives of this study were to a) evaluate CMD2’s capability for tracking and monitoring soil salinity in intensely used agricultural land over an extended period, b) develop a site-specific calibration for using time-lapse EMCI inversion and individual EMCI inversion, c) assess and compare the predictive performance of the developed calibration equation from both techniques in predicting ECe from EMCIs, and d) analyze and track the dynamics of soil salinity over time by creating soil salinity cross-sections for each data collection date. The indirect objective of the current research was to assess the impact of canal lining on soil salinity in the investigated agricultural plot.

2. Materials and methods

2.1. Study area

The study area is located approximately 10 km south of Port Said City in Egypt (Figure 1a). The study focused on one intensely used and representative agricultural plot in the area, as shown in Figure 1b. The Suez Canal bounds this area to the east and El-Manzala Lake to the west. The area is located between 32° 15’ E to 32° 17’ E longitude and 31° 10’ N to 31° 11’ N latitude, covering an agricultural area of 1200 ha. This area has a relatively flat topography, with an elevation ranging from 1 to 2 m above mean sea level. According to the USDA Soil Taxonomy (USDA, 1999), the soil is sandy clay loam, and the region is classified as arid according to the Köppen-Geiger climate classification (Geiger, 1954). In the past, this area was part of Lake Manzala but was drained and converted into agricultural land in 1990, where sabkha deposits and salt crusts with remnants of seashells dominate the region. When the water was drained from the lake, it left behind a sediment layer that was rich in nutrients and organics. However, the sediment layer also contains high levels of salt, which can lead to soil salinization and decreased crop productivity over time (Aziz et al., 2022).
The plot is comprised of four basins, and the dimensions of each basin, as well as the location of the drainage water and irrigation water system, are depicted in Figure 1b. Figure 2 displays daily recordings of total rainfall and average temperature obtained throughout the study period at the Port Said meteorological station indicated by the yellow circle in Figure 1a, located at 31°15’36.0"N and 32°17’24.0"E, along with irrigation and survey events (source: https://en.tutiempo.net/, accessed on 6 October 2023). The average annual rainfall is 98 mm, mainly occurring in winter with a maximum amount of 49 mm in January. The average minimum and maximum temperatures are 4.2 and 42.6°C occurring in January and May, respectively. The groundwater level was measured during each survey and the recorded depth was similar and approximately around 0.85 m during all surveys.
Flood irrigation is the main irrigation method used in the study area. The agricultural plot received irrigation water from the final branch of the Port Said Canal, which receives its water supply from the Ismailia Canal. The irrigation canal used to irrigate the agricultural lands in the study area is part of the national canal rehabilitation project initiated by the Egyptian Government (source: https://infonile.org, accessed on 11 September 2023). This canal was recently lined with cement concrete during the period between January 2022 and May 2023. During the 2021 surveys, there was occasional seepage of drainage water into the canals due to the absence of canal lining. Before the lining, the measured electrical conductivity of the irrigation water (ECw) was 4.0 dS m−1, falling within the moderately saline range (2-10 dS m−1) as defined by FAO guidelines (Rhoades et al., 1992). However, according to the guidelines for interpretations of water quality for irrigation, which take into account crop tolerance to salinity, the degree of restriction is severe for use for ECw superior to 3 dS m-1(Ayer and Westcot, 1985). Following the initiation of canal lining, subsequent measurements indicated a significant reduction in ECw, decreasing to 1 dS m−1 which falls within the slightly saline range (0.7-2 dS m−1). The surface drainage system consists of open drains in a V-shaped pattern with a depth of 1.0 meter and 17.5 meters apart. These drains ultimately converge into the branch drain, as shown in Figure 1b.
The main crops cultivated in the area are Sorghum Sudanese which is used as animal feed. The geophysical surveys were conducted to monitor soil salinity of the investigated plot at a 20 m transect shown in Figure 1b. These surveys mainly aimed to track the temporal variation in soil salinity distribution during a complete agricultural season. The salinity levels in the plot were relatively moderate, with vegetation covering the entire plot. The Turkmen Agricultural Institute found that planting Sorghum Sudanese in saline areas reduces soil salinity, promoting healthy crop growth and yields (Yollybayev and Gurbanov, 2018). Additionally, it lowers the groundwater level and prevents soil salinization (Clark, 2008; Yollybayev and Gurbanov, 2018). The productivity of Sorghum Sudanese has the potential for up to five annual harvests (Mirsharipova et al., 2023). The study area was initially prepared for planting Sorghum Sudanese in September 2021 when fertilization with potassium, nitrogen, and phosphate, as well as seed sowing, were conducted. However, the presence of harmful weeds caused a delay in cultivation until December 2021. The cultivation extended over six months, encompassing five harvests, with the last harvest in May 2022.

2.2. Soil sampling and laboratory analysis

As shown in Figure 1b, soil samples were taken along the investigated transect on all dates concurrently with EMI surveys. Between 1 and 6 boreholes were drilled, depending on the date of measurement, as illustrated in Table 1. At each borehole location, three soil samples were collected along 90 cm depth representing topsoil (0.0–0.3 m), subsurface (0.3–0.6 m), and subsoil (0.6–0.9 m). The ECe of the soil saturation paste extract was determined in the soil laboratory of the Faculty of Engineering, Port Said University, Egypt. According to Richards (1954), ECe was measured in the extract obtained using suction filters from soil saturation paste using a conductivity meter (HI5521-01). In general and according to the terminology proposed by Barrett-Lennard et al. (2008), the soil can be classified as non-saline, slightly saline, moderately saline, highly saline, and severely saline if its ECe ranged from (0-2 dS m−1), (2–4 dS m−1), (4–8 dS m−1), (8–16 dS m−1), and (>16 dS m−1), respectively.

2.3. Collection and inversion of ECa data

ECa data were collected using a low-frequency electromagnetic induction (EMI) technique (i.e., CMD2 conductivity meter; GF Instruments). The CMD2 has a transmitter coil consisting of horizontal (ECah) and vertical (ECav) receiver arrays. It functions at a frequency of 10 kHz. The separation between the transmitter and receiver is 1.89 m, allowing for theoretical measurements of ECa depths ranging from 0.0 to 1.5 m and 0.0 to 3.0 m for the ECah and the ECav, respectively.
the beginning, a pilot survey was conducted by CMD2 positioned approximately 1.00 m above the ground surface throughout the investigated plot (Figure 1b) to explore variations in soil salinity. Only the horizontal dipole orientation was considered during this survey. The objective of the initial investigation was to gain insights into the spatial variability of soil salinity across the field qualitatively and to identify a representative transect for further study. According to this survey, a specific transect within the investigated plot was selected for detailed study (Figure 1b). The location of this transect was selected based on the variability of measured ECa values in the investigated plot (Figure 3).
Notable heterogeneity in ECa values was observed along this transect. Furthermore, it was oriented parallel to the drainage direction, located between two drains to ensure that the CMD2 readings were not affected by drainage water or its salinity. Subsequently, a series of CMD2 surveys were conducted along the selected single transect over a period spanning six dates from August 2021 to April 2023. Measurements were obtained at intervals of 1 m along the 20 m transect (Figure 1c), using a GPS integrated with the device for registration of the position. ECa was collected with a CMD2 lying on the ground surface in both horizontal and vertical dipole orientations.
For the inversion, the EM4Soil software (EMTOMO, 2018) was used to estimate the true electrical conductivity (σ, mS m−1) at any depth. These estimates were generated by using 1D laterally constrained inversion, Quasi 2D, a technique developed by Santos et al. (2010 & 2011). A forward model based on the full solution of the Maxwell equations (Kaufman and Keller, 1983) was used in this study. This method is recommended when the ECa values are high (>100 mS m−1). Two different inversion algorithms, S1 (Sasaki, 1989) and S2 (Sasaki, 2001) were used that are based on Occam regularization (De Groot-Hedlin and Constable, 1990). To run this algorithm, the special Lagrangian multiplier (λ) should be determined. λ is responsible for regulating the balance between the smoothness of the spatial model and the misfit to the data response. A larger λ typically results in a model with a greater misfit error but smoother conductivity values. This is generally acceptable when soil conductivity changes follow a gradual pattern, leading to a more realistic model. Conversely, a smaller λ is usually required when anticipating sharper transitions in soil conductivity to accurately delineate abrupt boundaries. The determination of a suitable λ value typically conducts inversions with various λ values (e.g., Triantafilis and Monteiro Santos, 2013; Zare et al., 2020).
The number and depth of layers in the initial model and the conductivities of each layer were determined based on the ECa data mean. The value of λ varied from 0.02 to a maximum of 3.0, with inversion stopping after a maximum of 10 iterations.
Two different techniques were used to invert the six surveys, namely individual and time-lapse inversion. In the individual inversion, each survey was inverted independently, and only spatial regularization was applied. In contrast, in time-lapse inversion, the data for the six dates were simultaneously inverted and the temporal regularizations were also applied. Therefore, it was necessary to optimize the spatial and temporal Lagrangian multipliers. The temporal Lagrangian multiplier (α), functions as a temporal damping factor, assigning weight to the minimization of temporal conductivity changes along the time axis. The α remains constant and is determined by the similarity between two consecutive reference times. A higher α value yields more similar reference models resulting from the inversion, while a value of zero signifies the absence of temporal constraints, resembling a traditional non-time-lapse inversion (Farzamian et al., 2022).
The value of λ used in the current study ranged from 0.02 to a maximum of 1.5, while α was set at either 0.5 or 0.1, with a maximum number of 10 iterations. To develop optimum electromagnetic conductivity images (EMCIs) relative to measured ECe, the CMD2 ECa data associated with the soil sampling sites were used. Subsequently, the optimal inversion method was determined based on whether individual or time-lapse inversion algorithm provides better LR for soil salinity calibration, and optimal inversion parameters were chosen. The selected parameters achieved a higher R2 value between σ and the measured ECe and lower model misfit error among different regression models (EMCIs). The R2 values were compared using the classification proposed by Moore and Kirkland (2007), where a strong agreement was defined as R2 > 0.70, moderate as 0.5 < R2< 0.7, weak as 0.3 < R2 < 0.5, and very weak as R2 < 0.3.

2.4. Prediction of ECe from EMCI using site-specific calibrations

As a first step, a unique equation (i.e., a site-specific calibration equation) was derived for the investigated plot based only on the ECa data from the third survey. The best-selected sets of inversion parameters described in Section 2.3 were used to develop a linear regression model between σ and the measured ECe.
After that, a cross-validation process was used to assess the predictive ability of the calibration equation. Cross-validation was carried out using the leave-one-out cross-validation (LOOCV) method. In this method, one sample was removed, and the calibration was established based on the remaining samples to predict ECe at the point where the sample was removed. This process was iteratively repeated for each sampling point from the sampling sites (18 soil sampling points) until each sample had been taken once. The ability of the site-specific calibration to predict ECe was assessed using several metrics. The root mean square error (RMSE; Eq. 1) was computed to evaluate the overall prediction accuracy and the mean error (ME; Eq. 2) was calculated to assess any prediction bias. Additionally, Lin’s concordance correlation coefficient (LCCC; Lin, 1989) was calculated to evaluate the degree of similarity between the linear regression (LR) and the 1:1 relationship and to quantify the agreement between the two variables.
M E = 1 n ∑ j = 1 n E C m i − E C p i
R M S E = 1 n ∑ j = 1 n E C p i − E C m i 2
where n is the total number of data, ECmi and ECpi are the measured and predicted ECe, respectively.
In the second step, repeated EMI and ECe measurements, which were collected simultaneously with the EMI surveys for all measurement surveys except survey 3, were used as an independent validation dataset. This dataset was employed to assess (i.e., validate) the prediction ability of the site-specific calibration to predict ECe from EMCI at various depths over time. This is performed for both inversion techniques (i.e., time-lapse and individual inversions).
In the last step, RMSE, ME, and LCCC were used as indicators to evaluate the ability of the site-specific calibration equation to predict ECe.

3. Results and discussion

3.1. ECe data analysis

Initially, the third survey was chosen as a calibration dataset based on the fact that this survey included both the minimum and maximum ECe values measured across all surveys (Table 2) and it had a wide range of ECe (ECe range = 12.32 dS m−1). Moreover, the third survey was characterized by the largest number of soil samples collected during the surveys (i.e., n = 18). Table 3 summarizes descriptive statistics for the measured ECe in the calibration dataset (i.e., n = 18; the third survey). In the first layer (0.0–0.3 m), the minimum ECe was 4.63 dS m−1 which is classified as moderately saline (4–8 dS m−1). Both the mean (7.16 dS m−1) and the maximum (8.19 dS m−1) ECe values showed moderate and high salinity levels, respectively. The ECe in the second layer (0.3-0.6 m) was highly saline (8-16 dS m−1), with a minimum ECe value of 8.66 dS m−1 and a maximum of 11.50 dS m−1 and a mean of 9.96 dS m−1. In the third layer (0.6-0.9 m), the minimum ECe value was 12.95 dS m−1 and the mean was 4.92 dS m−1 indicating high salinity, while the maximum ECe value was 16.95 dS m−1 which classified as severely saline (16-32 dS m−1).
It is worth noting that the minimum ECe at all depths, excluding topsoil, indicated highly saline conditions. The higher salinity values in the study area can be attributed to climate change, leading to sea-level rise (Dawoud, 2004). Consequently, this phenomenon impacted groundwater, causing an increase in saltwater intrusion from El Manzala Lake, located near the study area (Mabrouk et al., 2013). Although Sorghum Sudanese is generally moderately salt-tolerant, the crop yield was high, which is consistent with the results of Almodares and Sharif (2007). Additionally, the extended trials conducted at the Turkmen Agricultural Institute demonstrated the feasibility of achieving a substantial Sorghum Sudanese yield, even in the presence of highly saline soils (Yollybayev and Gurbanov, 2018). Higher Sorghum Sudanese yield in the investigated plot can also be attributed to the use of organic and mineral fertilizers during the cultivation season.
Table 3 summarizes the statistics of the measured ECe in the validation dataset (i.e., n = 57). In the first layer, the minimum and the mean ECe values were 5.29 and 7.47 dS m−1, respectively, and moderately saline, while the maximum ECe value was 11.8 dS m−1 and highly saline. In the second layer, the validation ECe data had minimum, mean, and maximum ECe values of 8.00, 10.89, and 12.59 dS m−1, respectively. These values were slightly higher than the calibration data, except for the minimum. The validation ECe data for the third layer demonstrated lower minimum, mean, and maximum values in comparison to the calibration dataset.
The statistics for all layers in the calibration samples fell within the same salinity classes, with only a difference in the maximum value of the third layer. This high level of consistency enhances confidence in the calibration model’s ability to provide accurate estimations for ECe values.
By comparing the calibration and the validation datasets, it can be noted that the mean ECe value (i.e., 10.68 dS m−1) was slightly higher for the calibration dataset. The standard deviation (SD) was also slightly higher for the calibration ECe dataset and equal to 3.52 dS m−1 as compared to 2.94 dS m−1 corresponding to the validation ECe dataset, indicating greater variability in the calibration ECe data. Both the minimum and the maximum values were slightly higher for the calibration ECe dataset. Furthermore, the coefficient of variation (CV) was higher for the calibration ECe dataset and equal to 32.9% as compared to 27.9% for the validation ECe dataset, suggesting that the calibration ECe data exhibited relatively higher variability with its mean.

3.2. Determination of the optimal inversion parameters and inversion technique

After the analysis to determine the optimal parameters configuration for individual inversion and establish a calibration relationship between σ and the measured ECe, the best sets of inversion parameters were assessed. These were obtained using the S2 inversion algorithm, FS forward modeling, and λ = 0.4. The calibration equation developed from the individual inversion was as follows with an R2 value of 0.88:
ECe = 0.3084 + 0.05039 × σ
Likewise, to determine the optimal parameters configuration for time-lapse inversion and to develop a calibration relationship between σ and the measured ECe, various EMCIs were generated. These were created by using ECa data from all EMI survey dates, employing inversion algorithms (S1 and S2), and varying initial model conductivity, λ, and α values as described in Section 2.3.
Firstly, it is worth mentioning that the model derived using λ values exceeding 1 is not shown here. Using λ values higher than 1 led to a considerable misfit error between the predicted and measured ECa. Therefore, opting for a high λ is not advisable, especially when expecting sharp vertical conductivity contrasts in the field. Regarding the impact of the temporal smoothing parameter (α), various values were investigated (results not presented here). It was observed that values exceeding 0.10 overly smoothed the anticipated temporal changes, making it challenging to resolve detailed variations. Hence, for this study, a value of 0.10 was determined to be the optimal choice for α in addressing the temporal variation of σ.
Analysis for the time-lapse inversion technique was conducted using the best-selected sets of inversion parameters. These parameters were achieved when using the S2 inversion algorithm, α = 0.10, and λ = 0.90. The calibration equation developed from TL inversion was as follows with an R2 value of 0.89.
ECe = -9.1485 + 0.0910 × σ
Table 4 presents the R2 values between σ and measured ECe for both individual (IN) and time-lapse (TL) inversion techniques. Moreover, it shows the RMSE, ME, and LCCC between measured and predicted ECe for all data calculated by using site-specific calibration equations for both techniques (i.e., Eq. 3 and 4). These estimates were assessed by considering survey 3 (i.e., n = 18) as the calibration dataset and the remaining surveys (i.e., n = 57) as the validation dataset. The R2 values were strong for all surveys (i.e., R2 > 0.70) using both techniques, with R2 values of 0.81 and 0.77 for time-lapse and individual inversion, respectively. This suggests that the time-lapse technique may offer a slightly more accurate calibration for the measured ECe.
Considering the mean error (ME) between measured and predicted ECe values for all surveys, it was clear that the least biased (i.e., closest to zero) inversion technique was the time-lapse inversion as it represented a ME value of 0.17 dS m−1 compared to 0.85 dS m−1 for the individual inversion.
According to Singh et al. (2005) and Farzamian et al. (2023), a satisfactory prediction RMSE should be half the standard deviation (SD) of the measured ECe. As the SD of ECe for all the validation data was 2.94 dS m−1 as shown in Table 3, the RMSE should be 1.47 dS m−1 or lower to achieve satisfactory prediction. As the RMSE was equal to 1.45 dS m−1 in the time-lapse inversion as compared to 2.10 dS m−1 in the individual inversion, the time-lapse inversion is considered the optimal inversion technique.
The LCCC between measured and predicted ECe values for all surveys is displayed in Table 4. The analysis of LCCC reinforced the superiority of the time-lapse inversion technique, with a stronger agreement between measured and predicted ECe (LCCC = 0.90) compared to individual inversion (LCCC = 0.84), indicating the effectiveness of time-lapse inversion in making predictions. Based on the results, the time-lapse inversion is recommended compared to individual inversion as it showed improved accuracy and effectiveness in ECe prediction. Therefore, the time-lapse inversion technique was applied in this study and used for monitoring and mapping soil salinity, as shown below.

3.3. Time-lapse EMCIs

Figure 4 displays the obtained EMCIs from the time-lapse inversion of EMI surveys. The figure shows that σ increases with depth, consistent with the previously established soil salinity distribution.
Across multiple EMI surveys conducted at the same location, the temporal variations in σ were monitored for one agricultural season spanning from August 2021 to June 2022. In August 2021, σ values fluctuated between a minimum value of 138.4 mS m−1 and a maximum value of 352.5 mS m−1. This wide range indicated significant variability in σ compatible with the large measured ECe within this timeframe.
In September 2021, the σ values ranged from 142.7 to 343.7 mS m−1. The recorded σ ranges for both dates can be attributed to fertilizer application, irrigation water salinity (ECw = 4 dS m−1), and the existence of shallow saline groundwater with a level of 0.85 m depth below the soil surface and a salinity of 14 dS m−1. In arid climates and under evaporation, an important solute up-flow from the shallow and salty groundwater (capillarity rise processes) could be the main cause of soil salinization (Bouksila, 2011).
In February 2022, the maximum σ values were decreased as compared to August and September 2021. The σ values in February 2022 ranged from 142.7 to 323.7 mS m−1. April 2022 exhibited a similar pattern, with σ values ranging from 145.4 to 318.1 mS m−1. This reduction in maximum σ values can be attributed to the winter rains as shown in Figure 2. This resulted in a corresponding decrease in measured ECe which was agreed with the reduction in σ values in the subsoil as shown in Figure 4. In June 2022, a slight increase in minimum σ values was noted and the σ values ranged from 152.17 to 322.21 mS m−1.
Finally, in April 2023, another agricultural season one year later, σ values ranged from 157.0 to 329.8 mS m−1, maintaining an almost consistent pattern as observed during the antecedent four surveys. This pattern involves a decrease in σ values within the subsoil as compared to February 2022 as shown in Figure 4. This trend was also reflected in the measured ECe values at the subsoil. The ECe values decreased in the subsoil. The reduction in σ and measured ECe values within the subsoil can be attributed to the decrease in irrigation water salinity due to the canal lining and the continued cultivation of Sudanese Sorghum. Although the decrease in irrigation water salinity resulted in a reduction of measured ECe within the topsoil layer during the last survey, no corresponding decrease was observed in σ. This can be attributed to the lower sensitivity of the CMD2 to soil salinity within the root zone due to the existence of shallow groundwater which considerably affects the readings. Consequently, the ability to predict salinity from σ in the topsoil was diminished, as discussed later in Section 3.5.

3.4. Prediction of ECe using site-specific calibration.

Figure 5a displays the calibration relationship between ECe and σ using the calibration equation developed from survey 3 using the time-lapse technique and the best inversion parameters (i.e., S2 inversion algorithm, α = 0.10, and λ = 0.90). The best LR equation gave R2 equal to 0.89 expressed as:
ECe = -9.1485 + 0.0910 × σ
Figure 5b shows the predicted ECe plotted versus the measured ECe and the 1:1 line using the calibration equation developed using the time-lapse for different measurement dates and different measurement depths.
The validation of the calibration equation using time-lapse inversion resulted in an RMSE of 1.45 dS m−1 and a strong R2 of 0.79, indicating acceptable predictive capability given the wide ECe range (10.41 dS m−1). These results concur with the findings of Zare et al. (2015) who observed a larger RMSE (5.28 dS m−1) in an irrigated cotton field under a wider ECe range (68.4 dS m−1). However, in the current study, the predictive bias (ME) was small and equal to 0.24 dS m−1 indicating a slight tendency for overestimation of ECe. This level of bias was slightly higher than the (ME) reported by Zare et al. (2015) which was 0.03 dS m−1. However, it can be considered well within acceptable limits. A high LCCC of 0.88 indicates a strong agreement between measured and predicted ECe during validation.
Figure 5b displays variations in the predictive accuracy of validation data for all EMI survey dates. The Figure showed that the predicted ECe was generally slightly overestimated in the subsoil, particularly in April 2022. This result is unsurprising, given that the soil in April 2022 had the lowest maximum measured ECe among other survey dates as shown in Table 2.
In contrast, the predicted ECe in the subsurface was generally slightly underestimated except for April 2023. This discrepancy is likely due to April 2023 having the lowest ECe among other survey dates. The greater variations in measured ECe during survey 3 had a considerable impact on the calibration equation, limiting its ability to monitor the smaller variations observed in April 2022 and 2023. For the topsoil layer across all observation dates, the data points exhibit a dispersed distribution around the unity line (Figure 5b).
Generally, the statistical indicators presented in Table 4 for validation data, segregated by measurement date, indicate that the prediction ability remained relatively consistent across all five dates. These results indicate that the spatial variability of the data had a much stronger influence on the prediction ability of the site calibration than the temporal variability. To improve the spatial sensitivity of the site calibration, observation times can be extended, a number of EMI surveys can be also increased, and a different EM device with a shallow effective depth like the CMD mini explorer can be used. Paz et al. (2020b) recommended soil sampling and continuous monitoring of volumetric water content (θ), soil temperature, groundwater level, and salinity to improve the spatial sensitivity of the site calibration.

3.5. Generation of soil salinity cross sections from time-lapse EMC

Figure 6 shows the soil salinity cross sections for each EMI survey date. Consequently, the figure tracks the salinity dynamics over time. The cross-sections were generated by using the site-specific calibration equation to predict ECe, which was classified into three salinity classes ranging from moderately to severely saline. The measured ECe for each EMI survey’s sampling sites and groundwater level are also displayed.
The salinity cross sections revealed that the salinity levels tended to increase with depth starting from a moderately saline level in the topsoil to a highly saline level in the subsoil. Severe saline zones were found at the bottom of the subsoil layer across every surveyed date. These saline zones overestimated soil salinity compared to measured ECe values. The observed overestimation can be attributed to the proximity of groundwater to the root zone. Shallow groundwater can affect CMD2 measurements because water is a significantly better conductor of electricity than soil. Consequently, this situation can lead to an overestimation of soil salinity. Furthermore, the elevated ECgr reached a high saline level of 14 dS m−1 during the dry period of the year (i.e., summer of 2021). Higher groundwater salinity levels can be attributed to the absence of rainfall and the use of moderately saline irrigation water. However, the degree of salinity overestimation tended to subside during the winter surveys, due to rainfall which reduced the groundwater salinity to a relatively lower level (ECgr = 10 dS m−1). Figure 6 depicts soil salinity fluctuations for all surveyed dates. Soil salinity at the subsurface layer was accurately predicted in most sampling sites on all dates. In contrast, soil salinity predictions for the topsoil tended to be slightly overestimated.
During September 2021, the salinity cross-section revealed a larger portion of high salinity within the topsoil layer. This increase can be primarily attributed to the application of fertigation practices and the use of saline irrigation water (ECw of 4.0 dS m−1). Subsequently, with the arrival of the winter and rainy seasons which represented 66% of the year 2021 total annual rainfall, the salinity levels decreased. In February and April 2022, many soil sample sites had reached a moderately saline level as indicated by the measured ECe. It is worth mentioning that the initiation of canal lining in January 2022 led to a considerable reduction in irrigation water salinity. The salinity of irrigation water decreased to 1.0 dS m−1 by April 2023. This reduction in water salinity notably contributed to lower soil salinity levels. In April 2023, the measured ECe in all sampling sites within the top and the subsurface soils showed a moderate saline level (ECe = 4-8 dS m−1). However, the salinity cross sections for the topsoil estimated by CMD2 did not accurately reveal changes in soil salinity and tended to be overestimated. This inconsistency may be attributed to the weak correlation between the responses of the CMD2 device and the topsoil layer as demonstrated in similar results obtained with an EMI device having an effective depth of 2.0 m (Khongnawang et al., 2020). The CMD2 has an effective depth of 1.5 m for horizontal (ECah) and 3 m for vertical (ECav) receiver arrays according to the manufacturer’s guide. This exploration depth was significantly larger than the depth of topsoil, which makes it challenging to resolve ECe changes in the topsoil (e.g., Ramos et al., 2023). In addition, a larger variability of other soil properties (e.g., water content and temperature) and smaller variability of ECe at topsoil further limits the application of EMI sensors in monitoring ECe in this zone (Paz et al., 2020b; Dragonetti et al., 2022).
The adoption of Sorghum Sudanese cultivation may be another factor for the occurring soil salinity reduction during the successive surveys. Mirsharipova et al. (2023) stated that the Sorghum Sudanese can absorb salts from the soil and thereby decrease the soil salinity. The yield of Sorghum Sudanese in the investigated agricultural plot increased by more than 10% during year 2023 which can be attributed to the reduction in soil salinity.
Considering the circumstances of elevated initial soil salinity, active water movement, salt conveyance, and the presence of a shallow groundwater table, salts migrated upward through capillary action and accumulated within the root zone. This phenomenon was particularly notable during the dry season of summer 2021. However, the use of slightly saline irrigation water (after canal lining) led to a gradual reduction in soil salinity, particularly within the topsoil, proven by subsequent EMI survey dates.
Using the information obtained from time-lapse spatial distribution maps, the classification of soil salinity becomes a valuable tool for rational crop allocation within the investigated agricultural field. The primary objective of this allocation strategy is to effectively maximize crop yield in salt-affected soils. This approach involves the cultivation of crops renowned for their high salt tolerance, such as cotton, barley, and sorghum, even in soils exhibiting moderate to heavy salinity levels. The results of the current study can help local farmers monitor soil salinity more accurately, allowing them to develop customized soil management strategies and make informed decisions in agriculture.
Challenges in generating soil salinity predictive maps in field-scale were faced due to the following reasons: i) Lack of repeated field-scale EMI surveys across the entire field; only having data from a single measurement. ii) Absence of soil sample collection on the day of the field-scale EMI survey, preventing the upscaling of our approach for predictive maps, even on that single day. iii) Limited EMI measurements in a single orientation during the field-scale investigation, rendering them unsuitable for inversion modeling that was presented for quantitative investigation.

4. Conclusions

This research used data from EMI surveys and soil sampling data collected between August 2021 and April 2023. These datasets, combined with a site-specific calibration, allowed for estimating soil salinity distribution through a 20 m transect within an intensely used agricultural plot. A preliminary analysis of soil salinity dynamics throughout the investigated transect was performed. Site-specific calibration demonstrated consistent prediction accuracy over time. The results supported the suitability of the CMD2 device for monitoring soil salinity across different soil depths with a limitation in measuring accuracy in only the topsoil layer (0–0.3 m). Therefore, sensors like the CMD mini explorer may be employed for effective monitoring of topsoil salinity.
Using time-lapse (EMCI) in combination with calibration to analyze the temporal changes in soil salinity is an evolving approach. The developed salinity cross-sections showed how soil salinity responds to inputs such as salts and water, whether through irrigation, rainfall, or changes in shallow groundwater salinity. This method can assess suitable agricultural management practices for salinity reduction. For instance, the results indicated that the cultivation of Sudanese Sorghum caused a reduction in soil salinity. Canal lining as well was another reason for the reduction in soil salinity due to its direct impact on ECw.
The study underscores the positive impact of water management techniques, with a particular emphasis on the Egyptian Government’s canal lining method. This intervention effectively reduced the salinity of irrigation water (ECw) from 4.00 to 1.00 dS m−1, leading to a reduction in the risk level of soil salinization. These results underscore the importance of coordinated efforts at the irrigation systems level to address salinity issues. It also suggests that similar initiatives in other regions facing salinity challenges could yield considerable agricultural benefits.
Addressing irrigation water and soil salinity management challenges is often complex, particularly at field and large scales. EMI method offers a non-invasive, rapid, and cost-effective solution for soil salinity dynamic monitoring that can cover expansive areas quickly. Although our study focused on a small transect, there is potential for scaling up from small-scale to field assessments due to the method’s ability for rapid data collection. Our attempt is now towards securing additional funding to facilitate this undertaking in the near future. It’s important to highlight that such applications have been very rare in Egypt, despite many lands facing issues with soil salinity and degradation. We consider this study as a proof of concept to demonstrate how EMI can be applied in this region for quantitative monitoring of soil salinity. This lays the foundation for further research to explore the efficacy of EMI sensors and incorporate them into broader 4D investigations for monitoring soil salinity in filed and large scales.

Author Contributions

Conceptualization, Mohamed Eltarabily, Mohammad Farzamian, Mohamed Elkiki and Tarek Selim; Data curation, Abdulrahman Amer and Mohammad Farzamian; Formal analysis, Mohamed Eltarabily, Abdulrahman Amer, Mohammad Farzamian and Tarek Selim; Funding acquisition, Mohamed Eltarabily, Mohamed Elkiki and Tarek Selim; Investigation, Mohamed Eltarabily, Abdulrahman Amer and Tarek Selim; Methodology, Mohamed Eltarabily, Abdulrahman Amer, Mohammad Farzamian, Fethi Bouksila, Mohamed Elkiki and Tarek Selim; Project administration, Mohamed Eltarabily, Mohamed Elkiki and Tarek Selim; Resources, Mohamed Elkiki and Tarek Selim; Software, Abdulrahman Amer and Mohammad Farzamian; Supervision, Mohamed Eltarabily, Mohammad Farzamian, Mohamed Elkiki and Tarek Selim; Validation, Abdulrahman Amer and Mohammad Farzamian; Visualization, Mohamed Eltarabily, Mohammad Farzamian, Mohamed Elkiki and Tarek Selim; Writing – original draft, Abdulrahman Amer, Mohammad Farzamian and Tarek Selim; Writing – review & editing, Mohamed Eltarabily, Mohammad Farzamian, Fethi Bouksila, Mohamed Elkiki and Tarek Selim.

Funding

This research did not receive funding from specific agencies in the public, commercial, or not-for-profit divisions.

Data Availability Statement

The data presented in this study is available on request from the corresponding author.

Acknowledgments

The authors acknowledge the partial funding of this study from the Egyptian Academy of Scientific Research and Technology through the SALTFREE project (ARIMNET2/0005/2015, Coordination of Agricultural Research in the Mediterranean Area) grant agreement N 618127.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. a) Location of the study area in southern Port Said City and b) location of the plot (CMD2 transect marked in orange lines and soil sampling sites in black dots) and the location of the drainage water and irrigation water system (irrigation and drainage direction marked in blue and green lines, respectively.
Figure 1. a) Location of the study area in southern Port Said City and b) location of the plot (CMD2 transect marked in orange lines and soil sampling sites in black dots) and the location of the drainage water and irrigation water system (irrigation and drainage direction marked in blue and green lines, respectively.
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Figure 2. Distribution of daily rainfall (mm/day), and mean temperature (C°/day) recorded at the meteorological station located in the study area during the study period, including dates of irrigation events, the corresponding irrigation water amounts (mm/day), and EMI survey dates.
Figure 2. Distribution of daily rainfall (mm/day), and mean temperature (C°/day) recorded at the meteorological station located in the study area during the study period, including dates of irrigation events, the corresponding irrigation water amounts (mm/day), and EMI survey dates.
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Figure 3. The spatial distribution of apparent electrical conductivity (ECa, mS m-1) of CMD2 data obtained from a pilot survey using the horizontal dipole (ECah) orientation at a height of 1.00 m.
Figure 3. The spatial distribution of apparent electrical conductivity (ECa, mS m-1) of CMD2 data obtained from a pilot survey using the horizontal dipole (ECah) orientation at a height of 1.00 m.
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Figure 4. Time-lapse electromagnetic conductivity images (EMCIs) for the experimental plot.
Figure 4. Time-lapse electromagnetic conductivity images (EMCIs) for the experimental plot.
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Figure 5. Plots of (a) soil electrical conductivity (σ, mS m−1) and the measured electrical conductivity of the saturated soil paste extract (ECe, dS m−1) and the linear regression (LR) of the calibration equation using survey # 3 and Time-lapse inversion; (b) validation results of the LR calibration equation using other surveys excluding survey # 3.
Figure 5. Plots of (a) soil electrical conductivity (σ, mS m−1) and the measured electrical conductivity of the saturated soil paste extract (ECe, dS m−1) and the linear regression (LR) of the calibration equation using survey # 3 and Time-lapse inversion; (b) validation results of the LR calibration equation using other surveys excluding survey # 3.
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Figure 6. Predicted soil salinity maps along the vertical transects for plot with representation of measured ECe (in circles), and groundwater level (blue triangles) at the sampling sites located in the middle of each transect for all surveying campaigns.
Figure 6. Predicted soil salinity maps along the vertical transects for plot with representation of measured ECe (in circles), and groundwater level (blue triangles) at the sampling sites located in the middle of each transect for all surveying campaigns.
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Table 1. Detailed information regarding the dates of measurements, agricultural state, number of drilled boreholes in each survey, and survey utilization.
Table 1. Detailed information regarding the dates of measurements, agricultural state, number of drilled boreholes in each survey, and survey utilization.
Campaign Date of measurement State of agriculture Number of boreholes Survey data utilization
1st Aug 2021 Before cultivation 1 Validation
2nd Sep 2021 After fertilization and sowing 2
3rd Feb 2022 After second harvesting 6 Calibration
4th April 2022 After fourth harvesting 6 Validation
5th June 2022 After the last harvesting 6
6th June 2023 One year later 4
Table 2. Measured ECe for each surveying campaign, all surveys, and validation surveys.
Table 2. Measured ECe for each surveying campaign, all surveys, and validation surveys.
Surveys ECe min
(dS m−1)		 ECe max
(dS m−1)		 ECe range* Number of soil samples
1st survey 5.29 15.24 9.95 3
2nd survey 9.51 15.70 6.19 6
3rd (calibration) survey 4.63 16.95 12.32 18
4th survey 5.45 13.58 8.13 18
5th survey 6.10 15.16 9.06 18
6th survey 6.40 14.90 8.50 12
All surveys 4.63 16.95 12.32 75
Validation surveys (all surveys except the 3rd survey) 5.29 15.70 10.41 57
* ECe range = ECe max – ECe min.
Table 3. Descriptive statistics of the measured electrical conductivity of the saturated soil paste extract (ECe in dS m−1) at various depths including topsoil (0.0–0.3 m), subsurface (0.3–0.6 m), and subsoil (0.6–0.9 m) at calibration (n = 18) and validation (n = 57) soil samples. SD and Cv are the standard deviation and variation coefficient, respectively.
Table 3. Descriptive statistics of the measured electrical conductivity of the saturated soil paste extract (ECe in dS m−1) at various depths including topsoil (0.0–0.3 m), subsurface (0.3–0.6 m), and subsoil (0.6–0.9 m) at calibration (n = 18) and validation (n = 57) soil samples. SD and Cv are the standard deviation and variation coefficient, respectively.
ECe Calibration data (dS m−1)
Soil layer (m) N Min Max Mean SD Cv
All soil layers 18 4.63 16.95 10.68 3.52 32.92
0.0-0.3 6 4.63 8.19 7.16 1.37 19.11
0.3-0.6 6 8.66 11.50 9.96 1.06 10.65
0.6-0.9 6 12.95 16.95 14.92 1.39 9.30
ECe Validation data (dS m−1)
Soil layer (m) N Min Max Mean SD Cv
All soil layers 57 5.29 15.70 10.55 2.94 27.87
0.0-0.3 19 5.29 11.80 7.47 1.82 24.39
0.3-0.6 19 8.00 12.59 10.89 1.55 14.23
0.6-0.9 19 10.90 15.70 13.38 1.46 10.91
Table 4. Statistical indicators for measured ECe: RMSE, ME, LCCC, R2 for all data types using both individual (IN) and time-lapse (TL) inversion techniques.
Table 4. Statistical indicators for measured ECe: RMSE, ME, LCCC, R2 for all data types using both individual (IN) and time-lapse (TL) inversion techniques.
Surveys Type of inversion RMSE
(dS m−1)		 ME
(dS m−1)		 Lin’s CCC R2
All surveys IN 1.91 0.85 0.84 0.77
TL 1.38 0.17 0.90 0.81
Validation surveys (all surveys except 3rd survey) IN 2.10 1.15 0.81 0.77
TL 1.45 0.24 0.88 0.79
1st survey IN 1.48 0.04 0.93 0.87
TL 1.52 0.95 0.92 0.93
2nd survey IN 3.65 3.48 0.55 0.96
TL 1.24 -0.26 0.90 0.92
3rd (calibration) survey IN 1.15 0.00 0.94 0.88
TL 1.14 0.00 0.94 0.89
4th survey IN 1.94 0.63 0.79 0.69
TL 1.58 0.06 0.84 0.73
5th survey IN 1.62 0.57 0.86 0.79
TL 1.35 -0.07 0.89 0.80
6th survey IN 1.96 1.81 0.81 0.94
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