Using Apparent Electrical Conductivity as Indicator for Investigating Potential Spatial Variation of Soil Salinity across Seven Oases along Tarim River in Southern Xinjiang, China

Soil salinization is a major soil health issue globally. Over the past 40 years, extreme weather and increasing human activity have profoundly changed the spatial distribution of land use and water resources across seven oases in southern Xinjiang, China. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since a land survey in the 1970s to 1980s (the harmonized world soil database, HWSD) due to scarce observational data. Now, given the uncertainty raised by near future climate change, it is important to develop quick, reliable and accurate estimates of soil salinity at larger scales for a better manage strategy to the local fragile ecosystem that with limited land and water resources. This study collected electromagnetic induction (EMI) readings near surface soil to update on the spatial distribution and changes of water and salt in the region and to map apparent electrical conductivity (ECa, mS·m−1), in four coil configurations: vertical dipole in 1.50 m (ECav01) and 0.75 m (ECav05), so as the horizontal dipole in 0.75 m (ECah01) and 0.37 m (ECah05), then all the ECa coil configurations were modeled with random forest algorithm. The validation results showed an R2 range of 0.77–0.84 and an RMSE range of 115.17–142.76 mS·m−1. The validation accuracy of deep ECa dipole (ECah01, ECav05, and ECav01) was greater than that of shallow ECa (ECah05), as the former integrated a thicker portion of the subsurface. The range of EC spatial variability that can be explained by ECa is 0.19–0.36 (farmland, mean value is 0.28), grassland is 0.16–0.49 (shrub/grassland, mean value is 0.34), and bare land is 0.28–0.70 (bare land, mean value is 0.56). Among them, ECav01 has the best predictive ability. As the depth increased, the influence of soil-related variables decreased, and the contribution of climate-related variables increased. The main factor affecting ECa variation was climate-related variables, followed by vegetation-related variables and soil-related variables. Scatter plot show ECa was significantly correlated with ECe_HWSD_030 (0–30 cm, r = 0.482, p < 0.01) and ECe_HWSD_30100 (30–100 cm, r = 0.556, p < 0.01). The predicted spatial ECa maps were similar to the ECe values from HWSD, but also implies that the distribution of soil water and salt has undergone tremendous changes since 1980s. The study demonstrates that EMI data provide a reliable and cost-effective tool for obtaining high-resolution soil maps that can be used for better land evaluation and soil improvement at larger scales.

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

Remote Sensing ◽

10.3390/rs13101875 ◽

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.

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Soil salinization under different climate change scenarios: A global scale analysis

10.5194/egusphere-egu21-13668 ◽

2021 ◽

Author(s):

Nima Shokri ◽

Amirhossein Hassani ◽

Adisa Azapagic

Keyword(s):

Climate Change ◽

Water Resources ◽

Soil Salinity ◽

Global Scale ◽

Soil Salinization ◽

Water Evaporation ◽

Climate Change Scenarios ◽

Spatio Temporal ◽

Salt Affected Soils

Population growth and climate change is projected to increase the pressure on land and water resources, especially in arid and semi-arid regions. This pressure is expected to affect all driving mechanisms of soil salinization comprising alteration in soil hydrological balance, sea salt intrusion, wet/dry deposition of wind-born saline aerosols &#8212; leading to an increase in soil salinity. Soil salinity influences soil stability, bio-diversity, ecosystem functioning and soil water evaporation (1). It can be a long-term threat to agricultural activities and food security. To devise sustainable action plan investments and policy interventions, it is crucial to know when and where salt-affected soils occur. However, current estimates on spatio-temporal variability of salt-affected soils are majorly localized and future projections in response to climate change are rare. Using Machine Learning (ML) algorithms, we related the available measured soil salinity values (represented by electrical conductivity of the saturated paste soil extract, ECe) to some environmental information (or predictors including outputs of Global Circulation Models, soil, crop, topographic, climatic, vegetative, and landscape properties of the sampling locations) to develop a set of data-driven predictive tools to enable the spatio-temporal predictions of soil salinity. The outputs of these tools helped us to estimate the extent and severity of the soil salinity under current and future climatic patterns at different geographical levels and identify the salinization hotspots by the end of the 21st century in response to climate change. Our analysis suggests that a soil area of 11.73 Mkm2 located in non-frigid zones has been salt-affected in at least three-fourths of the 1980 - 2018 period (2). At the country level, Brazil, Peru, Sudan, Colombia, and Namibia were estimated to have the highest rates of annual increase in the total area of soils with an ECe &#8805; 4 dS m-1. Additionally, the results indicate that by the end of the 21st century, drylands of South America, southern and Western Australia, Mexico, southwest United States, and South Africa will be the salinization hotspots (compared to the 1961 - 1990 period). The results of this study could inform decision-making and contribute to attaining the United Nation&#8217;s Sustainable Development Goals for land and water resources management.1. Shokri-Kuehni, S.M.S., Raaijmakers, B., Kurz, T., Or, D., Helmig, R., Shokri, N. (2020). Water Table Depth and Soil Salinization: From Pore-Scale Processes to Field-Scale Responses. Water Resour. Res., 56, e2019WR026707. https://doi.org/ 10.1029/2019WR0267072. Hassani, A., Azapagic, A., Shokri, N. (2020). Predicting Long-term Dynamics of Soil Salinity and Sodicity on a Global Scale, Proc. Nat. Acad. Sci., 117, 52, 33017&#8211;33027. https://doi.org/10.1073/pnas.2013771117

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Relationship between Soil Salinization and Groundwater Hydration in Yaoba Oasis, Northwest China

Water ◽

10.3390/w11010175 ◽

2019 ◽

Vol 11 (1) ◽

pp. 175 ◽

Cited By ~ 6

Author(s):

Guoqing Cui ◽

Yudong Lu ◽

Ce Zheng ◽

Zhiheng Liu ◽

Jiamei Sai

Keyword(s):

Water Quality ◽

Spatial Distribution ◽

Soil Salinity ◽

Irrigation Water ◽

Surface Soil ◽

Soil Salinization ◽

Irrigation Water Quality ◽

Ion Content ◽

Soil Salt ◽

The Relationship

Precipitation is scarce and evaporation is intense in desert areas. Groundwater is used as the main water source to develop agriculture in the oases. However, the effects of using groundwater on the ecological environment elicit widespread public concern. This study investigated the relationship between soil salinity and groundwater characteristics in Yaoba Oasis through in situ experiments. The relationship of the mineral content, pH, and main ion content of groundwater with soil salt was quantitatively evaluated through a gray relational analysis. Four main results were obtained. First, the fresh water area with low total dissolved solid (TDS) was usually HCO3− or SO42− type water, and salt water was mostly Cl− and SO42−. The spatial distribution of main ions in groundwater during winter irrigation in November was basically consistent with that during spring irrigation in June. However, the spatial distribution of TDS differed in the two seasons. Second, soil salinization in the study area was severe, and the salinization rate reached 72.7%. In this work, the spatial variability of soil salinization had a relatively large value, and the values in spring were greater than those in autumn. Third, the soil in the irrigated area had a high salt content, and the salt ion content of surface soil was higher than that of subsoil. A piper trilinear diagram revealed that Ca2+ and K+ + Na+ were the main cations. SO42−, Cl−, and HCO3− were the main anions, and salinization soil mainly contained SO42−. Fourth, the changes in soil salt and ion contents in the 0–10 cm soil layer were approximately similar to those of irrigation water quality, both of which showed an increasing trend. The correlation of surface soil salinity with the salinity of groundwater and its chemical components was high. In summary, this study identified the progress of irrigation water quality in soil salinization and provided a scientific basis for improving the oasis ecosystem, maintaining the healthy development of agriculture, managing oasis water resources, and policy development. Our findings can serve as a reference for other, similar oasis research.

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Electromagnetic Induction Measurements for Investigating Soil Salinization Caused by Saline Reclaimed Water

Atmosphere ◽

10.3390/atmos13010073 ◽

2021 ◽

Vol 13 (1) ◽

pp. 73

Author(s):

Lorenzo De Carlo ◽

Gaetano Alessandro Vivaldi ◽

Maria Clementina Caputo

Keyword(s):

Electrical Conductivity ◽

Soil Salinity ◽

Electromagnetic Induction ◽

Reclaimed Water ◽

Soil Salinization ◽

Repeated Measurements ◽

Soil Electrical Conductivity ◽

Geophysical Research ◽

Soil Response ◽

Short Period

This paper focused on the use of electromagnetic induction measurements in order to investigate soil salinization caused by irrigation with saline reclaimed water. An experimental activity was carried out during the growing season of tomato crop in order to evaluate expected soil salinization effects caused by different saline agro-industrial wastewaters used as irrigation sources. Soil electrical conductivity, strictly related to the soil salinity, has been monitored for three months by means of Electromagnetic Induction (EMI) measurements, and evident differences in the soil response have been observed. The study highlighted two aspects that can improve soil investigation due to the utilization of geophysical tools. First, EMI data can map large areas in a short period of time with an unprecedented level of detail by overcoming practical difficulties in order to massively sample soil. At the same time, repeated measurements over time allow updating real-time soil salinity maps by using accurate correlations with soil electrical conductivity. This application points out how integrated agro-geophysical research approaches can play a strategic role in agricultural saline water management in order to prevent soil salinization risks in medium to long-term periods.

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Assessing the dynamics of soil salinity with time-lapse inversion of electromagnetic data guided by hydrological modelling

Hydrology and Earth System Sciences ◽

10.5194/hess-25-1509-2021 ◽

2021 ◽

Vol 25 (3) ◽

pp. 1509-1527

Author(s):

Mohammad Farzamian ◽

Dario Autovino ◽

Angelo Basile ◽

Roberto De Mascellis ◽

Giovanna Dragonetti ◽

...

Keyword(s):

Electrical Conductivity ◽

Soil Salinity ◽

Electromagnetic Induction ◽

Irrigation System ◽

Time Lapse ◽

Hydrological Modelling ◽

Irrigated Agriculture ◽

Field Assessment ◽

Apparent Electrical Conductivity ◽

Spatio Temporal

Abstract. Irrigated agriculture is threatened by soil salinity in numerous arid and semi-arid areas of the world, chiefly caused by the use of highly salinity irrigation water, compounded by excessive evapotranspiration. Given this threat, efficient field assessment methods are needed to monitor the dynamics of soil salinity in salt-affected irrigated lands and evaluate the performance of management strategies. In this study, we report on the results of an irrigation experiment with the main objective of evaluating time-lapse inversion of electromagnetic induction (EMI) data and hydrological modelling in field assessment of soil salinity dynamics. Four experimental plots were established and irrigated 12 times during a 2-month period, with water at four different salinity levels (1, 4, 8 and 12 dS m−1) using a drip irrigation system. Time-lapse apparent electrical conductivity (σa) data were collected four times during the experiment period using the CMD Mini-Explorer. Prior to inversion of time-lapse σa data, a numerical experiment was performed by 2D simulations of the water and solute infiltration and redistribution process in synthetic transects, generated by using the statistical distribution of the hydraulic properties in the study area. These simulations gave known spatio-temporal distribution of water contents and solute concentrations and thus of bulk electrical conductivity (σb), which in turn were used to obtain known structures of apparent electrical conductivity, σa. These synthetic distributions were used for a preliminary understanding of how the physical context may influence the EMI-based σa readings carried out in the monitored transects as well as being used to optimize the smoothing parameter to be used in the inversion of σa readings. With this prior information at hand, we inverted the time-lapse field σa data and interpreted the results in terms of concentration distributions over time. The proposed approach, using preliminary hydrological simulations to understand the potential role of the variability of the physical system to be monitored by EMI, may actually allow for a better choice of the inversion parameters and interpretation of EMI readings, thus increasing the potentiality of using the electromagnetic induction technique for rapid and non-invasive investigation of spatio-temporal variability in soil salinity over large areas.

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Assessing soil salinity dynamics using time-lapse electromagnetic conductivity imaging

SOIL ◽

10.5194/soil-6-499-2020 ◽

2020 ◽

Vol 6 (2) ◽

pp. 499-511

Author(s):

Maria Catarina Paz ◽

Mohammad Farzamian ◽

Ana Marta Paz ◽

Nádia Luísa Castanheira ◽

Maria Conceição Gonçalves ◽

...

Keyword(s):

Electrical Conductivity ◽

Cross Sections ◽

Soil Salinity ◽

Sea Water ◽

Management Strategies ◽

Time Lapse ◽

Soil Salinization ◽

Agricultural System ◽

Prediction Ability ◽

Conductivity Imaging

Abstract. 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 (ECa, mS m−1); (2) inversion of ECa 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 (ECe, 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 ECe 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 ECe 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.

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Monitoring soil salinity using time-lapse electromagnetic conductivity imaging

10.5194/soil-2019-99 ◽

2020 ◽

Author(s):

Maria Catarina Paz ◽

Mohammad Farzamian ◽

Ana Marta Paz ◽

Nádia Luísa Castanheira ◽

Maria Conceição Gonçalves ◽

...

Keyword(s):

Electrical Conductivity ◽

Soil Salinity ◽

Management Strategies ◽

Time Lapse ◽

Soil Salinization ◽

Spatiotemporal Variability ◽

Coefficient Of Determination ◽

Agricultural System ◽

Soil Electrical Conductivity ◽

Salinity Levels

Abstract. Lezíria Grande of Vila Franca de Xira, located in Portugal, is an important agricultural system where soil faces the risk of salinization, being thus prone to desertification and land abandonment. 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 (ECa, dS m−1); (2) inversion of ECa to obtain electromagnetic conductivity images (EMCI) which provide 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 (ECe, dS m−1), used as a proxy for soil salinity; and (4) conversion of EMCI into salinity maps 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 ECe from EMCI. This study aims to evaluate the potential of time-lapse EMCI and the regional calibration to predict the spatiotemporal variability of soil salinity in the study area. The results showed that ECe was satisfactorily predicted, with a root mean square error (RMSE) of 3.22 dS m−1 in a range of 52.35 dS m−1 and a coefficient of determination (R2) of 0.89. Results also showed strong concordance with a Lin’s concordance correlation coefficient (CCC) of 0.93, although, ECe was slightly overestimated with a mean error (ME) of −1.30 dS m−1. Soil salinity maps for each location revealed salinity fluctuations related to the input of salts and water either through irrigation, precipitation or groundwater level and salinity. Time-lapse EMCI has proven to be a valid methodology for evaluating the risk of soil salinization, and 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.

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Comparative regional-scale soil salinity assessment with near-ground apparent electrical conductivity and remote sensing canopy reflectance

Ecological Indicators ◽

10.1016/j.ecolind.2016.06.015 ◽

2016 ◽

Vol 70 ◽

pp. 276-284 ◽

Cited By ~ 37

Author(s):

Elia Scudiero ◽

Todd H. Skaggs ◽

Dennis L. Corwin

Keyword(s):

Remote Sensing ◽

Electrical Conductivity ◽

Soil Salinity ◽

Regional Scale ◽

Canopy Reflectance ◽

Apparent Electrical Conductivity

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Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

Remote Sensing ◽

10.3390/rs11070736 ◽

2019 ◽

Vol 11 (7) ◽

pp. 736 ◽

Cited By ~ 19

Author(s):

Jie Hu ◽

Jie Peng ◽

Yin Zhou ◽

Dongyun Xu ◽

Ruiying Zhao ◽

...

Keyword(s):

Electrical Conductivity ◽

Soil Salinity ◽

Quantitative Estimation ◽

Arable Land ◽

Cost Effective ◽

Soil Salinization ◽

Soil Samples ◽

Soil Reclamation ◽

Field Scale ◽

Sparse Vegetation

Soil salinization is a global issue resulting in soil degradation, arable land loss and ecological environmental deterioration. Over the decades, multispectral and hyperspectral remote sensing have enabled efficient and cost-effective monitoring of salt-affected soils. However, the potential of hyperspectral sensors installed on an unmanned aerial vehicle (UAV) to estimate and map soil salinity has not been thoroughly explored. This study quantitatively characterized and estimated field-scale soil salinity using an electromagnetic induction (EMI) equipment and a hyperspectral camera installed on a UAV platform. In addition, 30 soil samples (0~20 cm) were collected in each field for the lab measurements of electrical conductivity. First, the apparent electrical conductivity (ECa) values measured by EMI were calibrated using the lab measured electrical conductivity derived from soil samples based on empirical line method. Second, the soil salinity was quantitatively estimated using the random forest (RF) regression method based on the reflectance factors of UAV hyperspectral images and satellite multispectral data. The performance of models was assessed by Lin’s concordance coefficient (CC), ratio of performance to deviation (RPD), and root mean square error (RMSE). Finally, the soil salinity of three study fields with different land cover were mapped. The results showed that bare land (field A) exhibited the most severe salinity, followed by dense vegetation area (field C) and sparse vegetation area (field B). The predictive models using UAV data outperformed those derived from GF-2 data with lower RMSE, higher CC and RPD values, and the most accurate UAV-derived model was developed using 62 hyperspectral bands of the image of the field A with the RMSE, CC, and RPD values of 1.40 dS m−1, 0.94, and 2.98, respectively. Our results indicated that UAV-borne hyperspectral imager is a useful tool for field-scale soil salinity monitoring and mapping. With the help of the EMI technique, quantitative estimation of surface soil salinity is critical to decision-making in arid land management and saline soil reclamation.

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