Soil Salinity Prediction and Its Severity Mapping Using a Suitable Interpolation Method on Data Collected by Electromagnetic Induction Method (original) (raw)
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Spatial Assessment of Soil Salinity by Electromagnetic Induction Survey
Environmental Engineering and Management Journal
Salinization occurs in natural conditions as a result of a complex of factors such as climate, topography, and hydrogeology. Salinity principally occurs in sub-humid to arid regions but secondary salinization is a consequence of direct human activities it extends by the day. In the field, soil salinity is deduced from apparent electrical conductivity (ECa) by using a range of devices. Although a number of proximal sensors have recently been used worldwide to simplify fieldwork, few studies using new technologies have been addressed in Romania. The objective of this study was to assess the spatial variability of the apparent electrical conductivity of saline soils using a DUALEM instrument in Valea Sărată (Cluj). Spatial variability maps were generated by using of a geostatistical method. Significantly higher ECa was detected in poorly drained areas close to water channels (ECa above 1000 mS/m), while lower and less variable ECa values were recorded on the side slopes (ECa˂200 mS/m). These areas correspond with eutric salic regosol identified on upper lands. The map of ECa measurements at surface show a higher variability of salinity then at depth at which the ground water disolved the salts. The instrument proved to be more efficient compared with traditional methods, regarding soil salinity mapping and delineating the soil boundaries.
Developments in Soil Salinity Assessment and Reclamation, 2012
The knowledge about the magnitude, the spatial extent, the distribution and the evolution of salinity over a period of time is essential for the better management of salt-affected soils. Soil salinity is determined, conventionally, by measuring the electrical conductivity of a saturated past extract (ECe). However, given the spatiotemporal variability of salinity, numerous samples are necessary, which makes the conventional procedure laborious and expensive. As an alternative, the apparent electrical conductivity of soil (ECa) can be measured in the fi eld by the use of the electromagnetic induction (EMI) method. This method is fast and allows making extensive ECa determination in space and monitoring. In the present study, an area of 2,060 ha has been investigated in the irrigation district of Tadla, central Morocco. Twelve soil samples were collected for ECe measurement, while 92 ECa measurements were realized with EM38. The pairs of ECe-ECa values allowed establishing the calibration equation permitting to convert the ECa into ECe values and for other
Walailak Journal of Science and Technology (WJST), 2017
Salt-affected soil maps for Northeast Thailand focus on the percentage of salt crusts. Investigation was done to find the field-scale spatial variability of the electrical conductivity of saturation extract (ECe) in salt-affected areas (percentage salt crusts: very severely = class 1; severely = class 2, and moderately = class 3). Two study sites were selected for each class (n = 6). Soil samples (n = 100) were collected at each site using stratified, systematic, unaligned sampling, and analyzed for ECe. Variations in ECe were assessed using basic statistics and geostatistics. At the field-scale, in every class, the best-fit semivariogram model generated was satisfactory (R2 > 0.8). Interpretation from the relevant model parameters (i.e., nugget, sill, and effective range), together with the interpolated (kriged) maps, demonstrated that the characteristics of spatial variability of soil ECe were inconsistent, even between different sites of the same salt-affected soil class. In g...
Soil salinity is a major global issue due to its adverse impact on the environment, agro-ecosystems, agricultural productivity and sustainability. Saline soils are significant as formations of ecosystem on the earth affected by high concentrations of soluble salts, and as means of crop production with little economic value. Threats being the water scarcity, drought, degradation of surface and groundwater quality leading to soil salinization. Many plants either fail to grow in saline soils or their growth is retarded significantly. However, few plants grow well on saline soils; therefore, soil salinity often restricts options for cropping in a given area. Therefore, temporal understanding of soil salinity through mapping and monitoring helps understand subtle difference across the landscape and agricultural fields, and allows their precise management. Mapping on regional and national levels is appropriate to be accomplished through interpretation of Remote Sensing Imagery supplemente...
Determining Soil Salinity from Soil Electrical Conductivity using Different Models and Estimates
Soil Science Society of America Journal, 1990
The appropriateness of two versions of a model describing electrical current flow in undisturbed soil was evaluated for purposes of diagnosing and mapping soil salinity (EC e). Different methods of measuring bulk soil electrical conductivity (EC.) and different ways of obtaining the values of other parameters required by the model were also evaluated using three different sets of data. The reliability of the variously predicted salinities were evaluated by comparing them against conventionally measured salinities using linear regression analyses, ranking tests, and a procedure based on the weighted sums of squared differences. It was found that salinity-prediction accuracy, though relatively good, was underestimated for the instrumental/model techniques because of sample-size differences and spatial-variability effects. Conclusions arrived at from earlier sensitivity analyses about the suitability of the parameter-estimation procedures were borne out by these results, i.e., that soil salinity appraisal and mapping can be quite adequately made using field measurements of EC. (by any one of three methods) and estimates of soil water content, bulk density, and surface conductance determined by "feel" methods.
Soil Science Society of America Journal, 2001
such as kriging, have been introduced into soil science to provide BLUE at unsampled locations (Burgess and Various approaches have been used to estimate soil salinity (EC e ) Odeh et al., 1995). The major difference at unsampled locations. Some of these approaches are briefly discussed. Of these, geostatistical methods such as ordinary kriging (OK1 between geostatistics and classical statistics is that the and OK2), regression kriging (RK), three-dimensional kriging former allows for the direct modeling of the inherent (3-DK), and cokriging (Co-K), provide best linear unbiased estimates spatial data correlation. This is achieved through the (BLUE). These methods were tested with a raw electromagnetic ininitial calculation of a variogram, which acts as a quantiduction instrument (Type EM38) in a soil electrical conductivity (EC a ) fied summary of all the available structural information survey, and calibrated EC e data were obtained from an irrigated cotton of one or more random functions (Journel and Huij-(Gossypium hirsutum L.)-growing area in the Edgeroi district of the bregts, 1978). In comparing classical statistical and geoslower Namoi valley, northern New South Wales, Australia. We comtatistical methods, Hajrasuliha et al. (1980) found that pared these methods, on the basis of precision and bias of estimation, the spatial dependent nature of the variance structure in and found that RK was the best performer. This is because of the some fields allowed geostatistical techniques to produce incorporation of regression residuals within the kriging system. Mean and standard deviation of ranks (SDR) showed that Co-K performed better predictions of soil salinity. best against these criteria.
Assessment of soil and soil-water salinity in Ben Tre province by electromagnetic technology
Tạp chí Khoa học và Công nghệ biển, 2020
Assessment of soil and soil-water salinity is essential in agricultural production, therefore it is necessary to find out the non-costly, effective, rapid and reliable integrated methodology for this purpose. The paper presents the results of using the electromagnetic induction instrument EM31-MK2Ô in combination with collecting and analyzing soil and soil-water samples, and applying GIS and geostatistical techniques to assess the current status of soil and soil-water salinity in Ben Tre province. Apparent soil electrical conductivity ECa measured from ground surface to 6 m in depth increases from inland to the sea in northwest - southeast direction; ECa is closely related to topsoil salinity to 30 cm deep and to soil-water salinity at depth of 10–100 cm. Current status of soil and soil-water salinity in 2018 was assessed with a 4-fold increase in information, from 16 km2/data point to 4 km2/data point. Consequently four maps were established, consisting of electrical conductivity E...
Communications in Soil Science and Plant Analysis, 2020
The electrical conductivity of a saturated paste extract of soil (ECe) is a standard measurement of soil salinity, which may adversely effect the environment and plants. This study aimed to develop a precise linear regression relationship between ECe and electrical conductivity (EC) of different (1:5, 1:2.5, 1:1) soil-water ratios based on soil texture (represented by an index Txw) for the ease and rapid monitoring of soil salinity in an area. Surface (0-20 cm) soil samples (n = 150) from the coastal zone of Bangladesh were analyzed for particle size distribution, and EC by various methods. Entire soil samples were equally divided into two textural groups (finer and coarser) based on Txw values. Necessary statistical treatments were performed to satisfy the conditions of linear regression analysis. Significant correlations (r = −0.44** to −0.56**) were found between Txw and the EC values of various methods within the whole dataset. Derived general (not Txw-assisted, i.e., all data) and specific (Txw-assisted groups) equations followed the model ECe =10 ½a LogEC Soil:water ð Þ þ b. Almost all specific equations showed improved coefficient of determinations (mean of all r 2 = 0.86; all p < .0001) compared to general ones (mean of all r 2 = 0.81; all p < .0001). While validation, corresponding errors (RMSE and/or ME) were found less in the specific equations than the general ones in predicting ECe. Therefore, soil texture (Txw)-based derived equations can preferably be used to predict ECe using other methods for the monitoring of soil salinity in an area.
Prediction of soil salinity and sodicity using electromagnetic conductivity imaging
Geoderma, 2020
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 indepth 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 propertieselectrical 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.