Estimation Of Electrical Conductivity Research Articles

Soil Salinity Estimation in Cotton Fields in Arid Regions Based on Multi-Granularity Spectral Segmentation (MGSS)

Soil salinization seriously threatens agricultural production and ecological environments in arid areas. The accurate and rapid monitoring of soil salinity and its spatial variability is of great significance for the amelioration of saline soils. In this study, 191 soil samples were collected from cotton fields in southern Xinjiang, China, to obtain spectral reflectance and electrical conductivity (EC) indoors. Then, multi-granularity spectral segmentation (MGSS) and seven conventional spectral preprocessing methods were employed to preprocess the spectral data, followed by the construction of partial least squares regression (PLSR) models for soil EC estimation. Finally, the performance of the models was compared. The results showed that compared with conventional spectral preprocessing methods, MGSS could greatly improve the correlation between spectrum and soil EC, extract the weak spectral information of soil EC, and expand the spectral utilization range. The model validation results showed that the PLSR model based on the second-order derivative (2nd-der-PLSR) had the highest estimation accuracy among the models constructed by conventional methods. However, the PLSR model based on MGSS (MGSS-PLSR) had the highest estimation accuracy among all models, with Rp2 (0.901) and RPD (3.080) being 0.151 and 1.302 higher than those of the 2nd-der-PLSR model, respectively, and nRMSEP (5.857%) being 4.29% lower than that of the 2nd-der-PLSR model. The reason for the high accuracy of the MGSS-PLSR model is as follows: In the continuous segmentation of the raw spectrum by MGSS, the bands with strong and weak correlations with respect to soil EC were concentrated during low granularity segmentation. With the increase in granularity level, the spectral features decreased and were distributed discretely. In addition, the locations of spectral features were also different at different granularity levels. Therefore, the spectral features of soil EC can be effectively extracted by the MGSS, which significantly improves the spectral estimation accuracy of soil salinity. This study provides a new technical means for soil salinity estimation in arid areas.

Salinity Properties Retrieval from Sentinel-2 Satellite Data and Machine Learning Algorithms

The accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of semiarid regions. The objective of this study was to achieve the best estimation of electrical conductivity variables from salt-affected soils in a south Mediterranean region using Sentinel-2 multispectral imagery. In order to realize this goal, a test was carried out using electrical conductivity (EC) data collected in central Tunisia. Soil electrical conductivity and leaf electrical conductivity were measured in an olive orchard over two growing seasons and under three irrigation treatments. Firstly, selected spectral salinity, chlorophyll, water, and vegetation indices were tested over the experimental area to estimate both soil and leaf EC using Sentinel-2 imagery on the Google Earth Engine platform. Subsequently, estimation models of soil and leaf EC were calibrated by employing machine learning (ML) techniques using 12 spectral bands of Sentinel-2 images. The prediction accuracy of the EC estimation was assessed by using k-fold cross-validation and computing statistical metrics. The results of the study revealed that machine learning algorithms, together with multispectral data, could advance the mapping and monitoring of soil and leaf electrical conductivity.

Contribution of Non‐Water Ices to Salinity and Electrical Conductivity in Ocean Worlds

AbstractModeling of the electrical conductivity (EC) of icy moon oceans has previously assumed that chloride, sulfate, and other ions released from rock leaching are the main solutes and carriers of EC. Here, we show that accreted volatiles, such as carbon dioxide and ammonia, can add a significant fraction of solutes in bodies whose volatile content was in part supplied from cometary materials. These volatiles can increase the EC of aqueous solutions above 1 S/m. Our salinity and EC estimates can serve as a basis for planning future magnetometer investigations at icy moons and dwarf planets. In particular, oceans expected in some of the Uranian satellites and Neptune's satellite Triton could have EC above 3 S/m as a result of accretion of both carbon dioxide and ammonia, even if rock leaching during water‐rock separation was limited, and if chlorine and sulfur abundances may be at CI carbonaceous chondritic levels.

A novel hybrid framework based on the ANFIS, discrete wavelet transform, and optimization algorithm for the estimation of water quality parameters

Abstract Improving the performance of machine learning (ML) algorithms is essential for accurately estimating water quality parameters (WQPs). For the first time, a novel hybrid framework, namely the adaptive neural fuzzy inference system–discrete wavelet transform–gradient-based optimization (ANFIS–DWT–GBO), for estimation of electrical conductivity (EC) and total dissolved solids (TDS), is used. Before estimating WQPs, the performance of the ANFIS–DWT–GBO is proven by several benchmark data sets. In addition, three benchmark algorithms, including ANFIS, ANFIS–DWT, and ANFIS–GBO, are used to demonstrate the strength of the novel framework. The principal component analysis (PCA) method determines the best input combination in EC and TDS estimation. The consequences show that the ANFIS–DWT–GBO produces very successful and competitive results in benchmark data sets modeling and WQPs estimation compared to other algorithms. This result is due to the simultaneous use of DWT and optimization algorithm in the proposed framework. DWT can process WQP data before applying it to the algorithms. The GBO is utilized to optimize the hyperfine parameters in the ANFIS. The results show that the highest accuracy of estimating EC and TDS is in Mollasani and Gotvand stations, respectively. The correlation coefficient (R) value in the Mollasani station is 0.99, and in the Gotvand station it is 0.98.

AI-based ensemble modeling of landfill leakage employing a lysimeter, climatic data and transfer learning

Predicting leachate pollutants is of prime importance in detecting the amount of pollution in water resources adjacent to sources of leakage. In this study, Electrical Conductivity (EC) as a physicochemical water pollution parameter with the possibility of portable measurement was used as an indicator of leachate quality for the Tychy-Urbanowice operating and closed landfill complex. In order to simulate landfill conditions, two lysimeter experiments were conducted simultaneously. Using sensors mounted in the lysimeters, from the end of November 2018 to the end of December 2019, EC, waste temperature and waste moisture were measured for the open lysimeter and only waste moisture for the closed lysimeter. Additionally, meteorological data obtained from the nearest synoptic station and soil moisture and temperature acquired from the GLDAS satellite were employed as external data to analyze various conditions. Thereafter, Artificial Neural Network (ANN), Neuro-Fuzzy Inference System (ANFIS), and Emotional ANN (EANN) models were developed to determine the parameters affecting the EC value recorded for the open lysimeter and subsequently, predict the missing EC parameter of the closed lysimeter by employing the transfer learning method. Following that, in order to improve the precision of EC predictions, ensemble techniques were applied to the outputs of the models that were developed. The results showed that the moisture of the lysimeters made a significant contribution to the EC value prediction. It is worth mentioning that among ANN, ANFIS, and EANN, the EANN model yielded more precise results in EC estimation, with the average DC above 0.80 and 0.90 for individual and ensembled modeling in both the training and verification phases, respectively.

A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing

Water is a fundamental resource for human survival but the consumption of water that is unfit for drinking leads to serious diseases. Access to high–resolution satellite imagery provides an opportunity for innovation in the techniques used for water quality monitoring. With remote sensing, water quality parameter concentrations can be estimated based on the band combinations of the satellite images. In this study, a hybrid remote sensing and deep learning approach for forecasting multi–step parameter concentrations was investigated for the advancement of the traditionally employed water quality assessment techniques. Deep learning models, including a convolutional neural network (CNN), fully connected network (FCN), recurrent neural network (RNN), multi–layer perceptron (MLP), and long short term memory (LSTM), were evaluated for multi–step estimations of an optically active parameter, i.e., electric conductivity (EC), and an inactive parameter, i.e., dissolved oxygen (DO). The estimation of EC and DO concentrations can aid in the analysis of the levels of impurities and oxygen in water. The proposed solution will provide information on the necessary changes needed in water management techniques for the betterment of society. EC and DO parameters were taken as independent variables with dependent parameters, i.e., pH, turbidity, total dissolved solids, chlorophyll–α, Secchi disk depth, and land surface temperature, which were extracted from Landsat–8 data from the years 2014–2021 for the Rawal stream network. The bi–directional LSTM obtained better results with a root mean square error (RMSE) of 0.2 (mg/L) for DO and an RMSE of 281.741 (μS/cm) for EC, respectively. The results suggest that a hybrid approach provides efficient and accurate results in feature extraction and evaluation of multi–step forecast of both optically active and inactive water quality parameters.

Combination of Hyperspectral and Machine Learning to Invert Soil Electrical Conductivity

An accurate estimation of soil electrical conductivity (EC) using hyperspectral techniques is of great significance for understanding the spatial distribution of solutes and soil salinization. Although spectral transformation has been widely used in data pre-processing, the performance of different pre-processing techniques (or combination methods) on different models of the same data set is still ambiguous. Moreover, extremely randomized trees (ERT) and light gradient boosting machine (LightGBM) models are new learning algorithms with good generalization performance (soil moisture and above-ground biomass), but are less studied in estimating soil salinity in the visible and near-infrared spectra. In this study, 130 soil EC data, soil measured hyperspectral data, topographic factors, conventional salinity indices such as Salinity Index 1, and two-band (2D) salinity indices such as ratio indices, were introduced. The five spectral pre-processing methods of standard normal variate (SNV), standard normal variate and detrend (SNV-DT), inverse (1/OR) (OR is original spectrum), inverse-log (Log(1/OR) and fractional order derivative (FOD) (range 0–2, with intervals of 0.25) were performed. A gradient boosting machine (GBM) was used to select sensitive spectral parameters. Models (extreme gradient boosting (XGBoost), LightGBM, random forest (RF), ERT, classification and regression tree (CART), and ridge regression (RR)) were used for inversion soil EC and model validation. The results reveal that the two-dimensional correlation coefficient highlighted EC more effectively than the one-dimensional. Under SNV and the second order derivative, the two-dimensional correlation coefficient increased by 0.286 and 0.258 compared to the one-dimension, respectively. The 13 characteristic factors of slope, NDI, SI-T, RI, profile curvature, DOA, plane curvature, SI (conventional), elevation, Int2, aspect, S1 and TWI provided 90% of the cumulative importance for EC using GBM. Among the six machine models, the ERT model performed the best for simulation (R2 = 0.98) and validation (R2 = 0.96). The ERT model showed the best performance among the EC estimation models from the reference data. The kriging map based on the ERT simulation showed a close relationship with the measured data. Our study selected the effective pre-processing methods (SNV and the 2 order derivative) using one- and two-dimensional correlation, 13 important factors and the ERT model for EC hyperspectral inversion. This provides a theoretical support for the quantitative monitoring of soil salinization on a larger scale using remote sensing techniques.

Comparative study on the abilities of different crack parameters to estimate the salinity of soda saline-alkali soil in Songnen Plain, China

Salinity is commonly considered to be the main factor determining the crack extent of clayey soda saline-alkali soil in the Songnen Plain in China. However, there are great variations among different parameters in quantifying the extent of desiccation cracks in salt-affected soil, leading to substantial uncertainty in their abilities to characterize soil salinity. This study aimed to analyze the relationships between different types of crack parameters and the salinity of soil samples to establish optimal prediction models for the electrical conductivity (EC) values. To achieve these objectives, 104 soil samples with different salinity levels were obtained and pre-processed based on a unified standard for binary crack patterns. Thereafter, three common types of crack parameters including the crack intensity factor (CIF); box-counting dimensions under equal-partitions of 2, 3, 5, and 7; and four gray-level co-occurrence matrix (GLCM) texture features were extracted for correlation coefficients with the EC values of the soil samples based on a series of regression analyses. The results indicate that although different types of crack parameters all have a clear correlation with EC, CIF shows a poor relationship with EC values compared to those of the fractal dimensions and the GLCM texture features. The CIF, box-counting fractal dimensions based on 2 equal-partition, and contrast (CON) texture feature were then selected as the most ideal indices to develop exponential regression models for EC estimation. In addition, fitting results from verification work showed that CON was the best predictor of EC (R2 of 0.85), with an accuracy much higher than box-counting dimension based on 2 equal-partition (R2 of 0.68) and CIF (R2 of only 0.56).

Using depth specific electrical conductivity estimates to improve hydrological simulations in a heterogeneous tile-drained field

In agricultural fields, tile drains represent potential pathways for the migration of solutes, such as nitrates, in groundwater and surface water bodies. Tile drain flow is controlled by the temporal and spatial dynamics of the shallow groundwater table, which results from complex interactions between climate, topography and soil heterogeneity. Studies on the effect of topsoil heterogeneity on shallow water and drainage dynamics by fully 3D surface water and groundwater flow modeling are limited. The objective of our study is to demonstrate the use of depth specific electrical conductivity (EC) estimates to improve hydrological simulations in a tile-drained field. The model was applied to a field site in Denmark where times series of drainage discharge and water table elevations are available. Clay-rich soil zones were identified in a tile-drained field using depth specific electrical conductivity estimates generated by the inversion of apparent electrical conductivity data measured using an electromagnetic induction instrument. One model that included the low-permeability clayey zones in the soil layers down to a depth of 1.2 m was compared to a simpler model that assumed homogeneous soil layers. Both models simulate drainage discharge that compares well to the observations. However, including the clayey zones improves the simulation of hydraulic heads, and water table fluctuations, and generates flooded areas that are more representative of those observed during the wet seasons. Our results suggest that the simulation of water table fluctuations can be improved when the soil heterogeneity determined from depth specific EC estimates is included in integrated hydrological models. A better representation of the subsurface flow dynamics will also improve subsequent simulations of the transport and fate of agrochemical substances leaching from fields such as nitrate, which may deteriorate the quality of groundwater and surface water bodies.

Combining Vis–NIR spectroscopy and advanced statistical analysis for estimation of soil chemical properties relevant for forest road construction

AbstractA thorough quantification of soil chemical properties is essential for assessing the engineering properties of forest soils for road design, construction, and maintenance. Here, we investigate the applicability of visible–near‐infrared (Vis–NIR) spectroscopy in conjunction with advanced statistical analysis for estimation of soil chemical properties. Sixty forest soil samples were collected and analyzed for pH, electrical conductivity (EC), CaCO3, organic matter (OM), and cation exchange capacity (CEC) with established laboratory methods. The spectral measurements were performed with a Vis–NIR spectrometer within a range of 350–2,500 nm. To estimate abovementioned soil properties from reflectance spectra, advanced statistical techniques including partial least squares regression (PLSR), hybrid partial least squares and artificial neural networks (PLS–DI–ANN) models, hybrid partial least squares and adaptive neural fuzzy inference system (PLS–DI–ANFIS) models, as well as narrow band spectral indices were applied. The obtained results ​​indicate that the PLS–DI–ANFIS models show great potential for the estimation of pH, EC, OM, and CEC from reflectance spectra and their first derivatives, exhibiting higher R2 values and lower RMSE than the other investigated models. The estimation accuracy for CaCO3, however, was low for all applied methods. The results confirm that Vis–NIR spectroscopy may be applied as a rapid and cost‐efficient alternative to standard chemical soil analysis techniques, aiding forest road design, construction, and maintenance.

Modeling of contaminant concentration using the classification-based model integrated with data preprocessing algorithms

Abstract Water quality is one of the most important factors contributing to a healthy life; meanwhile, total dissolved solids (TDS) and electrical conductivity (EC) are the most important parameters in water quality, and many water developing plans have been implemented for the recognition of these factors. The accurate prediction of water quality parameters (WQPs) is an essential requisite for water quality management, human health, public consumption, and domestic uses. Using three novel data preprocessing algorithms (DPAs), including empirical mode decomposition (EMD), ensemble EMD (EEMD), and variational mode decomposition (VMD) to estimate two important WQPs, TDS and EC, differentiates this study from the existing literature. The acceptability and reliability of the proposed models (e.g., model tree (MT), EMD-MT, EEMD-MT, and VMD-MT) were evaluated using five performance metrics and visual plots. A comparison of the performances of standalone and hybrid models indicated that DPAs can enhance the performance of standalone MT model for both TDS and EC estimations. For instance, the VMD-MT model (root-mean-square error (RMSE) = 24.41 mg/l, ratio of RMSE to SD (RSD) = 0.231, and Nash–Sutcliffe efficiency (Ens) = 0.94 (Garmrood) and RMSE = 31.85 mg/l, RSD = 0.133, and Ens = 0.98 (Varand)) outperformed other hybrid models and original MT models for TDS estimations. Regarding the EC estimation results, as for R2, VMD could enhance the accuracy of prediction for the MT model for Garmrood and Varand stations by 10.2 and 7.6%, respectively.

Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China

Accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of arid regions. As a branch of artificial intelligence, machine learning acquires new knowledge through self-learning and continuously improves its own performance. The purpose of this study is to combine Sentinel-2 Multispectral Imager (MSI) data and MSI-derived covariates with measured soil salinity data and to apply three machine learning algorithms in modeling to estimate and map the soil salinity in the study sample area. According to the convenient transportation conditions, the study area and sampling quadrat were set up, and the 5-point method was used to collect the soil mixed samples, and 160 soil mixed samples were collected. Kennard–Stone (K–S) algorithm was used for sample classification, 70% for modeling and 30% for verification. The machine learning algorithm uses Support Vector Machines (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The results showed that (1) the average reflectance of each band of the MSI data ranged from 0.21–0.28. According to the spectral characteristics corresponding to different soil electrical conductivity (EC) levels (1.07–79.6 dS m−1), the spectral reflectance of salinized soil in the MSI data ranged from 0.09–0.35. (2) The correlation coefficient between the MSI data and MSI-derived covariates and soil EC was moderate, and the correlation between certain MSI data sets and soil EC was not significant. (3) The SVM soil EC estimation model established with the MSI data set attained a higher performance and accuracy (R2 = 0.88, root mean square error (RMSE) = 4.89 dS m−1, and ratio of the performance to the interquartile range (RPIQ) = 1.96, standard error of the laboratory measurements to the standard error of the predictions (SEL/SEP) = 1.11) than those attained with the soil EC estimation models established with the RF and ANN models. (4) We applied the SVM soil EC estimation model to map the soil salinity in the study area, which showed that the farmland with higher altitudes discharged a large amount of salt to the surroundings due to long-term irrigation, and the secondary salinization of the farmland also caused a large amount of salt accumulation. This research provides a scientific basis for the simulation of soil salinization scenarios in arid areas in the future.

Salt dome related soil salinity in southern Iran: Prediction and mapping with averaging machine learning models

AbstractIn order to manage soil salinity effectively, it is necessary to understand the origin and the spatial distribution of salinity. There are about 120 salt dome outcrops in southern Iran and little is known about their contribution as the potential sources of salts and the spatial pattern of salts around them. Six machine learning algorithms were applied to model topsoil electrical conductivity (EC) and sodium adsorption ratio (SAR) in the Darab Plain (surrounded by six salt domes), Fars Province. Decision trees (DT), k‐nearest neighbours (kNN), support vector machines (SVM), Cubist, random forests (RF) and extreme gradient boosting (XGBoost) were used as primary models and the Granger–Ramanathan (GR) method was used to combine the predictions of these models. The results showed that remotely sensed data contributed more to predict EC and SAR than terrain‐based data. In terms of root mean square errors (RMSE), Cubist followed by the RF model, tended to give the best estimates of EC, whereas for SAR, RF performed best and was followed closely by SVM and Cubist. Compared to the primary models, the GR method on average resulted in a decrease of 6.1% and 3.9% in RMSE and an increase of 10% and 10.9% in R2 for EC and SAR, respectively. The spatial pattern of SAR and EC suggested that the contribution of salt domes in soil salinization varied significantly according to their hydraulic behaviour in relation to adjacent aquifers and their activity. In general, the model averaging approach showed the potential to improve the estimates of EC and SAR.

Spatial prediction of soil electrical conductivity using soil axillary data, soft data derived from general linear model and error measurement

Indirect measurement of soil electrical conductivity (EC) has become a major data source in spatial/temporal monitoring of soil salinity. However, in many cases, the weak correlation between direct and indirect measurement of EC has reduced the accuracy and performance of the predicted maps. The objective of this research was to estimate soil EC based on a general linear model via using several soil properties. Through calibration equations, the error involved in such model-based data was calculated and employed in mapping soil EC using kriging with measurement errors (KME) method. The results were then compared with those of ordinary kriging (OK) and co-kriging (CK). Soil samples were taken from the depth of 0-20 cm in 78 points with spatial intervals of 500 m from an area of 40 km2, and they were analyzed for their electrical conductivity (EC) and certain other soil properties. Measured soil EC data (hard data) and auxiliary soil data were further used to develop the semi-variance and cross-semi-variance functions; moreover, soil salinity prediction was done on a grid of 100 m with OK and CK methods. Afterwards, the most optimal EC estimation model was developed using auxiliary soil data and GLM. As predicted values always involve uncertainty, the error involved with the predicted values was calculated and then the calibration equations were adjusted. Lastly, soil salinity was predicted using KME method. Results showed that the OK method had the lowest MSE and RMSE values, 0.65 and 0.8 dS m-1, respectively. Furthermore, among the auxiliary data, pH and silt content resulted in some of the best cross-semi-variance functions, among which, silt had a better performance regarding the spatial prediction of soil EC. The GLM model developed with the calculated error and KME resulted in predictions close to those of OK method (with MSE and RMSE of 0.74 and 0.86 dS m-1, respectively). KME method provided the possibility of merging error resulting from the use of soft data, derived from prediction equations; therefore, it successfully improved the spatial prediction of soil electrical conductivity

Spatial Prediction of Soil Salinity in a Semiarid Oasis: Environmental Sensitive Variable Selection and Model Comparison

Timely monitoring and early warning of soil salinity are crucial for saline soil management. Environmental variables are commonly used to build soil salinity prediction model. However, few researches have been done to summarize the environmental sensitive variables for soil electrical conductivity (EC) estimation systematically. Additionally, the performance of Multiple Linear Regression (MLR), Geographically Weighted Regression (GWR), and Random Forest regression (RFR) model, the representative of current main methods for soil EC prediction, has not been explored. Taking the north of Yinchuan plain irrigation oasis as the study area, the feasibility and potential of 64 environmental variables, extracted from the Landsat 8 remote sensed images in dry season and wet season, the digital elevation model, and other data, were assessed through the correlation analysis and the performance of MLR, GWR, and RFR model on soil salinity estimation was compared. The results showed that: 1) 10 of 15 imagery texture and spectral band reflectivity environmental variables extracted from Landsat 8 image in dry season were significantly correlated with soil EC, while only 3 of these indices extracted from Landsat 8 image in wet season have significant correlation with soil EC. Channel network base level, one of the terrain attributes, had the largest absolute correlation coefficient of 0.47 and all spatial location factors had significant correlation with soil EC. 2) Prediction accuracy of RFR model was slightly higher than that of the GWR model, while MLR model produced the largest error. 3) In general, the soil salinization level in the study area gradually increased from south to north. In conclusion, the remote sensed imagery scanned in dry season was more suitable for soil EC estimation, and topographic factors and spatial location also play a key role. This study can contribute to the research on model construction and variables selection for soil salinity estimation in arid and semiarid regions.

Frequency-Domain Electromagnetic Forward and Sensitivity Modeling: Practical Aspects of Modeling a Magnetic Dipole in a Multilayered Half-Space

Frequency-domain electromagnetic (FDEM) data are commonly acquired to provide spatially continuous information about subsurface electrical conductivity (EC) and/or magnetic susceptibility [1]. To link the phase-sensitive instrument responses?the quadrature-phase (QP) and in-phase (IP) components of the EM field (i.e., data space)-with the subsurface physical properties (i.e., model space), a forward model is indispensable. In addition, a forward model is essential for calculating the FDEM instrument response theoretically, e.g., during an inversion, and can be used for additional purposes, such as instrument calibration, synthetic data generation, depth of investigation estimation, instrument design, survey design, assessment of the detection limit for a given target within a known host body, apparent EC estimation, and educational purposes [2]-[7].

Spatial variation modelling of groundwater electrical conductivity using geostatistics and GIS

Optimized management of water resources, conservation and their quality increase is needful with data existence in basis of situation, amount and distribution of water chemical factors for example; electrical conductivity (EC) in determined geographical region. Accuracy of interpolation appropriate methods and variation map preparation of groundwater quality variables is independent to region conditions and existence of enough data. That is true selection of interpolation methods is basic and important step in management of groundwater resources. EC is one of the important indicators for groundwater quality evaluation. The objective of this research was to determine the most suitable interpolation method and their accuracy for analysis and checking spatial variation of groundwater EC amount in central regions of Guilan province, northern Iran. This investigation evaluated the inverse distance weighting (IDW), global polynomial interpolation (GPI), local polynomial interpolation (LPI), radial basis function (RBF) and ordinary kriging (OK) methods for estimation of groundwater EC in paddy fields. In IDW method, for variable estimation used power value 1–5 that power value equal 1 was exact. Gaussian model was the best one fitted on empirical semivariogram of variable data in OK method. Standard statistical performance evaluation criteria include root mean square error (RMSE), correlation coefficient (R) and mean absolute error (MAE) were used to control the accuracy of the prediction capability of the developed methods. Results showed that the best estimator was OK method which was the most exact with regard to other methods for estimation groundwater electrical conductivity.

Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices

Soil salinization is major environmental issue in irrigated agricultural production. Conventional methods for salinization monitoring are time and money consuming and limited by the high spatiotemporal variability of this phenomenon. This work aims to propose a spatiotemporal monitoring method of soil salinization in the Tadla plain in central Morocco using spectral indices derived from Thematic Mapper (TM) and Operational Land Imager (OLI) data. Six Landsat TM/OLI satellite images acquired during 13 years period (2000–2013) coupled with in-situ electrical conductivity (EC) measurements were used to develop the proposed method. After radiometric and atmospheric correction of TM/OLI images, a new soil salinity index (OLI-SI) is proposed for soil EC estimation. Validation shows that this index allowed a satisfactory EC estimation in the Tadla irrigated perimeter with coefficient of determination R2 varying from 0.55 to 0.77 and a Root Mean Square Error (RMSE) ranging between 1.02 dS/m and 2.35 dS/m. The times-series of salinity maps produced over the Tadla plain using the proposed method show that salinity is decreasing in intensity and progressively increasing in spatial extent, over the 2000–2013 period. This trend resulted in a decrease in agricultural activities in the southwestern part of the perimeter, located in the hydraulic downstream.

Potential of multispectral and hyperspectral data to detect saline-exposed soils in Brazil

Irrigation-induced soil salinization is an important land degradation process in northeastern Brazil. We used multispectral and hyperspectral sensors to detect saline-exposed soils in an area cultivated with irrigated rice. Spectral mixture analysis (SMA) was applied to Operational Land Imager (OLI)/Landsat-8 data to identify exposed soils. By measuring the electrical conductivity (EC) of soil samples from 53 sites, we classified them into saline and non-saline. The surface reflectance Thematic Mapper /Landsat-5 product was used to inspect the normalized difference vegetation index (NDVI) variations over time (1984–2011) at the sites. Using OLI/Landsat-8 and Hyperion/Earth Observing One, we obtained five salinity indices and scores from principal component analysis applied to exposed soil pixels. These indices along with the first principal component (PC1) were regressed against EC to estimate soil salinization. Different metrics and support vector machine (SVM) were tested to discriminate saline and non-saline soils. The results showed that exposed soils detected by SMA had NDVI with a lower mean and standard deviation over time in the saline areas due to vegetation growth limitation. NaCl absorption bands were not observed in the Hyperion spectra due to atmospheric water vapor. Therefore, soil salinity detected by OLI or Hyperion was due to soil brightness rather than absorption bands. Because most salinity indices and scores expressed brightness to some extent, they were correlated with EC, especially the Salinity Index and PC1. However, compared with OLI, the narrow-band salinity indices of Hyperion produced a lower root mean square error for EC estimates, better discrimination between saline and non-saline soils using the Euclidean distance and spectral angle metrics, and higher SVM classification accuracy.

Modelling electrical conductivity of soil from backscattering coefficient of microwave remotely sensed data using artificial neural network

Soil salinity is one of the main agricultural problems which expand to larger areas. Soil scientists categorize salinity level by electrical conductivity (EC) measurement. However, field measurements of EC require extensive time, cost and experiences. Remote sensing is one suitable option to investigate and collect spatial data in larger areas. Many researches estimated soil moisture through microwave, but there are fewer studies which mentioned about direct relationship between EC and backscattering coefficient (BC). Thus, this study aims to propose the estimation of EC directly from BC of microwave. The relationship between EC obtained from field survey and BC from microwave is non-linear, artificial neural network (ANN) is one technique proposed in this study to figure out EC and BC relationship. ANN uses multilayer of interconnected processing resulting in EC value with high accuracy which is acceptable. For this reason, ANN model can be successfully utilized as an effective tool for EC estimation from microwave.