Soil Salinity Monitoring Research Articles

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.

Methodology for Comparing Soil Maps of Different Dates with the Aim to Reveal and Describe Changes in the Soil Cover (by the Example of Soil Salinization Monitoring)

A methodology for comparing soil map of different dates in order to reveal changes in the soil cover is discussed. The analysis of a set of the maps of soil salinization on one of the farms in the Golodnaya Steppe region of Uzbekistan is used as an example. It is shown that traditional methods of comparing two maps developed in different years (normally, with an interval of five years and more) are low informative for the assessment of soil salinization dynamics. The suggested methodology assumes simultaneous analysis of several maps in order to reveal the trends in soil salinization. However, even in this case, the obtained results do not adequately characterize the dynamics of soil salinization on irrigated fields. It is argued that the direction of soil salinization–desalinization processes is an improper characteristic in this case. In order to understand the dynamics of soil salinization, the maps showing the dynamism of soil salinity and the maximum changes in the degree of salinity can be applied. A series of the compared maps make it possible to describe the changes in the soil cover related to salinization–desalinization processes. The high dynamism of these processes against the background of a virtually stable pool of soluble salts in the 1-m-deep soil layer is shown for the considered farm.

Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data

Soil salinization is one of the most widespread soil degradation processes on Earth, especially in arid and semi-arid areas. The salinized soil in arid to semi-arid Xinjiang Uyghur Autonomous Region in China accounts for 31% of the area of cultivated land, and thus it is pivotal for the sustainable agricultural development of the area to identify reliable and cost-effective methodologies to monitor the spatial and temporal variations in soil salinity. This objective was accomplished over the study area (Keriya River Basin, northwestern China) by adopting technologies that heavily rely on, and integrate information contained in, a readily available suite of remote sensing datasets. The following procedures were conducted: (1) a selective principle component analysis (S-PCA) fusion image was generated using Phased Array Type L-band SAR (PALSAR) backscattering coefficient (σ°) and Landsat Enhanced Thematic Mapper Plus (ETM+) multispectral image of Keriya River Basin; and (2) a support vector machines (SVM) classification method was employed to classify land cover types with a focus on mapping salinized soils; (3) a cross-validation method was adopted to identify the optimum classification parameters, and obtain an optimal SVM classification model; (4) Radarsat-2 (C band) and PALSAR polarimetric images were used to analyze polarimetric backscattering behaviors in relation to the variation in soil salinization; (5) a decision tree (DT) scheme for multi-source optical and polarimetric SAR data integration was proposed to improve the estimation and monitoring accuracies of soil salinization; and (6) detailed field observations and ground truthing were used for validation of the adopted methodology, and quantity and allocation disagreement measures were applied to assess classification outcome. Results showed that the fusion of passive reflective and active microwave remote sensing data provided an effective tool in detecting soil salinization. Overall accuracy of the adopted SVM classifier with optimal parameters for fused image of ETM+ and PALSAR data was 91.25% with a Kappa coefficient of 0.89, which was further improved by the DT data integration and classification method yielding an accuracy of 93.01% with a Kappa coefficient of 0.92 and lower disagreement of quantity and allocation.

Dielectric properties of saline soil based on a modified Dobson dielectric model

Soil salinization is a major concern for agricultural development in arid areas. In this paper, a modified Dobson dielectric model was applied to simulate the dielectric constant of saline soil in the Ugan-Kuqa river delta oasis of Xinjiang Uygur autonomous region, northwestern China. The model performance was examined through analyzing the influences of its parameters on the soil dielectric constant and the relationship between radar backscattering coefficient and the dielectric constant of saline soil. The results of the study indicate that: (1) The real part of the soil dielectric constant is affected by soil water content at low radar frequencies; the imaginary part is closely related with both the soil water content and soil salt content. (2) The soil water and salt contents are related with the coefficient of dialectical loss, which is consistent with the natural conditions of saline soil in arid areas and provides valuable references for the study of soil dielectric properties. (3) The changes of soil water content and soil salt content have instant influences on the dielectric constant of saline soil. Subsequently, the radar backscattering coefficient is affected to respond to the dielectric constant of saline soil. The radar backscattering coefficient is most responsible to the radar’s cross polarization pattern with a correlation coefficient of R 2=0.75. This study provides a potential method to monitor soil salinization and soil water content by using a soil dielectric model and radar techniques.

Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.

In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles.

Two fixed ratio dilutions for soil salinity monitoring in hypersaline wetlands.

Highly soluble salts are undesirable in agriculture because they reduce yields or the quality of most cash crops and can leak to surface or sub-surface waters. In some cases salinity can be associated with unique history, rarity, or special habitats protected by environmental laws. Yet in considering the measurement of soil salinity for long-term monitoring purposes, adequate methods are required. Both saturated paste extracts, intended for agriculture, and direct surface and/or porewater salinity measurement, used in inundated wetlands, are unsuited for hypersaline wetlands that often are only occasionally inundated. For these cases, we propose the use of 1:5 soil/water (weight/weight) extracts as the standard for expressing the electrical conductivity (EC) of such soils and for further salt determinations. We also propose checking for ion-pairing with a 1:10 or more diluted extract in hypersaline soils. As an illustration, we apply the two-dilutions approach to a set of 359 soil samples from saline wetlands ranging in ECe from 2.3 dS m-1 to 183.0 dS m-1. This easy procedure will be useful in survey campaigns and in the monitoring of soil salt content.

Quantitative inversion of soil salinity and analysis of its spatial pattern in agricultural area in Shihezi of Xinjiang

Salinization has been a serious threat to continuous farming and grain production security, therefore, the rapid inversion of soil salinity is particularly important. The monitoring and prevention of soil salinization are of great significance to agricultural sustainable development in Shihezi, Xinjiang. In this study, we took the remote sensing image of Landsat-8OLI and the soil salinity of 72 samples measured in the field as a data base. Then we introduced the soil brightness index, soil moisture index, soil cover degree index and soil radiation level index. Finally we built feature remote sensing index to invert quantitatively the soil salinity of Land Reclamation District of Shihezi in Xinjiang, and analyzed the spatial pattern of it. The results showed that: 1 The accuracy of characteristic remote sensing index value of image pixel to invert soil salinity which corresponded to the ground was better. The inversion method was feasible. REE and RMSM, the precision validation index of inversion model were 3.07 and 0.34 respectively, and retrieval accuracy was 66%; 2 The distribution map of soil salinity with obvious grades and clear levels presented the pattern of‘one stripe and one region??. One stripe referred to the stripe with high salinization formed by Manas basin,mostly located in the alluvial plains, low- lying area, and reservoir areas. And one region referred to the region with serious salinization caused by soil texture. 3 In the farming areas,the non- salinization cotton area accounted for 14.02%, the mild salinization cotton area accounted for 21.38%, the moderate salinization cotton area accounted for 21.27%, severe salinization cotton area accounted for 29.99%, and saline accounted for 13.30% in cotton area.Some 35.40% of the cotton field was suitable for cultivation and 64.60% of the cotton field which needed to be improved. It is urgent to control soil salinization of farming areas.

Detecting soil salinity with MODIS time series VI data

Mapping of salinization using the satellite derived vegetation indices (VIs) remains difficult at broad regional scales due to the low classification accuracy. Satellite derived VIs from the Moderate Resolution Imaging Spectroradiometer (MODIS) have more potential because the MODIS balances the requirements of spatial detail, spectral and temporal density and tends to reflect vegetation responses through time. However, the relationship between MODIS data and salinity may be underestimated in previous studies because the MODIS time series data were not investigated thoroughly, especially regarding vegetation phenology. This study assessed the applicability of MODIS time series VI data for monitoring soil salinization with a series of MODIS pixels selected in the Yellow River Delta, China. The hidden information in vegetation phenology was investigated by improving the quality of VIs time series data with the Savitzky–Golay filter, extracting the phenological markers and differentiating VIs time series data based on vegetation types. The results showed that the quality of the enhanced vegetation index (EVI) time series data were improved by the Savitzky–Golay filter, which could provide more accurate thresholds of phenological stages than the empirical definition. The seasonal integral of EVI (EVI-SI) extracted from the smoothed EVI time series profile was verified as the best indicator of the degree of soil salinity. Additionally, the correlation of EVI-SI and soil salinity was highly dependent on land cover heterogeneity, and the ranges of correlation coefficients were as high as 0.59–0.92. EVI-SI was linearly correlated with ECe in cropland with a high model fit (R2=0.85). The relationship of EVI-SI and ECe fit best with a binomial line and EVI-SI was able to explain 70% of the variance of ECe. Despite the poor fit of the linear regression model in mixed sites limited by spatial resolution (R2=0.32), MODIS time series VI data, as well as the extracted seasonal parameters, still show great potential to assess large-scale soil salinization.

Mapping soil salinity using a similarity-based prediction approach: A case study in Huanghe River Delta, China

Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe (Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30–40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Indices (NDVIs) and land surface reflectance data from Landsat Thematic Mapper (TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient (CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30–40 cm depth in the study area (with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.

Soil Salinity Retrieval from Advanced Multi-Spectral Sensor with Partial Least Square Regression

Improper use of land resources may result in severe soil salinization. Timely monitoring and early warning of soil salinity is in urgent need for sustainable development. This paper addresses the possibility and potential of Advanced Land Imager (ALI) for mapping soil salinity. In situ field spectra and soil salinity data were collected in the Yellow River Delta, China. Statistical analysis demonstrated the importance of ALI blue and near infrared (NIR) bands for soil salinity. A partial least square regression (PLSR) model was established between soil salinity and ALI-convolved field spectra. The model estimated soil salinity with a R2 (coefficient of determination), RPD (ratio of prediction to deviation), bias, standard deviation (SD) and root mean square error (RMSE) of 0.749, 3.584, 0.036 g∙kg−1, 0.778 g∙kg−1 and 0.779 g∙kg−1. The model was then applied to atmospherically corrected ALI data. Soil salinity was underestimated for moderately (soil salinity within 2–4 g∙kg−1) and highly saline (soil salinity >4 g∙kg−1) soils. The underestimates increased with the degree of soil salinization, with a maximum value of ~4 g∙kg−1. The major contribution for the underestimation (>80%) may result from data inaccuracy other than model ineffectiveness. Uncertainty analysis confirmed that improper atmospheric correction contributed to a very conservative uncertainty of 1.3 g∙kg−1. Field sampling within remote sensing pixels was probably the major source responsible for the underestimation. Our study demonstrates the effectiveness of PLSR model in retrieving soil salinity from new-generation multi-spectral sensors. This is very valuable for achieving worldwide soil salinity mapping with low cost and considerable accuracy.

Soil Salinity Prediction, Monitoring and Mapping Using Modern Technologies

In arid and semi-arid regions of the world, soil salinization is one of the most crucial environmental problems due to its adverse effects on agricultural productivity and sustainable development. Unconscious irrigation and old irrigation techniques extremely damage fertile land and accelerate water logging and salt accumulation in soil. In addition, some natural factors also exacerbate soil salinity. Therefore, it is an important concern to predict and monitor soil salinity in order to take protective measures against further deterioration of the soil. In this study, emphasis is given to the techniques used for predicting and monitoring soil salinity throughout the different regions of the world. Examples of soil salinity mapping will also be referred to alert especially the soil scientists and farmers. Traditionally, soil salinity prediction and monitoring are often carried out with intensive field work and sampling. Most previous studies have focused on differentiating salinized and non-salinized soil qualitatively by analysing the salinity distribution and monitoring its dynamics. Remote Sensing (RS), Geographical Information Systems (GIS) and modelling have recently outperformed the traditional methods. Mapping has progressed from qualitative to quantitative mapping via multiple-temporal and multi-spectral information obtained from RS observations. In zones of thickly vegetated soils, using vegetation indices in the evaluation, mapping of soil salinity have started to yield promising results; whereas indices have become the appropriate method in the case of exposed soils or soils with low scattered vegetation. This study reviews the application of various satellite images to delineate soil salinity maps by conducting either salinity indices generated by different spectral bands or vegetation indices.

Information Extraction Method of Soil Salinity in Typical Areas of the Yellow River Delta Based on Landsat Imagery

In order to get RS method to extract soil salinity of the Yellow River Delta, we set Kenli County as typical Yellow River Delta to be research area and get data of soil salinity through field investigation. By using RS image of Landsat-8 of March 14, 2014 and analyzing information features of each band and surface spectral features of research areas, we select out sensitive bands and build Soil Salinity Information Extraction (SSIE) model and vegetation index NDVI model for comparison. And then, we accordingly classify grades of soil salinity and get soil salinity information by decision tree approach based on expert knowledge. The results show that overall accuracy of SSIE model is 93.04% and coefficient of Kappa is 0.7869, while overall accuracy of NDVI model is 83.67% and coefficient of Kappa is 0.7017 respectively. By comparing with measured proportions of each class, we see that results from SSIE model is more accurate, which indicates significant advantage for soil salinity information extraction. This research provides scientific basis to get and monitoring soil salinity of the Yellow River Delta region quickly and accurately.

Mapping of regional soil salinities in Xinjiang and strategies for amelioration and management

Abstract: Information on the spatial distribution of soil salinity can be used as guidance in avoiding the continued degradation of land and water resources by better informing policy makers. However, most regional soil-salinity maps are produced through a conventional direct-linking method derived from historic observations. Such maps lack spatial details and are limited in describing the evolution of soil salinization in particular instances. To overcome these limitations, we employed a method that included an integrative hierarchical-sampling strategy (IHSS) and the Soil Land Inference Model (SoLIM) to map soil salinity over a regional area. A fuzzy c-means (FCM) classifier is performed to generate three measures, comprising representative grade, representative area, and representative level (membership). IHSS employs these three measures to ascertain how many representative samples are appropriate. Through this synergetic assessment, representative samples are obtained and their soil-salinity values are measured. These samples are input to SoLIM, which is constructed based on fuzzy logic, to calculate the soil-forming environmental similarities between representative samples and other locations. Finally, a detailed soil-salinity map is produced through an averaging function that is linearly weighted, which is used to integrate the soil salinity value and soil similarity. This case study, in the Uyghur Autonomous Region of Xinjiang of China, demonstrates that the employed method can produce soil salinity map at a higher level of spatial detail and accuracy. Twenty-three representative points are determined. The results show that 1) the prediction is appropriate in Kuqa Oasis (R 2 = 0.70, RPD = 1.55, RMSE = 12.86) and Keriya Oasis (R 2 = 0.75, RPD = 1.66, RMSE = 10.92), that in Fubei Oasis (R 2 = 0.77, RPD = 2.01, RMSE = 6.32) perform little better than in those two oases, according to the evaluation criterion. 2) Based on all validation samples from three oases, accuracy estimation show the employed method (R 2 = 0.74, RPD = 1.67, RMSE = 11.18) performed better than the multiple linear regression model (R 2 = 0.60, RPD = 1.47, RMSE = 14.45). 3) The statistical result show that approximately half (48.07%) of the study area has changed to salt-affected soil, mainly distributed in downstream of oases, around lakes, on both sides of rivers and more serious in the southern than the northern Xinjiang. To deal with this issue, a couple of strategies involving soil-salinity monitoring, water management, and plant diversification are proposed, to reduce soil salinization. Finally, this study concludes that the employed method can serve as an alternative model for soil-salinity mapping on a large scale.

Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan–Kuqa Oasis, China, using remote sensing and electromagnetic induction instruments

In arid and semi-arid regions, soil salinization is one of the most critical environmental problems due to its severe effects on agriculture productivity and long-term sustainable development. Monitoring, evaluating and predicting soil salinization are of utmost importance in those regions. The current study proposes an evaluating and predicting approach that is based on remote sensing (e.g., Landsat TM images) and near sensing technologies (e.g., electromagnetic induction device, EM38). We investigated seasonal and spatial changes of soil salinity in a Delta Oasis between the Werigan and Kuqa River in the northern rim of Tarim Basin, Xinjiang, China. Preliminary analysis suggests that apparent soil electrical conductivity obtained from EM38 is highly correlated with soil salinity, which is obtained from post-sampling laboratory tests. The study hence uses the apparent electrical conductivity as a surrogate for soil salinity to understand the spatial pattern of the latter. To understand soil salinity distribution pattern in the study region, we integrated spectral information derived from two Landsat TM images (acquired on April 15, 2011 for the dry season and September 22, 2011 for the wet season), and applied universal kriging, spectral index regression and regression–kriging approaches to obtain the pattern. Results suggest that regression–kriging with nested spherical model produces the closest fit of the observed soil apparent electrical conductivity. Since most previous studies often employ either one or the other approaches in soil salinity monitoring and evaluation, the study suggests that combining remote and near sensing technology provides a rapid and relatively accurate assessment of soil salinity in arid and semi-arid regions, which would be essential to manage and prevent further soil salinization and re-salinization.

Mapping soil salinity in irrigated land using optical remote sensing data

Soil salinity caused by natural or human-induced processes is certainly a severe environmental problem that already affects 400 million hectares and seriously threatens an equivalent surface. Salinization causes negative effects on the ground; it affects agricultural production, infrastructure, water resources and biodiversity. In semi-arid and arid areas, 21% of irrigated lands suffer from waterlogging, salinity and/or sodicity that reduce their yields. 77 million hectares are saline soils induced by human activity, including 58% in the irrigated areas. In the irrigated perimeter of Tadla plain (central Morocco), the increased use of saline groundwater and surface water, coupled with agricultural intensification leads to the deterioration of soil quality. Experimental methods for monitoring soil salinity by direct measurements in situ are very demanding of time and resources, and also very limited in terms of spatial coverage. Several studies have described the usefulness of remote sensing for mapping salinity by its synoptic coverage and the sensitivity of the electromagnetic signal to surface soil parameters. In this study, we used an image of the TM Landsat sensor and field measurements of electrical conductivity (EC), the correlation between the image data and field measurements allowed us to develop a semi-empirical model allowing the mapping of soil salinity in the irrigated perimeter of Tadla plain. The validation of this model by the ground truth provides a correlation coefficient r² = 0.90. Map obtained from this model allows the identification of different salinization classes in the study area.

干旱区土壤盐渍化特征空间建模

当前，土壤盐渍化以及因灌溉引起的土壤次生盐渍化问题是我国干旱、半干旱区所面临的主要生态环境问题。在特征空间理论的支持下，以波谱分解技术为基础，以Landsat-TM、Landsat-ETM⁺多光谱遥感影像和野外调查数据为基础数据源，通过分析干旱区土壤盐渍化对地表生物物理特征的影响，探讨了表征盐渍化过程与地表生物物理特征之间的规律及定量关系，进而利用土壤盐渍化遥感监测中关键的3个指标--经过波谱分解技术获得的直接表征盐渍化的土壤盐渍化光谱、间接表征盐渍化的植被覆盖度和土壤水分含量协同构建了二维特征空间支持下的土壤盐渍化遥感监测模型VSSI（Vegetation fraction and Soil fraction Soil Index）、SVSI（Soil water contents and Vegetation fraction Soil Index）、SSSI（Soil water contents and Soil salinization fraction Soil Index）和三维特征空间支持下的土壤盐渍化遥感监测模型SVWSI和SDI。研究结果表明：基于三维特征空间建立的SVWSI（Soil salinization fraction-Vegetation fraction-Water contents Soil Index）和SDI（Soil Distance Index）模型对不同盐渍化程度土壤的敏感程度要高于基于传统二维特征空间建立的VSSI、SVSI和SSSI模型。其中，SVWSI和SDI模型与实测0-10 cm土壤盐分含量决定系数分别为<em>R</em>²=0.8325和<em>R</em>²=0.8646，这充分说明基于高维数特征空间所构建的土壤盐渍化遥感监测模型能更准确地反映盐渍化土壤地表盐量组合及其变化信息，且指标简单、易于获取，对于今后干旱区区域大尺度盐渍地信息提取以及动态监测研究具有重要意义。;Soil salinization, especially secondary soil salinization caused by irrigation activities is one of the primary ecological and environmental concerns in the arid and semi-arid regions of China. A critical research question is to quickly and accurately monitor soil salinization in arid and semi-arid regions so that prevention strategies can be developed quickly and deployed efficiently. Traditional techniques based on field soil sampling and laboratory experiments, though could be rather accurate for the sampling sites and their immediate vicinity, can hardly produce real-time evaluation. Monitoring soil salinization using remotely sensed imageries, however, starts to attract scholarly attention during the past decades due to the almost real-time information collection, vast geographic coverage, and rich information contained in the remotely sensed imageries. The current study is an attempt to employ remote sensing technique to monitor soil salinization in the arid/semi-arid Weigan-Kuqa Delta Oasis region in Xinjiang, Western China. Data are collected from Landsat-TM and Landsat-ETM+ multiple-spectral remote sensing imageries. Field soil samples at the 0-10 cm depth are obtained for validation purposes as well. The study intends to establish a statistical relationship between the degrees of soil salinization and surface biophysical reflective characteristics that are captured by the remote sensing imageries. Spectral un-mixing analysis of the multispectral imageries produces three groups of commonly used spectral information for soil salinization monitor and evaluation, i. e., individual spectra that are sensitive to soil salinization, that can be used to derive vegetation cover, and that can be used to derive soil moisture contents. The study then combines these groups of information establish three two-dimensional and two three-dimensional soil salinization monitoring indices. The three two dimensional indices include: Vegetation fraction and Soil Index(VSSI), Soil water contents and Vegetation fraction Soil Index(SVSI) and Soil water contents and Soil salinization fraction Soil Index(SSSI). The two three-dimensional soil salinization monitoring indices include: Soil salinization fraction-vegetation fraction-Water contents Soil Index(SVWSI) and Soil Distance Index(SDI). Statistical analyses using these obtained two dimensional and three dimensional indices with field soil sample data are conducted as well. The result suggests that all the indices are able to provide sufficient monitoring and evaluating performance of the severity of soil salinization in our designated study region. Three dimensional indices, however, tend to be more sensitive to soil salinization than the two dimensional indices. In particular, SVWSI and SDI are highly correlated with soil salt contents at the 0-10 cm depth, with correlation coefficients of <em>R</em>²=0.8325 and <em>R</em>²=0.8646, respectively. The result suggests that higher dimensional indices derived from remote sensing imageries might provide more accurate soil salinization monitoring measurements than lower dimensional indices due to enriched information structure. Since obtaining spectral information from remote sensing imageries is relatively straightforward and is often either real-time of near real-time, our suggests that rich information that can be derived from remotely sensed imageries shall be of invaluable importance to provide real-time and accurate evaluation and monitor for soil salinization monitoring and evaluation might provide timely strategies that can mitigate or even prevent further soil salinization in arid and semi-arid regions.

Seasonal monitoring of soil salinity by electromagnetic conductivity in irrigated sandy soils from a Saharan oasis

Monitoring soil salinity over time is a crucial issue in Saharan oases to anticipate salinisation related to insufficient irrigation management. This project tested the ability of electromagnetic conductivity surveys to describe, by means of regression-tree inference models, spatiotemporal changes in soil salinity at different depths within a complex 10-ha pattern of irrigated plots in an Algerian oasis. Soils were sandy Aridic Salic Solonchaks with a fluctuating saline watertable at less than 2 m. Apparent electrical conductivity (ECa) was measured by an EM38 device at fixed 10- or 20-m intervals (2889 points) at four sampling dates between March 2009 and November 2010. For calibration and validation purposes, soil salinity was measured from a 1 : 5 diluted extract (EC1:5) in three layers (0–10, 10–25, 25–50 cm) at 30 of these points randomly chosen at each date. ECa measurements were used to predict EC1:5 using calibration regression trees created with the software Cubist, including either parameters specific to the study site (specific model) or more general parameters (general model), allowing extrapolation to other sites. Performance of regression tree predictions was compared with predictions derived from a multiple linear regression (MLR) model adjusted for each date using the software ESAP. Salinity was better predicted by Cubist regression tree models than MLR models. For the deep layer (25–50 cm), Cubist models were more accurate with the specific model (r2 = 0.8, RMSE = 1.6 dS/m) than the general model (r2 = 0.4, RMSE = 2.5 dS/m). Prediction accuracy of both models decreased from the bottom to the top of the soil profile. Salinity maps showed high inter-plot variability, which was captured better by the more flexible regression-tree inference models than the classic MLR models, but they need to build site-specific prediction models. Overall, the monitoring surveys, combined with the Cubist prediction tool, revealed both the seasonal dynamics and spatial variability of salinity at different depths.

Geostatistical monitoring of soil salinity in Uzbekistan by repeated EMI surveys

Soil salinization in the lower reaches of Amudarya is a constant threat. High seepage losses in irrigation water delivery network and deteriorated drainage network result in rising groundwater tables. The shallow groundwater table contributes to salinization of the rooting zone which is tackled by leaching at the end or beginning of the vegetation season. However, there is growing concern that the efficiency of the leaching with application of high amounts of water is low, and besides that the leaching effects are not long-lasting. To monitor local soil salinity an efficient strategy is developed, using electromagnetic induction (EMI) meter measurements. The monitoring strategy is applied and evaluated in a case study in Uzbekistan. The study area of 80ha was surveyed in 2008–2011, at the end of the vegetation season (October–November), with the EMI in vertical dipole mode. In addition, at 142 calibration locations (including 28 revisited) both EMI was recorded and the soil was sampled to determine electrical conductivity (ECe) in laboratory. Log-transformed values of EMI and ECe were used to fit a simple linear regression model. Maps of ECe at the four time points were obtained by simulating multiple maps of log(EMI) using the ordinary kriging model. Besides, multiple vectors of regression coefficients were simulated, which were used to transform the simulated log(EMI) maps. Finally, simulated maps of regression residuals were added to the transformed log(EMI) fields, and backtransformed. Besides maps with predicted ECe the simulations were used to derive maps of the probability that ECe exceeds the critical threshold of 8dSm−1, and a map of the predicted linear temporal trend in ECe. The results show that in 2008–2011 most of the area was not saline, with only several spots reaching a predicted ECe of 6dSm−1. The probabilities that ECe exceeds the critical threshold of 8dSm−1 were small, and the predicted percentage of the area where ECe exceeds this threshold was very small. At most places a slight positive linear trend in ECe was predicted, but this predicted local trend was not significant at most places. Two areas of concern could be distinguished. First, spots occur displaying pronounced fluctuations in salinity from year to year with peaks close to the critical threshold, suggesting that these areas might be prone to soil salinization over short periods of time. Second, in the central part of the area a gradual build-up of soil salinity was seen, which calls for interventions to halt or reverse the build-up.

Wireless Sensor Network Node Deployment Method for Soil Salinization Monitoring

Soil salinization is one form of soil degradation. In this paper, the formation of physical topology known as node deployment in wireless sensor networks is studied based on the characteristics of wireless sensor networks. After calculating the value of the edge length of the grid, the minimum number nodes achieved according to the relationship between the radius of node sensing and radius of node communication. For data transmission, a mobile sink energy efficient routing algorithm based on the shortest path was applied to reduce the energy consumption.

Digital assessment of soil-salinity dynamics after a major flood in the Niger River valley

Salinisation threatens the sustainability of irrigated paddy fields in western Africa. To reclaim saline soils and improve the management of irrigated paddy fields, it is necessary to evaluate and monitor soil salinity in space and time. Proximal soil sensing by apparent electrical resistivity (ER) measurements can help to survey soil salinity. This study aimed to assess changes in the spatial pattern of salinity in vertisols using high resolution 3D mapping at 2 dates on an 8-ha experimental site located at Kollo, Niger, along the Niger River. For each measurement campaign, the survey combined ER and electrical conductivity (EC) measurements of a 1:5 soil:water extract. This procedure was performed in May 2008 and again in March 2011 after a 100-year flood of the study area by the Niger River. EC measurements were performed on soil samples collected at 3 depths at 140 points. ER measurements were performed at 420 points at 2 and 4 depths for the first and second campaigns, respectively. We built an inference model of EC using a regression-tree method for 3 prediction depths. The predictive data were the two ER measurements, their ratio and a qualitative description of land use. The maps generated by ordinary kriging of predicted EC revealed strong lateral salinity gradients ranging from 0.3 to 5.0dS/m (RMSE=0.79–0.83dS/m). Vertical variability in salinity was also significant and its pattern could be explained by land use, mainly irrigation intensity. In 2011, salt stocks in the upper 60cm of the soil ranged from low stocks (<2kg/m2) to extremely high stocks along the levee border (5.5–8.0kg/m2). Comparisons of the 2008 and 2011 salinity maps at 3 depths indicated stable spatial patterns and highlighted the high resilience of the system. Consequently, desalinisation efforts can only minimise soil salinity in the upper cultivated horizon.