Assessing salt-affected soils using remote sensing, solute modelling, and geophysics

Farifteh, Jamshid; Farshad, A.; George, R.J.

doi:10.1016/j.geoderma.2005.02.003

Cited by 263 publications

(129 citation statements)

References 61 publications

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“…Their results showed that the use of Landsat ETM+ data bands 4, 5 and 7 in combination with all three types of ancillary data yielded the most accurate soil salinity map, with 83.6% overall accuracy. Additionally, Douaoui et al [55], Farifteh et al [56] and Eldeiry and Garcia [57] agreed that an integrated approach using remote sensing techniques in addition to ancillary data such as field data, topography and spatial models geophysical surveys can improve the development of high quality soil salinity maps. Using multispectral sensors for soil salinity research has also been studied by Goossens et al [58].…”

Section: Multispectral Satellite Sensors For Mapping and Monitoring Smentioning

confidence: 99%

Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review

Allbed¹,

Kumar²

2013

ARS

302

159

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Soil salinity is a serious environmental problem especially in arid and semiarid areas. It either occurs naturally or is human-induced. High levels of soil salinity negatively affect crop growth and productivity leading land degradation ultimately. Thus, it is important to monitor and map soil salinity at an early stage to enact effective soil reclamation program that helps lessen or prevent future increase in soil salinity. Remote sensing has outperformed the traditional method for assessing soil salinity offering more informative and professional rapid assessment techniques for monitoring and mapping soil salinity. Soil salinity can be identified from remote sensing data obtained by different sensors by way of direct indicators that refer to salt features that are visible at the soil surface as well as indirect indicators such as the presence of halophytic plant and assessing the performance level of salt-tolerant crops. The purposes of this paper are to 1) discuss some soil salinity indicators; 2) review the satellite sensors and methods used for remote monitoring, detecting and mapping of soil salinity, particularly in arid and semi-arid regions; 3) review various spectral vegetation and salinity indices that have been developed and proposed for soil salinity detection and mapping, with an emphasis on soil salinity mapping and assessment in arid and semi-arid regions; and 4) highlight the most important issues limiting the use of remote sensing for soil salinity mapping, particularly in arid and semi-arid regions.

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Section: Multispectral Satellite Sensors For Mapping and Monitoring Smentioning

confidence: 99%

Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review

Allbed¹,

Kumar²

2013

ARS

302

159

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“…Soil salinization negatively affects crop growth and productivity, especially in arid and semi-arid areas where evaporation exceeds rainfall [1]. The global extent of primary salt-affected soils is 955 M ha, while secondary salinization affects some 77 M ha, with 58% of these are situated in irrigated areas [2].…”

Section: Introductionmentioning

confidence: 99%

“…The global extent of primary salt-affected soils is 955 M ha, while secondary salinization affects some 77 M ha, with 58% of these are situated in irrigated areas [2]. Twenty percent of all irrigated land is salt-affected [2], and this proportion increases as a consequence of human activities and increasing population pressure particularly in countries like China where overpopulation poses a significant threat to the eco-environment [1,3]. Thus, it is imperative to detect, monitor, and map soil salinity over space and time to prevent further land degradation, and to ensure the sustainable development of agriculture [4,5].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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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°) OPEN ACCESSRemote Sens. 2015, 7 8804 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.

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“…Soil salinization is characterized by its evolution in both time and space (Abbas et al, 2013). Therefore, the use of traditional methods (laboratory analysis, field survey) for its monitoring is insufficient and unsuited to the rate of evolution of this phenomenon and is demanding high costs, contrariwise to the optical satellite imagery that can be a powerful tool for mapping and continuous monitoring of the progression of this phenomenon by its synoptic coverage and the sensitivity of the electromagnetic signal to soil parameters at the first centimeters of the surface layer, such as the dielectric constant that is directly related to the salt content of the soil (Farifteh et al, 2006;Metternichet and Zinck, 2003;Mougenot and Pouget, 1993).…”

Section: Introductionmentioning

confidence: 99%

Mapping soil salinity in irrigated land using optical remote sensing data

Lhissoui¹,

Harti²,

Chokmani

2014

EJSS

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Article Info Received : 01.05.2014 Accepted : 14.10.2014 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.

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Assessing salt-affected soils using remote sensing, solute modelling, and geophysics

Cited by 263 publications

References 61 publications

Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review

Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review

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

Mapping soil salinity in irrigated land using optical remote sensing data

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