Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data

Douaoui, Abdelkader; Nicolas, Hervé; Walter, Christian

doi:10.1016/j.geoderma.2005.10.009

Cited by 386 publications

(156 citation statements)

References 18 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%

“…Douaoui et al [55] have proposed three salinity indices (Table 1) produced from SPOT XS imagery to detect and map soil salinity hazards in a semi-arid environment in Algeria. They found that those indices were strongly correlated with measured values, but considerably underestimated the salinity of areas with high levels of surface salt.…”

Section: Vegetation and Soil Indicesmentioning

confidence: 99%

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

Allbed¹,

Kumar²

2013

ARS

297

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%

Section: Vegetation and Soil Indicesmentioning

confidence: 99%

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

Allbed¹,

Kumar²

2013

ARS

297

159

View full text Add to dashboard Cite

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“…The commonly used indices can be generated from green/red [26], red/NIR [28], NIR/SWIR [29] and SWIR/SWIR bands [30]. This demonstrates from a different angle that the process of soil salinization may change soil surface reflectance at most spectral bands.…”

Section: Soil Salinity Retrieval From Multi-spectral Sensor Data Basementioning

confidence: 99%

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

Fan

Liu

Tao³

et al. 2015

Remote Sensing

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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 R 2 (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 . 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 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.

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“…Salinity Index (SI). Reflectance data of Landsat ETM+ band 1 (B1) and band 3 (B3) could be applied to calculate the Salinity Index [36][37][38][39] as follows:…”

Section: Spectral Indicesmentioning

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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Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data

Cited by 386 publications

References 18 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

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

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

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