Modeling and Mapping of Soil Salinity with Reflectance Spectroscopy and Landsat Data Using Two Quantitative Methods (PLSR and MARS)

Nawar, Said; Buddenbaum, Henning; Hill, Joachim; Kozak, Jacek

doi:10.3390/rs61110813

Cited by 136 publications

(85 citation statements)

References 64 publications

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“…Development of MARS models for soil salinity assessment was described in detail in Nawar et al [58,59]. In this study, the MARS analysis was performed using the ARESLab toolbox [77] with selected adaptations based on MATLAB 8.0 software.…”

Section: Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

“…It has been effectively applied in different fields [54][55][56][57] and generally exhibits very high performance compared with other linear and non-parametric regression models, such as principal component regression (PCR) and artificial neural networks (ANN). Nawar et al [58] used PLSR and MARS with soil spectroscopy and Landsat TM/ETM+ data for mapping soil salinity in El-Tina Plain in Egypt and reported that MARS provided better estimations for the soil salinity, yielding better cross-validation R 2 and ratio of performance to deviation (RPD) values than the generally used PLSR method did. The results of this study indicated a potential to extend the modeling capability of PLSR to model and map other soil properties and the need to test MARS models with other than Landsat multispectral data in order to prove their robustness.…”

Section: Introductionmentioning

confidence: 99%

“…The aim of this study was therefore to extend the capabilities of the models elaborated in [58] to map soil salinity, clay content and organic matter and to test the performance of ASTER data in mapping these soil properties using a combination of field sampling and spectral reflectance derived from ASTER imagery. The specific objectives of the study were to: (I) evaluate the potential of ASTER data for the modeling of soil properties; (II) develop predictive models based on ASTER spectra for assessing soil salinity, clay content and organic matter using MARS and to compare its performance to the results acquired with the PLSR method; and (III) map the spatial distribution of soil properties based on ASTER imagery across a large agricultural area.…”

Section: Introductionmentioning

confidence: 99%

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Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

2015

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Modeling and mapping of soil properties has been identified as key for effective land degradation management and mitigation. The ability to model and map soil properties at sufficient accuracy for a large agriculture area is demonstrated using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Soil samples were collected in the El-Tina Plain, Sinai, Egypt, concurrently with the acquisition of ASTER imagery, and measured for soil electrical conductivity (ECe), clay content and soil organic matter (OM). An ASTER image covering the study area was preprocessed, and two predictive models, multivariate adaptive regression splines (MARS) and the partial least squares regression (PLSR), were constructed based on the ASTER spectra. For all three soil properties, the results of MARS models were better than those of the respective PLSR models, with cross-validation estimated R

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Section: Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

2015

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“…A growing body of studies, aided by statistical analyses of field spectroscopy data and satellite remote sensing observations demonstrates that both multispectral [10][11][12][13][14][15] and hyperspectral passive reflectance data can be used to map soil salinization at landscape scales [16]. However, passive optical remote sensing based approaches may be hampered over coastal areas, black-clay soils, and desert areas, due to the smoothness and the white color of the formed crust [2].…”

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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“…Many studies using Landsat TM and ETM+ data have focused on drylands and on soil processes such as erosion and crusting [46,[48][49][50][51]. Very complex procedures, such as SMA, have been adopted to map degraded forests [52][53][54] or to estimate trends of fractional vegetation cover [55].…”

Section: Introductionmentioning

confidence: 99%

Early Identification of Land Degradation Hotspots in Complex Bio-Geographic Regions

et al. 2015

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Abstract:The development of low-cost and relatively simple tools to identify emerging land degradation across complex regions is fundamental to plan monitoring and intervention strategies. We propose a procedure that integrates multi-spectral satellite observations and air temperature data to detect areas where the current status of local vegetation and climate shows evident departures from the mean conditions of the investigated region. Our procedure was tested in Basilicata (Italy), which is a typical bio-geographic example of vulnerable Mediterranean landscape. We grouped Landsat TM/ETM+ NDVI and air temperature (T) data by vegetation cover type to estimate the statistical distributions of the departures of NDVI and T from the respective land cover class means. The pixels characterized by contextual left tail NDVI values and right tail T values that persisted in time (2002)(2003)(2004)(2005)(2006) were classified as critical to land degradation. According to our results, most of the critical areas (88.6%) corresponded to forests affected by erosion and to riparian buffers that are shaped by fragmentation, as confirmed by aerial and in-situ surveys. Our procedure enables cost-effective screenings of complex areas able to identify raising hotspots that require urgent and deeper investigations.

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Modeling and Mapping of Soil Salinity with Reflectance Spectroscopy and Landsat Data Using Two Quantitative Methods (PLSR and MARS)

Cited by 136 publications

References 64 publications

Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

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

Early Identification of Land Degradation Hotspots in Complex Bio-Geographic Regions

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