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

Nawar, Said; Buddenbaum, Henning; Hill, Joachim

doi:10.3390/rs70201181

Cited by 73 publications

(35 citation statements)

References 89 publications

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“…The coefficients of determination (R 2 ) were relatively good, ∼0.7 in Fan et al (2015) for ALI data, ∼0.8 in Nawar et al (2015) for ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data and even 0.992 in Sidike et al (2014) for Quickbird data. Except for PLSR, several nonlinear and linear models have been developed.…”

Section: Introductionmentioning

confidence: 96%

“…Currently, several models have been applied to part of these multi-spectral data, including PLSR (Partial Least Square Regression) (Sidike et al, 2014;Fan et al, 2015;Nawar et al, 2015). The coefficients of determination (R 2 ) were relatively good, ∼0.7 in Fan et al (2015) for ALI data, ∼0.8 in Nawar et al (2015) for ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data and even 0.992 in Sidike et al (2014) for Quickbird data.…”

Section: Introductionmentioning

confidence: 98%

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Towards decadal soil salinity mapping using Landsat time series data

Fan

Weng

Tao³

2016

International Journal of Applied Earth Observation and Geoinfor

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 98%

Towards decadal soil salinity mapping using Landsat time series data

Fan

Weng

Tao³

2016

International Journal of Applied Earth Observation and Geoinfor

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“…Salt-affected soils can be discriminated using the visible and infrared portions of remote sensing spectra [2,[6][7][8][9]. 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].…”

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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“…The fi nal model is built from these functions and their products (Hastie et al, 2009). This method was used to estimate soil properties and biomass through regression with variables derived from satellite imagery (Fillipi et al, 2014;Nawar et al, 2014;Nawar et al, 2015). Again, no example of ISA evaluation has been found.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Drzewiecki

2016

Geodesy and Cartography

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Abstract:In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

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

Cited by 73 publications

References 89 publications

Towards decadal soil salinity mapping using Landsat time series data

Towards decadal soil salinity mapping using Landsat time series data

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

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

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