Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra

Sidike, Ayetiguli; Zhao, Shuang; Wen, Yuming

doi:10.1016/j.jag.2013.06.002

Cited by 125 publications

(100 citation statements)

References 58 publications

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“…Other factors contributing to the high performance of the models were the negligible influence of moisture content on the image spectra, because the image was selected in the early summer when soil was mostly dried, and a relatively uniform composition of clay minerals in the study area, with the dominance of montmorillonite [95,96], decreasing the influence of clay mineral type on the spectra variability. The good performance and accuracy of the predictive models in this study may be attributed also to the effectiveness of atmospheric correction in removing radiometric distortions and the retrieval of true reflectance values [50,97]. However, the occurrence of negative values for soil salinity and clay content proves the need to improve the data processing and modeling approach for the lower range values of these two properties.…”

Section: Modeling Soil Properties Using Mars and Plsr Methodsmentioning

confidence: 69%

“…This is because MARS is a non-linear and flexible modeling method, capable of fitting complex and non-linear relationships and specifying the interaction effects, as well as the linear combinations of variables [53,54,90]. Several studies showed that the prediction of soil properties implies some non-linear relationship between the measured soil values and soil reflectance spectra [26,50,52]. MARS makes it possible to reduce the effects of the multistep process and any other unknown nonlinearity and, therefore, produces superior and more effective models compared to the PLSR method [90].…”

Section: Modeling Soil Properties Using Mars and Plsr Methodsmentioning

confidence: 99%

“…The PLSR approach has inference capabilities useful to model probable linear relationships between the reflectance spectra and soil properties [26] and enables the modeling of several response variables simultaneously, at the same time effectively addressing strongly collinear and noisy predictor variables [48]. PLSR has been used successfully for mapping soil properties (e.g., [26,[49][50][51]), including also soil variables characterized by high spatial variability and complex nonlinear relationships [26,52]. In this context, a non-linear predictive model for soil mapping may offer a better solution.…”

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: Modeling Soil Properties Using Mars and Plsr Methodsmentioning

confidence: 69%

Section: Modeling Soil Properties Using Mars and Plsr Methodsmentioning

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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“…There is renewed interest in the use of broadband multispectral imagery for regional soil salt content prediction (e.g., [4,5,[9][10][11][12][13][14][15][16]). Generally, these studies include (1) the bands' selection and investigation of spectral indices, including salinity indices and vegetation indices (e.g., [12,13]); (2) the optimized combination of bands and spectral indices for modeling, such as [4,5,14]; and (3) the long-term soil salinity dynamic analysis, such as in References [15,16].…”

Section: Introductionmentioning

confidence: 99%

Agricultural Soil Alkalinity and Salinity Modeling in the Cropping Season in a Spectral Endmember Space of TM in Temperate Drylands, Minqin, China

Sun

Jiang

2016

Remote Sensing

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This paper presents the potential of the four-image spectral endmember (EM) space comprising sand (SL), green vegetation (GV), saline land (SA), and dark materials (DA), unmixed from Landsat TM/ETM+ to map dryland agricultural soil alkalinity and salinity (i.e., soil alkalinity (pH) and soil electrical conductivity (EC)) in the shallow root zone (0-20 cm) using partial least squares regression (PLSR) and an artificial neural network (ANN). The results reveal that SA, SL, and GV fractions at the subpixel level, and land surface temperature (LST) are necessary independent variables for soil EC modeling in Minqin Oasis, a temperate-arid system in China. The R 2 (coefficient of determination) of the optimized parameters with the ANN model was 0.79, the root mean squared error (RMSE) was 0.13, and the ratio of prediction to deviation (RPD) was 1.95 when evaluated against all sampled data. In addition to the aforementioned four variables, the DA fraction and the recent historical SA fraction (SAH) in the spring dry season in 2008 were also helpful for soil pH modeling. The model performance is R 2 = 0.76, RMSE = 0.24, and RPD = 1.96 for all sampled data. In summary, the stable EMs and LST space of TM imagery with an ANN approach can generate near-real-time regional soil alkalinity and salinity estimations in the cropping period. This is the case even in the critical agronomic range (EC of 0-20 dS·m −1 and pH of 7-9) at which researchers and policy-makers require near-real-time crop management information.

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Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra

Cited by 125 publications

References 58 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

Agricultural Soil Alkalinity and Salinity Modeling in the Cropping Season in a Spectral Endmember Space of TM in Temperate Drylands, Minqin, China

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