Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy

Nawar, Said; Buddenbaum, Henning; Hill, Joachim; Kozak, Jacek; Mouazen, Abdul Mounem

doi:10.1016/j.still.2015.07.021

Cited by 242 publications

(143 citation statements)

References 59 publications

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“…Though, it is hard to estimate SOM content of desert soil precisely when it is less than 2%. In this research, the spectral reflectance displayed the higher [51]. Comparing our results with previous research, in this study, not only considering the single band reflectance, we excavated more potential spectral information by using fractional derivative.…”

Section: Discussionsupporting

confidence: 62%

Quantitative Estimation of Organic Matter Content in Arid Soil Using Vis-NIR Spectroscopy Preprocessed by Fractional Derivative

Wang¹,

Tiyip²,

Ding³

et al. 2017

Journal of Spectroscopy

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Soil organic matter (SOM) content is an important index to measure the level of soil function and soil quality. However, conventional studies on estimation of SOM content concerned about the classic integer derivative of spectral data, while the fractional derivative information was ignored. In this research, a total of 103 soil samples were collected in the Ebinur Lake basin, Xinjiang Uighur Autonomous Region, China. After measuring the Vis-NIR (visible and near-infrared) spectroscopy and SOM content indoor, the raw reflectance and absorbance were treated by fractional derivative from 0 to 2nd order (order interval 0.2). Partial least squares regression (PLSR) was applied for model calibration, and five commonly used precision indices were used to assess the performance of these 22 models. The results showed that with the rise of order, these parameters showed the increasing or decreasing trends with vibration and reached the optimal values at the fractional order. A most robust model was calibrated based on 1.8 order derivative of R, with the lowest RMSEC (3.35 g kg

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Section: Discussionsupporting

confidence: 62%

Quantitative Estimation of Organic Matter Content in Arid Soil Using Vis-NIR Spectroscopy Preprocessed by Fractional Derivative

Wang¹,

Tiyip²,

Ding³

et al. 2017

Journal of Spectroscopy

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“…In the modeling process, SVM algorithm maps original input data into a high-dimensional feature space through a kernel function [43]. It is able to deal with large input spaces efficiently [10,44]. For more details about SVM, readers are directed to Vapnik [41].…”

Section: Model Calibration and Validationmentioning

confidence: 99%

“…Several preprocessing methods (i.e., data transformation, spectral smoothing, scatter corrections and spectral derivatives and so on), have been utilized to transform the reflectance spectra, reduce the instrumental noise, enhance spectral features and extract useful spectral information for subsequent modeling [10]. These spectral preprocessing techniques can be mainly divided into two categories: scatter-corrections and spectral-derivatives, according to Rinnan et al [11] and Dotto et al [12], but it should be noted that the accuracies of these preprocessing methods may vary from case to case.…”

Section: Introductionmentioning

confidence: 99%

“…Multivariate statistics are essential to mathematically correlate the spectral data with measured SOM. Recently, interest in using nonlinear modeling techniques is increasing because the relationship between spectral data and SOM is seldom linear [10,18]. Support vector machine (SVM) is one of such techniques, which is able to solve the nonlinear problems usually with high model accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

Hong

Chen

et al. 2018

Remote Sensing

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Visible and near-infrared (VIS-NIR) spectroscopy has been extensively applied to estimate soil organic matter (SOM) in the laboratory. However, if field/moist VIS-NIR spectra can be directly applied to estimate SOM, then much of the time and labor would be avoided. Spectral derivative plays an important role in eliminating unwanted interference and optimizing the estimation model. Nonetheless, the conventional integer order derivatives (i.e., the first and second derivatives) may neglect some detailed information related to SOM. Besides, the full-spectrum generally contains redundant spectral variables, which would affect the model accuracy. This study aimed to investigate different combinations of fractional order derivative (FOD) and spectral variable selection techniques (i.e., competitive adaptive reweighted sampling (CARS), elastic net (ENET) and genetic algorithm (GA)) to optimize the VIS-NIR spectral model of moist soil. Ninety-one soil samples were collected from Central China, with their SOM contents and reflectance spectra measured. Support vector machine (SVM) was applied to estimate SOM. Results indicated that moist spectra differed greatly from dried ground spectra. With increasing order of derivative, the spectral resolution improved gradually, but the spectral strength decreased simultaneously. FOD could provide a better tool to counterbalance the contradiction between spectral resolution and spectral strength. In full-spectrum SVM models, the most accurate estimation was achieved by SVM model based on 1.5-order derivative spectra, with validation R 2 = 0.79 and ratio of the performance to deviation (RPD) = 2.20. Of all models studied (different combinations of FOD and variable selection techniques), the highest validation model accuracy for SOM was achieved when applying 1.5 derivative spectra and GA method (validation R 2 = 0.88 and RPD = 2.89). Among the three variable selection techniques, overall, the GA method yielded the optimal predictability. However, due to its long computation time, one alternative was to use CARS method. The results of this study confirm that a suitable combination of FOD and variable selection can effectively improve the model performance of SOM in moist soil.

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“…In the competitive adaptive reweighted sampling (CARS), the subset corresponding to the minimum root mean square error of cross validation (RMSECV) in the partial least squares (PLS) model was determined using the adaptive reweighted sampling (ARS) technique and the Monte Carlo (MC) sampling technique [3] and is defined as the optimized subset [4] . The MC sampling time was set as 100 and the wavelength variable subset was selected according to the 10-fold RMSECV value in the PLS model.…”

Section: Determination Of Characteristic Wavelengthmentioning

confidence: 99%

Identification of Edible Oil Based on Multi-source Spectra Data Fusion

Yu¹,

Tu²,

Wang³

et al. 2017

Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017

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An approach based on multi-source spectra data fusion for identification of edible oil is proposed. A qualitative model based on fusion of Raman spectra and near-infrared spectroscopy (Raman-NIR) was established and compared with conventional single-spectra model. The spectra data was pre-processed using the moving average method (MA11), the Savitzky-Golay method (SG9), the adaptive iteratively reweighted penalized least squares method (airPLS), the normalization method (Nor), the multiplicative scatter correction method (MSC), and the standard normal variant and standard normal variant transformation de-trending method (SNV-DT). Then, optimized characteristic variables were selected using the competitive adapt i ive reweighted sampling method (CARS-SPA) and the backward interval partial least squares method (BiPLS). Based on that, a model for identification of edible oil was established using the support vector classification method (SVC). The results revealed that the SVC model established can accurately identify and classify eight different edible oil (soybean oil, peanut oil, rapeseed oil, tea seed oil, rice oil, corn oil, sunflower oil, and palm oil). The prediction accuracy for samples in calibration set and prediction set by the proposed model can be 100%, which is superior to that of conventional single-spectra model. The proposed model exhibits excellent generalization capability. Additionally, the study suggests that the Raman-NIR fusion shows improved efficiency in identification of edible oil and great potential for practical application.

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Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy

Cited by 242 publications

References 59 publications

Quantitative Estimation of Organic Matter Content in Arid Soil Using Vis-NIR Spectroscopy Preprocessed by Fractional Derivative

Quantitative Estimation of Organic Matter Content in Arid Soil Using Vis-NIR Spectroscopy Preprocessed by Fractional Derivative

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

Identification of Edible Oil Based on Multi-source Spectra Data Fusion

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