Evaluating the characteristics of soil vis-NIR spectra after the removal of moisture effect using external parameter orthogonalization

Ya, Liu; Deng, Chao; Lü, Yuanyuan; Shen, Qianyan; Zhao, Haifeng; Tao, Y.; Pan, Xinfu

doi:10.1016/j.geoderma.2020.114568

Cited by 30 publications

(11 citation statements)

References 54 publications

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“…Diffuse reflectance spectra are particularly sensitive to soil particle size, shape and aggregation, with the result that prediction accuracy of soil properties is very sensitive to soil particle fineness, sample surface roughness and soil moisture (Chang et al, 2001;Liu et al, 2020;Manage et al, 2018;Nduwamungu et al, 2009;Reeves, 2010;Sun, 2021;Tekin et al, 2012). Not surprisingly then, soil properties such as texture, C and N content, CEC and pH have been estimated by NIR and VNIR with widely varying degrees of success (Gates, 2018;Nduwamungu et al, 2009;Reyna et al, 2017;Viscarra Rossel et al, 2006).…”

Section: Basic Features Of Nir Vnir and Mir Reflectance Spectramentioning

confidence: 99%

Estimating soil chemical properties by diffuse reflectance spectroscopy: Promise versus reality

McBride

2021

European J Soil Science

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Section: Basic Features Of Nir Vnir and Mir Reflectance Spectramentioning

confidence: 99%

Estimating soil chemical properties by diffuse reflectance spectroscopy: Promise versus reality

McBride

2021

European J Soil Science

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“…These include spectra transformation, and dividing the spectral data into different classes of moisture content, each of which will have specific calibration models. Most reported spectra transformation methods are external parameter orthogonalization (EPO), orthogonal signal correction (OSC), piecewise direct standardization (PDS), and direct standardization (DS) (Wold et al, 1998;Pinto et al, 2012;Roudier et al, 2017;Liu et al, 2020;Nawar et al, 2020;Wijewardane et al, 2020). The EPO and OSC correct the vis-NIR spectra for orthogonality neutralization, while the DS and PDS algorithms transfer the sample spectra from one (e.g., on-line scanning) to another sampling mode (e.g., laboratory scanning).…”

Section: Introductionmentioning

confidence: 99%

Removal of external influences from on-line vis-NIR spectra for predicting soil organic carbon using machine learning

Munnaf

Mouazen

2022

CATENA

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External factors including moisture content negatively affect the prediction accuracy of soil organic carbon (SOC) using on-line visible and near-infrared (vis-NIR) spectroscopy. This study compared the performances of four algorithms to remove the moisture content effect [direct standardization (DS), piecewise direct standardization (PDS), external parameter orthogonalization (EPO), and orthogonal signal correction (OSC)] against noncorrected (NC) spectral models developed with partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and M5Rules regression. An on-line soil sensing platform coupled with a vis-NIR spectrophotometer (305-1700 nm) was used to scan twelve agricultural fields in Belgium and France. A total of 372 soil samples collected during the on-line measurement were divided into a calibration (260) and a prediction (112) dataset, using the Kennard-Stone algorithm. The latter set together with identical laboratorymeasured 112 dry soil spectra formed a transfer dataset to develop EPO, DS and PDS correction matrices. Results showed that models after EPO, PDS and OSC corrections resulted in improved accuracy [coefficient of determination (R 2 ) = 0.60-0.82, root mean square error (RMSE) = 16.1-5.7 g kg − 1 )], compared to the NC models (R 2 = 0.58-0.73, RMSE = 16.5-6.8 g kg − 1 ), whereas the DS (R 2 = − 0.10 to 0.26, RMSE = 26.8-21.9 g kg − 1 ) provided deteriorated prediction accuracy. The EPO and OSC models provided better prediction accuracy than that of the PDS corrected models. The OSC-M5Rules (R 2 = 0.82, RMSE = 5.7 g kg − 1 ) obtained the highest accuracy followed by EPO-M5Rules (R 2 = 0.74, RMSE = 6.7 g kg − 1 ) and NC-M5Rules (R 2 = 0.73, RMSE = 6.8 g kg − 1 ), which outperformed all PLSR, RF and SVM models. Therefore, on-line vis-NIR spectra should be corrected with the OSC algorithm before calibrating a machine learning model for accurate prediction of SOC.

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“…Thus, several previous studies have demonstrated that calibration models are more accurate when wavelength variable selection methods are applied [22,[42][43][44][45]. Among them, the most commonly used method is Pearson correlation analysis [6,30,46,47]. To a certain extent, the correlation coefficient (r) and significance level reflect the correlation relationship between soil elements and wavelength variables [46], and one or more bands with the highest correlation coefficient can be used to build the model.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy

Guo

Gao

et al. 2021

Remote Sensing

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Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has proven to be a rapid, nondestructive, and effective technique for predicting soil properties in general and potassium, phosphorous, and organic matter in particular. However, the successful estimation of soil nutrient content by visible and near-infrared (Vis-NIR) reflectance spectroscopy depends on proper calibration methods (including preprocessing transformation methods and multivariate methods for regression analysis) and the selection of appropriate variable selection techniques. In this study, raw spectrum and 13 preprocessing transformations combined with 2 variable selection methods (competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA)) and 2 regression algorithms (support vector machine (SVM) and partial least squares regression (PLSR)), for a total of 56 calibration methods, were investigated for modeling and predicting the above three soil nutrients using hyperspectral Vis-NIR data (400–2450 nm). The results show that first-order derivatives based on logarithmic and inverse transformations (FD-LGRs) can provide better predictions of soil available potassium and phosphorous, and the best form of soil organic matter transformation is SG+MSC. CARS was superior to the SPA in selecting effective variables, and the PLSR model outperformed the SVM models. The best estimation accuracies (R2, RMSE) for soil available potassium, phosphorous, and organic matter were 0.7532, 32.3090 mg/kg; 0.7440, 6.6910 mg/kg; and 0.9009, 3.2103 g/kg, respectively, and their corresponding calibration methods were (FD-LGR)/SPA/PLSR, (FD-LGR)/SPA/PLSR, and SG+MSC/CARS/SVM, respectively. Overall, for the prediction of the soil nutrient content, organic matter was superior to available phosphorous, followed by available potassium. It was concluded that the application of hyperspectral images (Vis-NIR data) was an efficient method for mapping and monitoring soil nutrients at the regional scale, thus contributing to the development of precision agriculture.

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Evaluating the characteristics of soil vis-NIR spectra after the removal of moisture effect using external parameter orthogonalization

Cited by 30 publications

References 54 publications

Estimating soil chemical properties by diffuse reflectance spectroscopy: Promise versus reality

Estimating soil chemical properties by diffuse reflectance spectroscopy: Promise versus reality

Removal of external influences from on-line vis-NIR spectra for predicting soil organic carbon using machine learning

Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy

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