Predicting Soil Salinity with Vis–NIR Spectra after Removing the Effects of Soil Moisture Using External Parameter Orthogonalization

Ya, Liu; Pan, Xinfu; Wang, Changkun; Li, Yanli; Shi, Rongjie

doi:10.1371/journal.pone.0140688

Cited by 22 publications

(20 citation statements)

References 32 publications

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“…It is interesting to discuss the advantages of the modelling approach used in this study over the conventional methods (i.e., EPO, DS, and OSC) used for removing external effects from soil spectra. Several studies have used the EPO to remove the influences of known external factors, e.g., MC [6,51], soil roughness, aggregation, and ambient temperature [52]. However, both EPO and SD have not been reported to remove the influences of unknown factors, e.g., those frequently encountered during the on-line soil sensing such as noise, and presence of stones and plant roots and residues.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Secondary Soil Properties by Fusion of Laboratory and On-Line Measured Vis–NIR Spectra

2019

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Visible and near infrared (vis–NIR) diffuse reflectance spectroscopy has made invaluable contributions to the accurate estimation of soil properties having direct and indirect spectral responses in NIR spectroscopy with measurements made in laboratory, in situ or using on-line (while the sensor is moving) platforms. Measurement accuracies vary with measurement type, for example, accuracy is higher for laboratory than on-line modes. On-line measurement accuracy deteriorates further for secondary (having indirect spectral response) soil properties. Therefore, the aim of this study is to improve on-line measurement accuracy of secondary properties by fusion of laboratory and on-line scanned spectra. Six arable fields were scanned using an on-line sensing platform coupled with a vis–NIR spectrophotometer (CompactSpec by Tec5 Technology for spectroscopy, Germany), with a spectral range of 305–1700 nm. A total of 138 soil samples were collected and used to develop five calibration models: (i) standard, using 100 laboratory scanned samples; (ii) hybrid-1, using 75 laboratory and 25 on-line samples; (iii) hybrid-2, using 50 laboratory and 50 on-line samples; (iv) hybrid-3, using 25 laboratory and 75 on-line samples, and (v) real-time using 100 on-line samples. Partial least squares regression (PLSR) models were developed for soil pH, available potassium (K), magnesium (Mg), calcium (Ca), and sodium (Na) and quality of models were validated using an independent prediction dataset (38 samples). Validation results showed that the standard models with laboratory scanned spectra provided poor to moderate accuracy for on-line prediction, and the hybrid-3 and real-time models provided the best prediction results, although hybrid-2 model with 50% on-line spectra provided equally good results for all properties except for pH and Na. These results suggest that either the real-time model with exclusively on-line spectra or the hybrid model with fusion up to 50% (except for pH and Na) and 75% on-line scanned spectra allows significant improvement of on-line prediction accuracy for secondary soil properties using vis–NIR spectroscopy.

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Section: Discussionmentioning

confidence: 99%

Estimation of Secondary Soil Properties by Fusion of Laboratory and On-Line Measured Vis–NIR Spectra

2019

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“…Hyperspectral data, via narrow and fine spectral bands, can capture the deep information hidden in the soil. And it was widely used in analysis of soil physical and chemical properties, such as soil humus structure [ 2 ], soil nutrients [ 3 – 4 ], soil salinity [ 5 ] and soil moisture content [ 6 ]. All above studies have confirmed that hyperspectral data had the advantage of real time, high efficiency and low cost, which could make up the shortcomings of the traditional methods primely.…”

Section: Introductionmentioning

confidence: 99%

Prediction of soil organic carbon in a coal mining area by Vis-NIR spectroscopy

Sun

Niu

2018

PLoS ONE

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Coal mining has led to increasingly serious land subsidence, and the reclamation of the subsided land has become a hot topic of concern for governments and scholars. Soil quality of reclaimed land is the key indicator to the evaluation of the reclamation effect; hence, rapid monitoring and evaluation of reclaimed land is of great significance. Visible-near infrared (Vis-NIR) spectroscopy has been shown to be a rapid, timely and efficient tool for the prediction of soil organic carbon (SOC). In this study, 104 soil samples were collected from the Baodian mining area of Shandong province. Vis-NIR reflectance spectra and soil organic carbon content were then measured under laboratory conditions. The spectral data were first denoised using the Savitzky-Golay (SG) convolution smoothing method or the multiple scattering correction (MSC) method, after which the spectral reflectance (R) was subjected to reciprocal, reciprocal logarithm and differential transformations to improve spectral sensitivity. Finally, regression models for estimating the SOC content by the spectral data were constructed using partial least squares regression (PLSR). The results showed that: (1) The SOC content in the mining area was generally low (at the below-average level) and exhibited great variability. (2) The spectral reflectance increased with the decrease of soil organic carbon content. In addition, the sensitivity of the spectrum to the change in SOC content, especially that in the near-infrared band of the original reflectance, decreased when the SOC content was low. (3) The modeling results performed best when the spectral reflectance was preprocessed by Savitzky-Golay (SG) smoothing coupled with multiple scattering correction (MSC) and first-order differential transformation (modeling R2 = 0.86, RMSE = 2.00 g/kg, verification R2 = 0.78, RMSE = 1.81 g/kg, and RPD = 2.69). In addition, the first-order differential of R combined with SG, MSC with R, SG together with MSC and R also produced better modeling results than other pretreatment combinations. Vis-NIR modeling with specific spectral preprocessing methods could predict SOC content effectively.

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“…Moisture effects on NIR spectroscopy calibrations have been studied for analysis of soils (Liu, Pan, Wang, Li, & Shi, 2015;Minasny et al, 2011;Nocita, Stevens, Noon, & van Wesemael., 2013;Wang & Pan, 2016) and wood extracts (Giordanengo et al, 2008). However, studies on the moisture effects on the performance of NIR spectroscopy calibrations for cereal grain traits analysis are limited.…”

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confidence: 99%

Moisture effects on robustness of sorghum grain protein near‐infrared spectroscopy calibration

et al. 2019

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Background and objectives A near‐infrared (NIR) spectroscopy method was developed for rapid and nondestructive evaluation of protein content of intact sorghum grains. Effect of grain sample moisture variation on the robustness of protein calibration was investigated. Findings An initial NIR protein calibration model with sorghum grains in 7.28%–11.57% moisture content range with coefficient of determination (R2) of 0.94 and standard error of cross‐validation (SECV) of 0.41%, predicted protein content of an external validation set of different varieties with R2 = 0.90, root‐mean‐square error of prediction (RMSEP) = 0.42% and bias = −0.02%. However, when grains with a wider range of moisture content (7.50%–17.75%) were used for validation, prediction errors increased with R2 = 0.72 RMSEP = 0.87% and bias = −0.32%. Inclusion of grains with a wider moisture range to the calibration set improved the performance of the calibration model with a R2 = 0.83, RMSEP = 0.67% with a bias of −0.04%. Conclusions Variation of moisture content in grains affected the performance of the NIR protein calibration model. Likewise, inclusion of moisture variation in the calibration sample set improved the robustness of the model. Significance and novelty In addition to the traits of interest, variation of other physical or chemical traits should also be considered for inclusion into the calibration sample set to improve the robustness of NIR calibration models. This is especially important when NIR spectroscopy methods are developed for evaluation of breeding populations as the future grain samples from numerous crosses may be substantially diverse.

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Predicting Soil Salinity with Vis–NIR Spectra after Removing the Effects of Soil Moisture Using External Parameter Orthogonalization

Cited by 22 publications

References 32 publications

Estimation of Secondary Soil Properties by Fusion of Laboratory and On-Line Measured Vis–NIR Spectra

Estimation of Secondary Soil Properties by Fusion of Laboratory and On-Line Measured Vis–NIR Spectra

Prediction of soil organic carbon in a coal mining area by Vis-NIR spectroscopy

Moisture effects on robustness of sorghum grain protein near‐infrared spectroscopy calibration

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