Rapid estimation of soil engineering properties using diffuse reflectance near infrared spectroscopy

Waruru, Bernard K.; Shepherd, Keith D.; Ndegwa, George M.; Kamoni, P.; Sila, Andrew

doi:10.1016/j.biosystemseng.2014.03.003

Cited by 61 publications

(46 citation statements)

References 22 publications

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“…Yitagesu et al (2009) reported good accuracy for the PL ( R 2 = 0.71 and RMSEP = 0.57) and LL ( R 2 = 0.87 and RMSEP = 0.54); however, neither the number of samples nor the limits’ ranges were reported. The estimation accuracy for the LL ( R 2 = 0.71, RMSEP = 5.23%, and SRMSE = 0.15) is comparable to results by Waruru et al (2014), who reported R 2 of 0.74, RMSEP of 9.9%, and SRMSE = 0.13. The visible and NIR region from the current study and the NIR region from the Waruru et al (2014) study used to estimate the PL showed lower accuracy.…”

Section: Resultssupporting

confidence: 77%

“…The estimation accuracy for the LL ( R 2 = 0.71, RMSEP = 5.23%, and SRMSE = 0.15) is comparable to results by Waruru et al (2014), who reported R 2 of 0.74, RMSEP of 9.9%, and SRMSE = 0.13. The visible and NIR region from the current study and the NIR region from the Waruru et al (2014) study used to estimate the PL showed lower accuracy. The lower accuracy could be due to the smaller range and variation of limits used for the development of the Vis‐NIRS cross‐validation model and independent validation.…”

Section: Resultssupporting

confidence: 77%

“…The observed performance of the models reflects the stable relationship between Atterberg limits and spectrally active soil components such as clay mineralogy, clay content, and moisture content. Few studies have reported independent validation of Vis‐NIRS determination of the PL and LL (Yitagesu et al, 2009) and of NIRS determination of the PL and LL (Waruru et al, 2014). Yitagesu et al (2009) reported good accuracy for the PL ( R 2 = 0.71 and RMSEP = 0.57) and LL ( R 2 = 0.87 and RMSEP = 0.54); however, neither the number of samples nor the limits’ ranges were reported.…”

Section: Resultsmentioning

confidence: 99%

“…Few studies have been conducted to evaluate the application of spectral information to estimate soil engineering properties. For example, Waruru et al (2014) used NIRS (800–2500 nm) and presented good estimation performance for the PL (11–39%), with a root mean square error of prediction (RMSEP) of 4.6%, and the LL (22–91%), with a RMSEP of 9.9% for Kenyan surface and subsurface soil samples ( n = 120). Good estimation performance was also reported for the PL (RMSEP of 0.57) and LL (RMSEP of 0.54) for Ethiopian samples (number of samples and ranges of the limits were not provided) using Vis‐NIRS (Yitagesu et al, 2009).…”

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confidence: 99%

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Estimating Atterberg Limits of Fine‐Grained Soils by Visible–Near‐Infrared Spectroscopy

Rehman

Knadel

Kayabalı³

et al. 2019

Vadose Zone Journal

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Two methods each to measure each Atterberg limit (SL, PL, and LL) were evaluated. A Vis‐NIRS technique successfully estimated Atterberg limits for fine‐grained soils. The Atterberg limits correlated with typical clay signatures in the Vis‐NIR spectrum Vis‐NIRS model performance was independent of the method used to generate the reference data. The Atterberg limits (shrinkage limit [SL], plastic limit [PL], and liquid limit [LL]) describe the physico‐mechanical behavior of soils and thus are crucial for civil and agricultural applications. Conventional laboratory methods for measurement of these limits are tedious and costly for a large number of samples. Our objective was to develop visible–near‐infrared spectroscopy (Vis‐NIRS, from 400–2500 nm) based reliable models to estimate the Atterberg limits. Two conventional methods for each Atterberg limit were used to generate the reference data: paraffin wax and Hg methods for SL; rolling and motorized devices for PL; and Casagrande cup and drop‐cone penetrometer methods for LL. Calibration models were built on 80% of the data using partial least squares regression and validated with the remaining 20% of the dataset. The Vis‐NIRS independent validation of LL showed very good estimation with standardized RMSE (SRMSE = RMSE/Range) of 0.16 and 0.15, respectively, for LLdrop‐cone and LLCasagrande methods. Similarly for PL, the Vis‐NIRS estimation accuracy was quite good with SRMSE values of 0.18 and 0.22, respectively, for PLmotorized and PLrolling. Reasonably good estimation was obtained for the SLparaffin and SLHg with SRMSE of 0.25 for both methods. The results suggest that the Vis‐NIRS calibration models and the accuracy following validation were similar for the pair of methods used for the SL, PL, and LL. Finally, analyses of the model regression coefficients revealed that the important wavelengths to estimate the SL, PL, and LL in the Vis‐NIR regions were present across the entire Vis‐NIR spectrum and were strongly related to clay type and content.

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Section: Resultssupporting

confidence: 77%

Section: Resultssupporting

confidence: 77%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Estimating Atterberg Limits of Fine‐Grained Soils by Visible–Near‐Infrared Spectroscopy

Rehman

Knadel

Kayabalı³

et al. 2019

Vadose Zone Journal

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“…The low RMSE value obtained by these authors was due to the homogeneity of soil types and the large size of the experimental area (53.6 ha). For the prediction of clay content of soils in Kenia, Waruru et al (2014) calculated R 2 = 0.5 and RMSE = 16.0 % for the validation set. These authors claimed that the DRS technique proved little efficient because of the high heterogeneity of the soil samples.…”

Section: Clay = 4368 + (-7271×r420) + (4301×r440) + (-31600×r460) +mentioning

confidence: 99%

Potential of Spectroradiometry to Classify Soil Clay Content

Dotto¹,

Dalmolin²,

Caten

et al. 2016

Rev. Bras. Ciênc. Solo

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Combining Vis–NIR spectroscopy and advanced statistical analysis for estimation of soil chemical properties relevant for forest road construction

Mousavi

Abdi

Knadel

et al. 2021

Soil Science Soc of Amer J

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A thorough quantification of soil chemical properties is essential for assessing the engineering properties of forest soils for road design, construction, and maintenance. Here, we investigate the applicability of visible-near-infrared (Vis-NIR) spectroscopy in conjunction with advanced statistical analysis for estimation of soil chemical properties. Sixty forest soil samples were collected and analyzed for pH, electrical conductivity (EC), CaCO 3 , organic matter (OM), and cation exchange capacity (CEC) with established laboratory methods. The spectral measurements were performed with a Vis-NIR spectrometer within a range of 350-2,500 nm. To estimate abovementioned soil properties from reflectance spectra, advanced statistical techniques including partial least squares regression (PLSR), hybrid partial least squares and artificial neural networks (PLS-DI-ANN) models, hybrid partial least squares and adaptive neural fuzzy inference system (PLS-DI-ANFIS) models, as well as narrow band spectral indices were applied. The obtained results indicate that the PLS-DI-ANFIS models show great potential for the estimation of pH, EC, OM, and CEC from reflectance spectra and their first derivatives, exhibiting higher R 2 values and lower RMSE than the other investigated models. The estimation accuracy for CaCO 3 , however, was low for all applied methods. The results confirm that Vis-NIR spectroscopy may be applied as a rapid and cost-efficient alternative to standard chemical soil analysis techniques, aiding forest road design, construction, and maintenance.

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Rapid estimation of soil engineering properties using diffuse reflectance near infrared spectroscopy

Cited by 61 publications

References 22 publications

Estimating Atterberg Limits of Fine‐Grained Soils by Visible–Near‐Infrared Spectroscopy

Estimating Atterberg Limits of Fine‐Grained Soils by Visible–Near‐Infrared Spectroscopy

Potential of Spectroradiometry to Classify Soil Clay Content

Combining Vis–NIR spectroscopy and advanced statistical analysis for estimation of soil chemical properties relevant for forest road construction

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