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Visible and near-infrared spectroscopy with chemometrics are able to predict soil physical and chemical properties

  • Soils, Sec 1 • Soil Organic Matter Dynamics and Nutrient Cycling • Research Article
  • Published:
Journal of Soils and Sediments Aims and scope Submit manuscript

Abstract

Purpose

Comparing to conventional laboratory methods, visible–near-infrared reflectance (vis–NIR) spectroscopy is a more practical and cost-effective approach for estimating soil physical and chemical properties.

Materials and methods

This paper aims to build statistical machine learning models to investigate the efficiency of spectral data for comprehensive evaluation of the soil quality indicators. Seventeen physical and chemical properties were measured using standard methods as indicators of soil quality. Soil samples were scanned in the laboratory in the vis–NIR range (350–2500 nm), the calibration set of 31 samples and the validation set of 13 samples for cross-validation and independent validation; twenty-four preprocessing methods were tested to improve predictions, and a partial least squares regression (PLSR) was used to predict soil quality indicators.

Results and discussion

Comparing model indices, the model constructed based on the PLSR machine learning method has a good predictive power (R2 > 0.9, ratio of performance to deviation (RPD) > 3.0). For physical and chemical properties, the bulk density (BD, R2 = 0.97, RPD = 5.90), soil organic matter (SOM, R2 = 0.98, RPD = 8.56), pH (R2 = 0.95, RPD = 4.40), and TN (R2 = 0.98, RPD = 6.67) concentration were predicted. This indicates that the method is suitable for the prediction of these soil elements in this study area. For the heavy properties, except for Mn, Zn, Cd, and As, the other five heavy metal concentrations were well predicted. It can be seen that the prediction ability of the construction model is Hg, Cr, Pb, Ni, and Cu in order of superiority to inferiority. The results show that a combination of spectroscopic and chemometric techniques can be applied as a practical, rapid, low-cost, and quantitative approach for evaluating soil physical and chemical properties in Shaanxi, China.

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Abbreviations

BD:

Bulk density

SW:

Gravimetric soil water

M:

Soil organic matter

TN:

Total nitrogen

NN:

Nitrate nitrogen

AK:

Available potassium

AP:

Available phosphorus

SG:

Savitzky-Golay

FD:

First deviation

SD:

Second deviation

SNV:

Standard normal variate

MSC:

Multiplicative scatter correction

NOR:

Normalization

Max:

Maximum

Min:

Minimum

SD:

Standard deviation

CV:

Coefficient of variation

References

  • Abdi D, Tremblay GF, Ziadi N, Bélanger G, Parent L (2012) Predicting soil phosphorus-related properties using near-infrared reflectance spectroscopy. Sci Soil Sci Soc Am J 76(6):2318

    CAS  Google Scholar 

  • Al-Asadi RA, Mouazen AM (2014) Combining frequency domain reflectometry and visible and near infrared spectroscopy for assessment of soil bulk density. Soil Tillage Res 135:60–70

    Google Scholar 

  • Angelopoulou T, Dimitrakos A, Terzopoulou E, Zalidis G, Theocharis J, Stafilov T, Zouboulis A (2017) Reflectance spectroscopy (Vis-NIR) for assessing soil heavy metals concentrations determined by two different analytical protocols, based on iso 11466 and iso 14869-1. Water Air Soil Pollut 228(11):436

    Google Scholar 

  • Bendor E (2002) Quantitative remote sensing of soil properties. Adv Agron 75(02):173–243

    CAS  Google Scholar 

  • Bishop JL, Lane MD, Dyar MD, Brown AJ (2008) Reflectance and emission spectroscopy study of four groups of phyllosilicates: smectites, kaolinite-serpentines, chlorites and micas. Clay Miner 43(1):35–54

    CAS  Google Scholar 

  • Bo S, Rossel RAV, Mouazen AM, Wetterlind J (2010) Chapter five – visible and near infrared spectroscopy in soil science. Adv Agron 107(107):163–215

    Google Scholar 

  • Buondonno A, Amenta P, Viscarra-Rossel RA, Leone AP (2012) Prediction of soil properties with PLSR and Vis-NIR spectroscopy: application to Mediterranean soils from southern Italy. Curr Anal Chem 8(2)

  • Burger J, Geladi P (2007) Spectral pre-treatments of hyperspectral near infrared images: analysis of diffuse reflectance scattering. J Near Infrared Spectrosc 15(2):29

    CAS  Google Scholar 

  • Cuffney TF (2010) Input, movement and exchange of organic matter within a subtropical coastal black water river-flood plain system. Freshw Biol 19(3):305–320

    Google Scholar 

  • Dhanoa MS, Lister SJ, Sanderson R, Barnes RJ (1995) The link between multiplicative scatter correction (msc) and standard normal variate (snv) transformations of nir spectra. Near Infrared Spectrosc 2(1):43–47

    Google Scholar 

  • Ding J, Yao Y, Wang F (2013) Quantitative remote sensing of soil salinization in arid regions based on three dimensional spectrum Eigen spaces. Acta Pedol Sin 50(5):853–861

    Google Scholar 

  • Dotto AC, Dalmolin RSD, Grunwald S, Ten Caten A, Pereira Filho W (2017) Two preprocessing techniques to reduce model covariables in soil property predictions by Vis-NIR spectroscopy. Soil Tillage Res 172:59–68

    Google Scholar 

  • Ergon R (2014) Principal component regression (PCR) and partial least squares regression (PLSR). In: Granato, Ares G (eds) Mathematical and statistical methods in food science and technology. Wiley, Blackwell, Chichester, pp 121–142

    Google Scholar 

  • Figueroa D (2003) The role of relict vegetation in maintaining physical, chemical, and biological properties in an abandoned stipa -grass agroecosystem. Arid Land Res Manag 17(2):103–111

    Google Scholar 

  • Filippi P, Cattle SR, Bishop TFA, Jones EJ, Minasny B (2018) Combining ancillary soil data with VisNIR spectra to improve predictions of organic and inorganic carbon content of soils. MethodsX 5:551–560

    Google Scholar 

  • Fu C, Gan S, Yuan X, Xiong H, Tian A (2018) Determination of soil salt content using a probability neural network model based on particle swarm optimization in areas affected and non-affected by human activities. Remote Sens 10:1387

    Google Scholar 

  • Gajek R, Barley F, She J (2013) Determination of essential and toxic metals in blood by ICP-MS with calibration in synthetic matrix. Anal Methods 5(9):2193–2202

    CAS  Google Scholar 

  • Gholizadeh A, Carmon N, Klement A, Ben-Dor E, Borůvka L (2017) Agricultural soil spectral response and properties assessment: effects of measurement protocol and data mining technique. Remote Sens 9:1078

    Google Scholar 

  • Gross A, Boyd CE, Seo J (2010) Evaluation of the ultraviolet spectrophotometric method for the measurement of total nitrogen in water. J World Aquacult Soc 30(3):388–393

    Google Scholar 

  • Hbroge N, Gthomsen A, Hgreve M (2004) Prediction of topsoil organic matter and clay content from measurements of spectral reflectance and electrical conductivity. Acta Agric Scand 54(4):232–240

    Google Scholar 

  • Hong YS, Yu L, Chen YY, Liu Y (2017) Prediction of soil organic matter by Vis-NIR spectroscopy using normalized soil moisture index as a proxy of soil moisture. Remote Sens 10(1):28

    Google Scholar 

  • Houba VJG (1997) Methods of soil analysis-chemical methods. Sci Hortic 70(4):342–343

    Google Scholar 

  • Ito K, Kato T, Ona T (2002) Non-destructive method for the quantification of the average particle diameter of latex as water-based emulsions by near-infrared Fourier transform Raman spectroscopy. J Raman Spectrosc 33(6):466–470

    CAS  Google Scholar 

  • Jiang Q, Chen Y, Guo L, Fei T, Qi K (2016) Estimating soil organic carbon of cropland soil at different levels of soil moisture using VIS-NIR spectroscopy. Remote Sens 8:755

    Google Scholar 

  • Karlen DL, Mausbach MJ, Doran JW, Cline RG, Harris RF, Schuman GE (1997) Soil quality: a concept, definition, and framework for evaluation (a guest editorial). Soil Sci Soc Am J 61(1):4–10

    CAS  Google Scholar 

  • Kataoka H, Ueno Y, Makita M (1991) Analysis of o-phosphoamino acids in proteins by gas chromatography with flame photometric detection. Agric Biol Chem 55(6):1587–1592

    CAS  Google Scholar 

  • Kuang B, Mouazen AM (2013) Non-biased prediction of soil organic carbon and total nitrogen with Vis–NIR spectroscopy, as affected by soil moisture content and texture. Biosyst Eng 114(3):249–258

    Google Scholar 

  • Liu YL, Jiang QH, Shi TZ, Fei T, Wang JJ, Liu GL, Chen YY (2014) Prediction of total nitrogen in cropland soil at different levels of soil moisture with Vis/NIR spectroscopy. Acta Agric Scand Sect B 64(3):267–281

    CAS  Google Scholar 

  • Liu JB, Han JC, Zhang Y, Wang HY, Kong H, Shi L (2018) Prediction of soil organic carbon with different parent materials development using visible-near infrared spectroscopy. Spectrochim Acta Part A 204:33–39

    CAS  Google Scholar 

  • Morellos A, Pantazi XE, Moshou D, Alexandridis T, Whetton R, Tziotzios G, Wiebensohn J, Bill R, Mouazenb AM (2016) Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using Vis-NIR spectroscopy. Biosyst Eng 152:104–116

    Google Scholar 

  • Mortimore JL, Marshall LJR, Almond MJ, Hollins P, Matthews W (2004) Analysis of red and yellow ochre samples from clearwell caves and Çatalhöyük by vibrational spectroscopy and other techniques. Spectrochim Acta A Mol Biomol Spectrosc 60(5):1179–1188

    Google Scholar 

  • Mouazen AM, Kuang B, Baerdemaeker JD, Ramon H (2010) Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma 158(1):23–31

    CAS  Google Scholar 

  • Nawar S, Buddenbaum H, Hill J, Kozak J, Mouazen AM (2016) Estimating the soil clay content and organic matter by means of different calibration methods of Vis-NIR diffuse reflectance spectroscopy. Soil Tillage Res 155:510–522

    Google Scholar 

  • Palma LD, Ferrantelli P, Merli C, Petrucci E, Pitzolu I (2007) Influence of soil organic matter on copper extraction from contaminated soil. J Soil Contam 16(3):13

    Google Scholar 

  • Peng XT, Shi TZ, Song AH, Chen YY, Gao WX (2014) Estimating soil organic carbon using VIS/NIR spectroscopy with SVMR and SPA methods. Remote Sens 6:2699–2717

    Google Scholar 

  • Prs V, Marchão RL, Brunet D, Becquer T (2012) The potential of nir spectroscopy to predict soil texture and mineralogy in cerrado latosols. Eur J Soil Sci 63(5):743–753

    Google Scholar 

  • Qiao T, Ren J, Craigie C, Zabalza J, Maltin C, Marshall S (2015) Quantitative prediction of beef quality using visible and NIR spectroscopy with large data samples under industry conditions. J Appl Spectrosc 82(1):137–144

    CAS  Google Scholar 

  • Quraishi MZ, Mouazen AM (2013) A prototype sensor for the assessment of soil bulk density. Soil Tillage Res 134(8):97–110

    Google Scholar 

  • Rodionov A, Pätzold S, Welp G, Pallares RC, Damerow L, Amelung W (2014) Sensing of soil organic carbon using visible and near-infrared spectroscopy at variable moisture and surface roughness. Sci Soil Sci Soc Am J 78(3):949

    Google Scholar 

  • Rossel RAV, Walvoort DJJ, Mcbratney AB, Janik LJ, Skjemstad JO (2006) Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131(1):59–75

    Google Scholar 

  • Rossel RAV, Jeon YS, Odeh IOA, Mcbratney AB (2008) Using a legacy soil sample to develop a mid-ir spectral library. Soil Res 46(1):1–16

    CAS  Google Scholar 

  • Rossel RAV, Behrens T, Ben-Dor E, Brown DJ, Demattê JAM, Shepherd KD (2016) A global spectral library to characterize the world’s soil. Earth Sci Rev 155:198–230

    Google Scholar 

  • Ruberto L, Dias R, Balbo AL, Vazquez SC, Hernandez EA, Cormack WPM (2010) Influence of nutrients addition and bioaugmentation on the hydrocarbon biodegradation of a chronically contaminated Antarctic soil. J Appl Microbiol 106(4):1101–1110

    Google Scholar 

  • Sarathjith MC, Das BS, Wani SP, Sahrawat KL (2014) Dependency measures for assessing the covariation of spectrally active and inactive soil properties in diffuse reflectance spectroscopy. Soil Sci Soc Am J 78(5):1522–1530

    Google Scholar 

  • Schoell A, Zou Y, Huebner D, Urquhart SG, Schmidt T, Fink R et al (2005) A comparison of fine structures in high-resolution x-ray-absorption spectra of various condensed organic molecules. J Chem Phys 123(4):45

    Google Scholar 

  • Shepherd KD, Walsh MG (2002) Development of reflectance spectral libraries for characterization of soil properties. Soil Sci Soc Am J 66(3):988–998

    CAS  Google Scholar 

  • Sun WC, Xia Z, Sun XJ, Sun YL, Yi C (2018) Predicting nickel concentration in soil using reflectance spectroscopy associated with organic matter and clay minerals. Geoderma 327:25–35

    CAS  Google Scholar 

  • Todorova M, Atanassova S, Sitaula B, Apturachim D, Valkova P, Dermendgieva D (2018) Application of nirs as a rapid and alternative method for prediction of heavy metals content in soil. Transbound Emerg Dis 65(1):S32–S37

    Google Scholar 

  • Vasques GM, Grunwald S, Sickman JO (2008) Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 146(1):14–25

    CAS  Google Scholar 

  • Wang JJ, Cui LJ, Gao WX, Shi TZ, Chen YY, Gao Y (2014) Prediction of low heavy metal concentrations in agricultural soils using visible and near-infrared reflectance spectroscopy. Geoderma 216(4):1–9

    Google Scholar 

  • Wu YZ, Chen J, Ji JF, Gong P, Liao QL, Tian QJ, Ma HR (2007) A mechanism study of reflectance spectroscopy for investigating heavy metals in soils. Soil Sci Soc Am J 71(3):918–926

    CAS  Google Scholar 

  • Xu SX, Zhao YC, Wang MY, Shi XZ (2018) Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis-NIR spectroscopy. Geoderma 310:29–43

    CAS  Google Scholar 

  • Yaseen M, Malhi SS (2009) Differential growth performance of 15 wheat genotypes for grain yield and phosphorus uptake on a low phosphorus soil without and with applied phosphorus fertilizer. J Plant Nutr 32(6):1015–1043

    CAS  Google Scholar 

  • Zornoza R, Guerrero C, Mataix-Solera J, Scow KM, Arcenegui V, Mataix-Beneyto J (2008) Near infrared spectroscopy for determination of various physical, chemical and biochemical properties in Mediterranean soils. Soil Biol Biochem 40(7):1923–1930

    CAS  Google Scholar 

Download references

Acknowledgments

This study was supported by the National Key Research and Development Program of China (2017YFC0504705), the Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (2018JM4023), and the Fund Project of Shaanxi Key Laboratory of Land Consolidation (2018-TD02). We thank the Key Laboratory of Degraded and Unused Land Consolidation Engineering, the Ministry of Land and Resources, and the China-US Center for Ecological Land Engineering and Technology for their support. Furthermore, we thank the anonymous reviewers and editor for their helpful comments.

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Authors and Affiliations

  1. Institute of Water Resources and Hydro-electric Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Jinbao Liu, Jiancang Xie & Shaoxuan Li

  2. Key Laboratory of Degraded and Unused Land Consolidation Engineering, The Ministry of Land and Resources, Xi’an, 710075, Shaanxi, China

    Jinbao Liu, Jichang Han & Huanyuan Wang

  3. Shaanxi Provincial Land Engineering Construction Group Co., Ltd., Xi’an, 710075, Shaanxi, China

    Jichang Han, Huanyuan Wang, Jianhong Sun & Rui Li

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  1. Jinbao Liu
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  2. Jiancang Xie
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  4. Huanyuan Wang
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Contributions

We thank Huanyuan Wang, Rui Li, and Jianhong Sun for contributing the soil samples and spectroscopic measurements, and Jichang Han and Jiancang Xie for their help with the article writing.

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Correspondence to Jiancang Xie.

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Liu, J., Xie, J., Han, J. et al. Visible and near-infrared spectroscopy with chemometrics are able to predict soil physical and chemical properties. J Soils Sediments 20, 2749–2760 (2020). https://doi.org/10.1007/s11368-020-02623-1

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