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Rapid Soil Analyses Using Modern Sensing Technology: Toward a More Sustainable Agriculture

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Sustainability of Agricultural Environment in Egypt: Part II

Part of the book series: The Handbook of Environmental Chemistry ((HEC,volume 77))

Abstract

Modern sensing technology must be utilized to provide farmers with rapid soil analysis in making farming more sustainable. Modern technologies in agriculture have been given an important role for the improvement of agricultural productions, e.g., sustainable agriculture, in order to maintain food security. It has been known that modern agricultural technology can sustainably improve agricultural production. Up-to-date information on soil properties is imperative for sustainable agriculture. Conventional soil analyses cannot efficiently give this information since they are slow and expensive and sometimes incorporate environmentally damaging chemicals. Soil spectroscopy is a well-known technique to assess soil properties quickly and quantitatively.

To assess the utility of spectroscopy for soil characteristic (clay content, salinity, and OM) prediction, 35 soil samples collected from Bahr El Baqar, Egypt were scanned in the 350–2,500 nm region (FieldSpec Spectroradiometer). Reflectance spectroscopy gives an alternate method to nondestructively characterize key soil properties. Chemometrics techniques have been utilizing to estimate soil properties from visible and near-infrared (VNIR, 350–1,200 nm) and shortwave-infrared (SWIR, 1,200–2,500 nm) reflectance domains. Partial least squares regression (PLSR) was put in place to develop calibration models, which were independently tested for the predictions of soil organic carbon, salinity, and clay content from the soil spectra. Some spectral data pre-processing techniques were carried out to diminish noise, to offset effects, and to improve the linearity between measured absorbance and soil properties. These models were developed by the correlation between spectral characteristics and physicochemical soil properties separately for each soil property, using PLSR analysis. The continuum removal (CR) spectra yielded the best calibration models with respect to estimates of the soil salinity, which generated R 2 values of 0.62. In the case of the clay content, the prediction capacity of the method proved to be high (R 2 = 0.57) using CR. These results can be explained by the strong spectral activity of organic carbon and clay in the VNIR-SWIR region. The model accuracy (RMSE OM = 0.425) is low, indicating the need for improving the measurement protocol to achieve more reliable data and to test other pre-processing and modeling methods as well. The deviation of the arch (DOA) at 600 nm is indicative of the convex and concave features of the spectral curves generated by OM. The DOA contains the majority of information regarding OM and can be utilized to estimate OM.

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References

  1. Du C, Linker R, Shaviv A (2008) Identification of agricultural Mediterranean soils using mid-infrared photoacoustic spectroscopy. Geoderma 143:85–90

    Article  CAS  Google Scholar 

  2. Giasson E, Inda Junior AV, Nascimento PC (2006) Estimativa do benefício econômico potencial de dois levantamentos de solos no Estado do Rio Grande do Sul. Cienc Rural 36:478–486

    Article  Google Scholar 

  3. Brown DJ, Shepherd KD, Walsh MG, Dewayne Mays M, Reinsch TG (2006) Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma 132(3–4):273–290

    Article  CAS  Google Scholar 

  4. Rizzo R, Demattê JAM, da Silva TF (2014) Using numerical classification of profiles based on Vis-Nir spectra to distinguish soils from the Piracicaba region, Brazil. Rev Bras Ciênc Solo 38:372–385

    Article  CAS  Google Scholar 

  5. Omran ESE (2008) Is soil science dead and buried? Future image in the world of 10 billion people. CATRINA 3(2):59–68

    Google Scholar 

  6. Omran ESE (2016) Inference model to predict heavy metals of Bahr El Baqar soils, Egypt using spectroscopy and chemometrics technique. Model Earth Syst Environ 2(4):200. doi:10.1007/s40808-016-0259-7

    Article  Google Scholar 

  7. Summers D (2009) Discriminating and mapping soil variability with hyperspectral reflectance data. PhD thesis, Faculty of science, School of earth and environmental science, Adelaide University

    Google Scholar 

  8. Wu Y, Chen J, Wu X, Tian Q (2005) Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils. Appl Geochem 20:1051–1059

    Article  CAS  Google Scholar 

  9. Ben-Dor E, Chabrillat S, Demattê J, Taylor G, Hill J, Whiting M, Sommer S (2009) Using imaging spectroscopy to study soil properties. Remote Sens Environ 113:S38–S55

    Article  Google Scholar 

  10. Minasny B, Tranter G, McBratney A, Brough D, Murphy B (2009) Regional transferability of mid-infrared diffuse reflectance spectroscopic prediction for soil chemical properties. Geoderma 153:155–162

    Article  CAS  Google Scholar 

  11. Stenberg B, Viscarra Rossel R, Mouazen AM, Wetterlind J (2010) Visible and near infrared spectroscopy in soil science. Adv Agron 107:163–215

    Article  CAS  Google Scholar 

  12. Viscarra Rossel RA, Behrens T (2010) Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158(1–2):46–54

    Article  Google Scholar 

  13. Farifteh J, van der Meer F, van der Meijde M, Atzberger C (2008) Spectral characteristics of salt-affected soils: a laboratory experiment. Geoderma 145:196–206

    Article  CAS  Google Scholar 

  14. Weng YL, Gong P, Zhu ZL (2008) Soil salt content estimation in the Yellow River delta with satellite hyperspectral data. Can J Remote Sens 34:259–270

    Google Scholar 

  15. Pan T, Wu ZT, Chen HZ (2012) Waveband optimization for near-infrared spectroscopic analysis of total nitrogen in soil. Chin J Anal Chem 40:920–924. doi:10.3724/SP.J.1096.2012.10987

    Article  CAS  Google Scholar 

  16. Kuang BY, 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 114:249–258. doi:10.1016/j.biosystemseng.2013.01.005

    Article  Google Scholar 

  17. Saiano F, Oddo G, Scalenghe R, la Mantia T, Ajmone-Marsan F (2013) DRIFTS sensor: soil carbon validation at large scale. Sensors 13:5603–5613. doi:10.3390/s130505603

    Article  CAS  Google Scholar 

  18. Rinnan A, van den Berg F, Engelsen SB (2009) Review of the most common pre-processing techniques for near-infrared spectra. Trends Anal Chem 28:1201–1222

    Article  CAS  Google Scholar 

  19. Volkan BA, van Es M, Akbas F, Durak A, Hively W (2010) Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey. J Arid Environ 74:229–238

    Article  Google Scholar 

  20. Mashimbye Z, Cho M, Nell J, De Clercq W, Van Niekerk A, Turner D (2012) Model-based integrated methods for quantitative estimation of soil salinity from hyperspectral remote sensing data: a case study of selected South African soils. Pedosphere 22:640–649

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  22. Todorova M, Atanassova S, Lange H, Pavlov D (2011) Estimation of total N, total P, pH and electrical conductivity in soil using near-infrared reflectance spectroscopy. Agric Sci Technol 3:50–53

    Google Scholar 

  23. Genot V, Colinet G, Bock L, Vanyve D, Reusen Y, Dardenne P (2011) Near infrared reflectance spectroscopy for estimating soil characteristics valuable in the diagnosis of soil fertility. J Near Infrared Spectrosc 19:117–138

    Article  CAS  Google Scholar 

  24. Viscarra Rossel RA, 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(1–2):59–75

    Article  CAS  Google Scholar 

  25. Ingleby HR, Crowe TG (2000) Reflectance models for predicting organic carbon in Saskatchewan soils. Can Agric Eng 42(2):57–63

    Google Scholar 

  26. Hummel JW, Sudduth KA, Hollinger SE (2001) Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor. Comput Electron Agric 32(2):149–165

    Article  Google Scholar 

  27. McCarty GW, Reeves JB, Reeves VB, Follett RF, Kimble JM (2002) Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Sci Soc Am J 66:640–646

    Article  CAS  Google Scholar 

  28. Kooistra L, Wanders J, Epema GF, Leuven RSEW, Wehrens R, Buydens LMC (2003) The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Anal Chim Acta 484:189–200

    Article  CAS  Google Scholar 

  29. Chang CW, Laird DA, Hurburgh CR (2005) Influence of soil moisture on near-infrared reflectance spectroscopic measurement of soil properties. Soil Sci 170(4):244–255

    Article  CAS  Google Scholar 

  30. Cozzolino D, Moron A (2006) Potential of near-infrared reflectance spectroscopy and chemometrics to predict soil organic carbon fractions. Soil Tillage Res 85(1–2):78–85

    Article  Google Scholar 

  31. He Y, Huang M, Garcia A, Hernandez A, Song H (2007) Prediction of soil macronutrients content using near-infrared spectroscopy. Comput Electron Agric 58(2):144–153

    Article  Google Scholar 

  32. Bartholomeus HM, Schaepman ME, Kooistra L, Stevens A, Hoogmoed WB, Spaargaren OSP (2008) Spectral reflectance based indices for soil organic carbon quantification. Geoderma 145:28–36

    Article  CAS  Google Scholar 

  33. Curcio D, Ciraolo G, D’Asaro F, Minacapilli M (2013) Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy. Procedia Environ Sci 19:494–503

    Article  Google Scholar 

  34. Conforti M, Raffaele F, Giorgio M, Tommaso C, Gabriele B (2013) Potentiality of laboratory visible and near infrared spectroscopy for determining clay content in forest soils: a case study from high forest beech (Fagus Sylvatica) in Calabria (Southern Italy). Environ Qual 11:49–64

    Google Scholar 

  35. Du C, Linker R, Shaviv A (2007) Characterization of soils using photoacoustic mid-infrared spectroscopy. Appl Spectrosc 61:1063–1067

    Article  CAS  Google Scholar 

  36. Du C, Zhou J, Wang H, Chen X, Zhu A, Zhang J (2008) Determination of soil properties using Fourier transform mid-infrared photoacoustic spectroscopy. Vib Spectrosc 49(1):32–37. doi:10.1016/j.vibspec.2008.04.009

    Article  CAS  Google Scholar 

  37. Chen H, Zhao G, Li S, Wang R, Liu Y (2016) Prediction of soil salinity using near-infrared reflectance spectroscopy with nonnegative matrix factorization. Appl Spectrosc 70(9):1589–1597

    Article  CAS  Google Scholar 

  38. Nawar S, Buddenbaum H, Hill J (2014) Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy: a case study from Egypt. Arab J Geosci 8(7):5127–5140. doi:10.1007/s12517-014-1580-y

    Article  CAS  Google Scholar 

  39. Abdalsatar AAA, Weindorf DC, Chakraborty S, Sharma A, Bin L (2015) Combination of proximal and remote sensing methods for rapid soil salinity quantification. Geoderma 239–240:34–46

    Google Scholar 

  40. Brunet D, Barthès BG, Chotte JL, Feller C (2007) Determination of carbon and nitrogen contents in Alfisols, Oxisols and Ultisols from Africa and Brazil using NIRS analysis: effects of sample grinding and set heterogeneity. Geoderma 139:106–117

    Article  CAS  Google Scholar 

  41. Malley DF, Martin PD, Ben-Dor E (2004) Application in analysis of soils. In: Roberts CA (ed) Near-infrared spectroscopy. In agriculture. Agronomy monograph 44. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, pp 729–784

    Google Scholar 

  42. Malley DF, Yesmin L, Eilers RG (2002) Rapid analysis of hog manure and manure-amended soils using near-infrared spectroscopy. Soil Sci Soc Am J 66:1677–1686

    Article  CAS  Google Scholar 

  43. Bogrekci I, Lee WS (2005) Improving phosphorus sensing by eliminating soil particle size effect in spectral measurement. Trans ASAE 48:1971–1978

    Article  CAS  Google Scholar 

  44. Patzold S, Mertens FM, Bornemann L, Koleczek B, Franke J, Feilhauer H, Welp G (2008) Soil heterogeneity at the field scale: a challenge for precision crop protection. Precis Agric 9:367–390

    Article  Google Scholar 

  45. Stevens A, Udelhoven T, et al. (2010) Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy. Geoderma 158(1–2):32–45

    Article  CAS  Google Scholar 

  46. Schwartz G, Eshel G, Ben-Dor E (2012) Reflectance spectroscopy as a rapid tool for monitoring contaminated soil. PhD thesis, Porter School of Environmental Studies, Tel Aviv University

    Google Scholar 

  47. Rossel VRA, McGlynn RN, McBratney AB (2006) Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy. Geoderma 137:70–82

    Article  Google Scholar 

  48. Yaolin L, Wei L, Guofeng W, Xinguo X (2011) Feasibility of estimating heavy metal contaminations in floodplain soils using laboratory-based hyperspectral data-a case study along Le’an River, China. Geospat Inf Sci 14(1):10–16

    Article  Google Scholar 

  49. Divya Y, Sanjeevi S, Ilamparuthi K (2013) Studies on textural and compositional characteristics of sand and clay mixtures using hyperspectral radiometry. J Indian Soc Remote Sens 42:589. doi:10.1007/s12524-013-0336-6

    Article  Google Scholar 

  50. Morón A, Cozzolino D (2003) Exploring the use of near infrared reflectance spectroscopy to study physical properties and microelements in soils. J Near Infrared Spectrosc 11:145–154

    Article  Google Scholar 

  51. Chang CW, Laird DA, Mausbach MJ, Hurburgh CRJ (2001) Near-infrared reflectance spectroscopy – principal components regression analyses of soil properties. Soil Sci Soc Am J 65:480–490

    Article  CAS  Google Scholar 

  52. 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 40:1923–1930

    Article  CAS  Google Scholar 

  53. Goldshleger N, Ben-Dor E, Benyamini Y, Blumberg D, Agassi M (2002) Spectral properties and hydraulic conductance of soil crusts formed by raindrop impact. Int J Remote Sens 23:3909–3920

    Article  Google Scholar 

  54. Guerrero C, Mataix-Solera J, Arcenegui V, Mataix-Beneyto J, Gómez I (2007) Near-infrared spectroscopy to estimate the maximum temperatures reached on burned soils. Soil Sci Soc Am J 71:1029–1037

    Article  CAS  Google Scholar 

  55. Viscarra Rossel RA, Cattle SR, Ortega A, Fouad Y (2009) In situ measurements of soil colour, mineral composition and clay content by vis–NIR spectroscopy. Geoderma 150:253–266

    Article  CAS  Google Scholar 

  56. Wetterlind J, Bo S, Jonsson A (2008) Near infrared reflectance spectroscopy compared with soil clay and organic matter content for estimating within-field variation in N uptake in cereals. Plant Soil 302(1–2):317–327

    Article  CAS  Google Scholar 

  57. Omran ESE, Abd El Razek AA (2012) Mapping and screening risk assessment of heavy metals concentrations in soils of the Bahr El-Baker Region, Egypt. J Soil Sci Environ Manag 6(7):182–195

    Google Scholar 

  58. Gee GW, Bauder JW (1986) Particle size analysis. In: Klute A (ed) Methods of soil analysis: Part 1 – Physical and mineralogical methods. Agronomy monographs 92nd edn. ASA and SSSA, Madison, pp 383–411

    Google Scholar 

  59. Walkley A, Black AI (1934) An examination of the Degtjareff method for determining soil organic matter, and a proposed

    Google Scholar 

  60. Wetterlind J, Stenberg B, Rossel V, Raphael A (2013) Soil analysis using visible and near infrared spectroscopy. In: Maathuis FJM (ed) Plant mineral nutrients: methods and protocols. Methods in molecular biology 953. Humana Press and Springer, New York, pp 95–107

    Chapter  Google Scholar 

  61. Vohland M, Bossung C, Frund H (2009) A spectroscopic approach to assess trace–heavy metal contents in contaminated floodplain soils via spectrally active soil components. J Plant Nutr Soil Sci 172(2):201–209

    Article  CAS  Google Scholar 

  62. Nicolaï BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46(2):99–118

    Article  Google Scholar 

  63. Kokaly RF, Clark RN (1999) Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sens Environ 67(3):267–287

    Article  Google Scholar 

  64. Naes T, Isaksson T, Fearn T, Davies T (2002) Outlier detection. A user-friendly guide to multivariate calibration and classification. NIR Publications, Chichester, pp 177–189

    Google Scholar 

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

    Article  CAS  Google Scholar 

  66. Ertlen D, Schwartz D, Trautmann M, Webster R, Brunet D (2010) Discriminating between organic matter in soil from grass and forest by near-infrared spectroscopy. Eur J Soil Sci 61:207–216

    Article  CAS  Google Scholar 

  67. Esbensen KH (2002) Multivariate data analyses, an introduction to multivariate data analyses and experimental design5th edn. Aalborg University Esbjerg, Esbjerg

    Google Scholar 

  68. Li L (2006) Partial least squares modeling to quantify lunar soil composition with hyperspectral reflectance measurements. J Geophys Res 111:E04002. doi:10.1029/2005JE002598

    Article  CAS  Google Scholar 

  69. Zheng G, Dongryeol R, Jjao C, Hong C (2016) Estimation of organic matter content in coastal soil using reflectance spectroscopy. Pedosphere 26(1):130–136

    Article  Google Scholar 

  70. Peng Y, Knadel M, Gislum R, Deng F, Norgaard T, Wollesen de Jonge L, Moldrup P, Humlekrog Greve M (2013) Predicting soil organic carbon at field scale using a national soil spectral library. J Near Infrared Spectrosc 21:213–222

    Article  CAS  Google Scholar 

  71. Omran E-SE (2017) Will the conventional soil-plant analysis pass into oblivion? Rapid and low-cost determination using spectroscopy. Commun Soil Sci Plant Anal 48(7):705–715

    CAS  Google Scholar 

  72. Omran E-SE (2017) Early sensing of peanut leaf spot using spectroscopy and thermal imaging. Arch Agron Soil Sci 63(7):883–896

    Article  CAS  Google Scholar 

  73. Omran E-SE (2017) Will the traditional agriculture pass into oblivion? Adaptive remote sensing approach in support of precision agriculture. In: HBS AR, Ghosh S (eds) Adaptive soil management: from theory to practices. Springer, Singapore, pp 39–67. 571 pp

    Chapter  Google Scholar 

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  1. Soil and Water Department, Faculty of Agriculture, Suez Canal University, 41522, Ismailia, Egypt

    El-Sayed Ewis Omran

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  1. Faculty of Engineering, Zagazig University, Zagazig, Egypt

    Abdelazim M. Negm

  2. Faculty of Agriculture, Zagazig University, Zagazig, Egypt

    Mohamed Abu-hashim

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Omran, ES.E. (2017). Rapid Soil Analyses Using Modern Sensing Technology: Toward a More Sustainable Agriculture. In: Negm, A., Abu-hashim, M. (eds) Sustainability of Agricultural Environment in Egypt: Part II. The Handbook of Environmental Chemistry, vol 77. Springer, Cham. https://doi.org/10.1007/698_2017_76

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