Full-text resources of PSJD and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


Preferences help
enabled [disable] Abstract
Number of results
2017 | 50 | 1 |

Article title

Visible and Near-Infrared Spectroscopy as a Tool for Soil Classification and Soil Profile Description

Content

Title variants

Languages of publication

EN

Abstracts

EN
This paper presents preliminary results of the use of visible and near-infrared (VIS -NIR) spectroscopy for soil classification and soil profile examination. Three experiments involving (1) three different soil types (Albic Luvisol, Gleyic Phaeozem, Brunic Arenosol), (2) three artificial micro-plots with similar texture (loamy sand, Gleyic Phaeozem) but different soil organic carbon (SOC) content and (3) a soil profile (Fluvisol) have been investigated using VIS -NIR spectroscopy. Results indicated that VIS -NIR is a promising technique for preliminary soil description and can classify soils according to soil properties (especially SOC ) and horizons. Instead of complex chemical and physical analyses involved in routine soil profile classification, VIS-NIR spectroscopy is suggested as a useful, rapid, and inexpensive tool for soil profile investigation.

Year

Volume

50

Issue

1

Physical description

Dates

published
2017
online
30 - 08 - 2017

Contributors

References

  • [1] Aïchi, H., Fouad, Y., Walter, C., ViscarraRossel, R.A., Lili Chabaane, Z., Sanaa, M., 2009. Regional Predictions of Soil Organic Carbon Content from Spectral Reflectance Measurements. Biosystems Engineering, 104, 3: 442–446.
  • [2] Barthès, B.G., Brunet, D., Hien, E., Enjalric, F., Conche, S., Freschet, G.T., d’Annunzio, R., Toucet-Louri, J., 2008. Determining the Distributions of Soil Carbon and Nitrogen in Particle Size Fractions Using Near-Infrared Reflectance Spectrum of Bulk Soil Samples. Soil Biology and Biochemistry, 40, 6: 1533–1537.
  • [3] Bartmiński, P., Plak, A., Dębicki, R., 2012. Buffer Capacity of Soil as Indicator of Urban Forest Soil Resistance to Degradation. Polish Journal of Soil Science, 45, 2: 129–136.
  • [4] Ben-Dor, E., Heller, D., Chudnovsky, A., 2008. A Novel Method of Classifying Soil Profiles in the Field Using Optical Means. Soil Science Society of America Journal, 72, 4: 1113–1123.
  • [5] Cierniewski, J., Kaźmierowski, C., Krolewicz, S., 2015. Evaluation of the Effects of Surface Roughness on the Relationship Between Soil BRF Data and Broadband Albedo. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 4: 1528–1533.
  • [6] Croft, H., Anderson, K., Kuhn, N.J., 2009. Characterizing Soil Surface Roughness Using a Combined Structural and Spectral Approach. European Journal of Soil Science, 60, 3: 431–442.
  • [7] Debaene, G., Niedźwiecki, J., Pecio, A., 2013. On-the-Go Mapping of Soil Organic Carbon Content in Western Poland. Proceedings of the 3rd Global Workshop on Proximal Soil Sensing: 248–251.
  • [8] Debaene, G., Niedźwiecki, J., Pecio, A., 2010. Visible and Near-Infrared Spectrophotometer for Soil Analysis: Preliminary Results. Polish Journal of Agronomy, 3: 3–9.
  • [9] Debaene, G., Niedźwiecki, J., Pecio, A., Żurek, A., 2014a. Effect of the Number of Calibration Samples on the Prediction of Several Soil Properties at the Farm-Scale. Geoderma, 214–215: 114–125.
  • [10] Debaene, G., Pikula, D., Niedzwiecki, J., 2014b. Use of VIS-NIRS for Land Management Classification with a Support Vector Machine and Prediction of Soil Organic Carbon and Other Soil Properties. Ciencia e Investigación AGRARIA , 41, 1: 21–32.
  • [11] Demattê, J.A., Campos, R.C., Alves, M.C., Fiorio, P.R., Nanni, M.R., 2004. Visible–NIR Reflectance: A New Approach on Soil Evaluation. Geoderma, 121, 1–2: 95–112.
  • [12] Eshel, G., Levy, G.J., Singer, M.J., 2004. Spectral Reflectance Properties of Crusted Soils under Solar Illumination. Soil Science Society of America Journal, 68, 6: 1982–1991.
  • [13] Fabre, S., Briottet, X., Lesaignoux, A., 2015. Estimation of Soil Moisture Content from the Spectral Reflectance of Bare Soils in the 0.4–2.5 μm Domain. Sensors, 15, 2: 3262–3281.
  • [14] Kweon, G., Maxton, C., 2013. Soil Organic Matter Sensing with an On-the-Go Optical Sensor. Biosystems Engineering, 115, 1: 66–81.
  • [15] Madari, B.E., Reeves, J.B., Machado, P.L., Guimarães, C.M., Torres, E., McCarty, G.W., 2006. Mid- and Near-Infrared Spectroscopic Assessment of Soil Compositional Parameters and Structural Indices in Two Ferralsols. Geoderma, 136, 1–2: 245–259.
  • [16] Paz-Kagan, T., Shachak, M., Zaady, E., Karnieli, A., 2014. A Spectral Soil Quality Index (SSQI) for Characterizing Soil Function in Areas of Changed Land Use. Geoderma, 230–231: 171–184.
  • [17] Roberts, C.A., Workman, Jr. J., Reeves, III J.B., Workman, J., Shenk, J., 2004.Understanding and Using the Near-Infrared Spectrum as an Analytical Method, In: Roberts, C.A., Workman, J., Reeves, J.B. (eds.), Near-Infrared Spectroscopy in Agriculture. Agronomy Monograph. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America: 2–10.
  • [18] Stenberg, B., Viscarra Rossel, R.A., Mouazen, A.M., Wetterlind, J., 2010.Visible and Near Infrared Spectroscopy in Soil Science, In: Sparks, D.L. (ed.), Advances in Agronomy: 163–215, http://dx.doi.org/10.1016/S0065-2113(10)07005-7
  • [19] Vasques, G.M., Demattê, J., Viscarra Rossel, R. A., Ramírez-López, L., Terra, F.S., 2014. Soil Classification Using Visible/Near-Infrared Diffuse Reflectance Spectra from Multiple Depths. Geoderma, 223–225: 73–78.
  • [20] Waiser, T.H., Morgan C.L. S., Brown, D.J., Hallmark, C.T., 2007. In Situ Characterization of Soil Clay Content with Visible Near-Infrared Diffuse Reflectance Spectroscopy. Soil Science Society of America Journal, 71, 2: 389–396.
  • [21] Wenjun, J., Zhou, S., Jingyi, H., Shuo, L., Motta, A., 2014. In Situ Measurement of Some Soil Properties in Paddy Soil Using Visible and Near-Infrared Spectroscopy. PLoS ONE , 9, 8: e105708, http://dx.doi.org/10.1371/journal.pone.0105708
  • [22] World reference base for soil resources, 2014. International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports. FAO, Rome.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.ojs-doi-10_17951_pjss_2017_50_1_1
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.