in

Quantitative mapping and spectroscopic characterization of particulate organic matter fractions in soil profiles with imaging VisNIR spectroscopy

  • 1.

    Smith, P. et al. The changing faces of soil organic matter research. Eur. J. Soil Sci. 69, 23–30. https://doi.org/10.1111/ejss.12500 (2018).

    Article 

    Google Scholar 

  • 2.

    Kögel-Knabner, I. The macromolecular organic composition of plant and microbial residues as inputs to soil organic matter. Soil Biol. Biochem. 34, 139–162 (2002).

    Article 

    Google Scholar 

  • 3.

    Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56. https://doi.org/10.1038/nature10386 (2011).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 4.

    Lehmann, J. & Kleber, M. The contentious nature of soil organic matter. Nature 528, 60–68. https://doi.org/10.1038/nature16069 (2015).

    ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 5.

    Lehmann, J. et al. Persistence of soil organic carbon caused by functional complexity. Nat. Geosci. 13, 529–534. https://doi.org/10.1038/s41561-020-0612-3 (2020).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 6.

    Dong, L. et al. Effect of grazing exclusion and rotational grazing on labile soil organic carbon in north China. Eur. J. Soil Sci. https://doi.org/10.1111/ejss.12952 (2020).

    Article 

    Google Scholar 

  • 7.

    Leifeld, J. & Kogel-Knabner, I. Soil organic matter fractions as early indicators for carbon stock changes under different land-use?. Geoderma 124, 143–155 (2005).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 8.

    Poeplau, C. & Don, A. Sensitivity of soil organic carbon stocks and fractions to different land-use changes across Europe. Geoderma 192, 189–201. https://doi.org/10.1016/j.geoderma.2012.08.003 (2013).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 9.

    Besnard, E., Chenu, C., Balesdent, J., Puget, P. & Arrouays, D. Fate of particulate organic matter in soil aggregates during cultivation. Eur. J. Soil Sci. 47, 495–503 (1996).

    CAS 
    Article 

    Google Scholar 

  • 10.

    von Lützow, M. et al. Stabilization of organic matter in temperate soils: mechanisms and their relevance under different soil conditions—a review. Eur. J. Soil Sci. 57, 426–445. https://doi.org/10.1111/j.1365-2389.2006.00809.x (2006).

    CAS 
    Article 

    Google Scholar 

  • 11.

    Peng, X. H., Zhu, Q. H., Zhang, Z. B. & Hallett, P. D. Combined turnover of carbon and soil aggregates using rare earth oxides and isotopically labelled carbon as tracers. Soil Biol. Biochem. 109, 81–94. https://doi.org/10.1016/j.soilbio.2017.02.002 (2017).

    CAS 
    Article 

    Google Scholar 

  • 12.

    Dynarski, K. A., Bossio, D. A. & Scow, K. M. Dynamic stability of soil carbon: reassessing the “permanence” of soil carbon sequestration. Front. Environ. Sci. 8, 1. https://doi.org/10.3389/fenvs.2020.514701 (2020).

    Article 

    Google Scholar 

  • 13.

    Basile-Doelsch, I., Balesdent, J. & Pellerin, S. Reviews and syntheses: The mechanisms underlying carbon storage in soil. Biogeosci. Discuss. https://doi.org/10.5194/bg-2020-49 (2020).

  • 14.

    Poeplau, C. et al. Isolating organic carbon fractions with varying turnover rates in temperate agricultural soils—a comprehensive method comparison. Soil Biol. Biochem. 125, 10–26. https://doi.org/10.1016/j.soilbio.2018.06.025 (2018).

    CAS 
    Article 

    Google Scholar 

  • 15.

    Rumpel, C. & Kögel-Knabner, I. Deep soil organic matter-a key but poorly understood component of terrestrial C cycle. Plant Soil 338, 143–158. https://doi.org/10.1007/s11104-010-0391-5 (2011).

    CAS 
    Article 

    Google Scholar 

  • 16.

    Steffens, M., Kölbl, A., Schörk, E., Gschrey, B. & Kögel-Knabner, I. Distribution of soil organic matter between fractions and aggregate size classes in grazed semiarid steppe soil profiles. Plant Soil 338, 63–81. https://doi.org/10.1007/s11104-010-0594-9 (2011).

    CAS 
    Article 

    Google Scholar 

  • 17.

    Soriano-Disla, J. M., Janik, L. J., Rossel, R. A. V., Macdonald, L. M. & McLaughlin, M. J. The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Appl. Spectrosc. Rev. 49, 139–186. https://doi.org/10.1080/05704928.2013.811081 (2014).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 18.

    Stenberg, B., Rossel, R. A. V., Mouazen, A. M. & Wetterlind, J. Visible and near infrared spectroscopy in soil science. Adv. Agron. 107(107), 163–215. https://doi.org/10.1016/s0065-2113(10)07005-7 (2010).

    CAS 
    Article 

    Google Scholar 

  • 19.

    Mouazen, A. M., Steffens, M. & Borisover, M. Reflectance and fluorescence spectroscopy in soil science-Current and future research and developments. Soil Tillage Res. 155, 448–449 (2016).

    Article 

    Google Scholar 

  • 20.

    Viscarra Rossel, R. A. & Bouma, J. Soil sensing: A new paradigm for agriculture. Agric. Syst. 148, 71–74. https://doi.org/10.1016/j.agsy.2016.07.001 (2016).

    Article 

    Google Scholar 

  • 21.

    Nocita, M. et al.. Soil spectroscopy: An alternative to wet chemistry for soil monitoring. Adv. Agron. 132, 139–159 (2015)

    Article 

    Google Scholar 

  • 22.

    Gholizadeh, A., Boruvka, L., Saberioon, M. & Vasat, R. Visible, near-infrared, and mid-infrared spectroscopy applications for soil assessment with emphasis on soil organic matter content and quality: state-of-the-art and key issues. Appl. Spectrosc. 67, 1349–1362. https://doi.org/10.1366/13-07288 (2013).

    ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 23.

    Hermansen, C. et al. Complete soil texture is accurately predicted by visible near-infrared spectroscopy. Soil Sci. Soc. Am. J. 81, 758–769. https://doi.org/10.2136/sssaj2017.02.0066 (2017).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 24.

    Zimmermann, M., Leifeld, J. & Fuhrer, J. Quantifying soil organic carbon fractions by infrared-spectroscopy. Soil Biol. Biochem. 39, 224–231. https://doi.org/10.1016/j.soilbio.2006.07.010 (2007).

    CAS 
    Article 

    Google Scholar 

  • 25.

    Madhavan, D. B. et al. Rapid prediction of particulate, humus and resistant fractions of soil organic carbon in reforested lands using infrared spectroscopy. J. Environ. Manage. 193, 290–299. https://doi.org/10.1016/jjenvman.2017.02.013 (2017).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 26.

    St. Luce, M. et al. Rapid determination of soil organic matter quality indicators using visible near infrared reflectance spectroscopy. Geoderma 232–234, 449–458. https://doi.org/10.1016/j.geoderma.2014.05.023 (2014).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 27.

    Terhoeven-Urselmans, T., Michel, K., Helfrich, M., Flessa, H. & Ludwig, B. Near-infrared spectroscopy can predict the composition of organic matter in soil and litter. J. Plant Nutr. Soil Sci. 169, 168–174. https://doi.org/10.1002/jpln.200521712 (2006).

    CAS 
    Article 

    Google Scholar 

  • 28.

    Margenot, A., O’Neill, T., Sommer, R. & Akella, V. Predicting soil permanganate oxidizable carbon (PDXC) by coupling DRIFT spectroscopy and artificial neural networks (ANN). Comput. Electron. Agric. https://doi.org/10.1016/j.compag.2019.105098 (2020).

    Article 

    Google Scholar 

  • 29.

    Fang, Q. et al. Visible and near-infrared reflectance spectroscopy for investigating soil mineralogy: a review. J. Spectrosc. https://doi.org/10.1155/2018/3168974 (2018).

    Article 

    Google Scholar 

  • 30.

    Shi, P., Castaldi, F., van Wesemael, B. & van Oost, K. Vis-NIR spectroscopic assessment of soil aggregate stability and aggregate size distribution in the Belgian Loam Belt. Geoderma https://doi.org/10.1016/j.geoderma.2019.113958 (2020).

    Article 

    Google Scholar 

  • 31.

    Canasveras, J. C., Barron, V., del Campillo, M. C., Torrent, J. & Gomez, J. A. Estimation of aggregate stability indices in Mediterranean soils by diffuse reflectance spectroscopy. Geoderma 158, 78–84. https://doi.org/10.1016/j.geoderma.2009.09.004 (2010).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 32.

    Hermansen, C. et al. Visible-near-infrared spectroscopy can predict the clay/organic carbon and mineral fines/organic carbon ratios. Soil Sci. Soc. Am. J. 80, 1486–1495. https://doi.org/10.2136/sssaj2016.05.0159 (2016).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 33.

    Jaconi, A., Don, A. & Freibauer, A. Prediction of soil organic carbon at the country scale: stratification strategies for near-infrared data. Eur. J. Soil Sci. 68, 919–929. https://doi.org/10.1111/ejss.12485 (2017).

    CAS 
    Article 

    Google Scholar 

  • 34.

    Jaconi, A., Vos, C. & Don, A. Near infrared spectroscopy as an easy and precise method to estimate soil texture. Geoderma 337, 906–913. https://doi.org/10.1016/j.geoderma.2018.10.038 (2019).

    ADS 
    Article 

    Google Scholar 

  • 35.

    Riedel, F., Denk, M., Muller, I., Barth, N. & Glasser, C. Prediction of soil parameters using the spectral range between 350 and 15,000 nm: A case study based on the Permanent Soil Monitoring Program in Saxony Germany. Geoderma 315, 188–198. https://doi.org/10.1016/j.geoderma.2017.11.027 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 36.

    Clairotte, M. et al. National calibration of soil organic carbon concentration using diffuse infrared reflectance spectroscopy. Geoderma 276, 41–52. https://doi.org/10.1016/j.geoderma.2016.04.021 (2016).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 37.

    Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A. & Fernandez-Ugalde, O. LUCAS soil, the largest expandable soil dataset for Europe: a review. Eur. J. Soil Sci. 69, 140–153. https://doi.org/10.1111/ejss.12499 (2018).

    Article 

    Google Scholar 

  • 38.

    Stevens, A., Nocita, M., Toth, G., Montanarella, L. & van Wesemael, B. Prediction of soil organic carbon at the european scale by visible and near infrared reflectance spectroscopy. PLoS ONE 8, 1. https://doi.org/10.1371/journal.pone.0066409 (2013).

    CAS 
    Article 

    Google Scholar 

  • 39.

    Nocita, M. et al. Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach. Soil Biol. Biochem. 68, 337–347. https://doi.org/10.1016/j.soilbio.2013.10.022 (2014).

    CAS 
    Article 

    Google Scholar 

  • 40.

    Viscarra Rossel, R. A. & Hicks, W. S. Soil organic carbon and its fractions estimated by visible-near infrared transfer functions. Europ. J. Soil Sci. 66, 438–450. https://doi.org/10.1111/ejss.12237 (2015).

    CAS 
    Article 

    Google Scholar 

  • 41.

    Steffens, M. & Buddenbaum, H. Laboratory imaging spectroscopy of a stagnic Luvisol profile – High resolution soil characterisation, classification and mapping of elemental concentrations. Geoderma 195–196, 122–132 (2013).

    ADS 
    Article 

    Google Scholar 

  • 42.

    Steffens, M., Kohlpaintner, M. & Buddenbaum, H. Fine spatial resolution mapping of soil organic matter quality in a Histosol profile. Eur. J. Soil Sci. 65, 827–839. https://doi.org/10.1111/ejss.12182 (2014).

    CAS 
    Article 

    Google Scholar 

  • 43.

    Hobley, E., Steffens, M., Bauke, S. L. & Kogel-Knabner, I. Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging. Sci. Rep. 8, 1. https://doi.org/10.1038/s41598-018-31776-w (2018).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 44.

    Lucas, M., Pihlap, E., Steffens, M., Vetterlein, D. & Kogel-Knabner, I. Combination of imaging infrared spectroscopy and x-ray computed microtomography for the investigation of bio- and physicochemical processes in structured soils. Front. Environ. Sci. 8, 1. https://doi.org/10.3389/fenvs.2020.00042 (2020).

    Article 

    Google Scholar 

  • 45.

    Mueller, C. W., Steffens, M. & Buddenbaum, H. Permafrost soil complexity evaluated by laboratory imaging Vis-NIR spectroscopy. Eur. J. Soil Sci. https://doi.org/10.1111/ejss.12927 (2019).

    Article 

    Google Scholar 

  • 46.

    Schreiner, S., Buddenbaum, H., Emmerling, C. & Steffens, M. VNIR/SWIR laboratory imaging spectroscopy for wall-to-wall mapping of elemental concentrations in soil cores. Photogrammetrie Fernerkundung Geoinformation https://doi.org/10.1127/pfg/2015/0279 (2015).

    Article 

    Google Scholar 

  • 47.

    Askari, M. S., O’Rourke, S. M. & Holden, N. M. A comparison of point and imaging visible-near infrared spectroscopy for determining soil organic carbon. J. Near Infrared Spectrosc. 26, 133–146. https://doi.org/10.1177/0967033518766668 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 48.

    O’Rourke, S. M. & Holden, N. M. Determination of soil organic matter and carbon fractions in forest top soils using spectral data acquired from visible-near infrared hyperspectral images. Soil Sci. Soc. Am. J. 76, 586–596. https://doi.org/10.2136/sssaj2011.0053 (2012).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 49.

    Buddenbaum, H. & Steffens, M. Laboratory imaging spectroscopy of soil profiles. J. Spectral Imag. 2, 1. https://doi.org/10.1255/jsi.2011.a2 (2011).

    Article 

    Google Scholar 

  • 50.

    Buddenbaum, H. & Steffens, M. Mapping the distribution of chemical properties in soil profiles using laboratory imaging spectroscopy SVM and PLS regression. EARSeL eProc. 11, 25–32 (2012).

    Google Scholar 

  • 51.

    Poeplau, C. et al. Stocks of organic carbon in German agricultural soils-Key results of the first comprehensive inventory. J. Plant Nutr. Soil Sci. 183, 665–681. https://doi.org/10.1002/jpln.202000113 (2020).

    CAS 
    Article 

    Google Scholar 

  • 52.

    Viscarra Rossel, R. A., Lobsey, C. R., Sharman, C., Flick, P. & McLachlan, G. Novel proximal sensing for monitoring soil organic C stocks and condition. Environ. Sci. Technol. 51, 5630–5641. https://doi.org/10.1021/acs.est.7b00889 (2017).

    ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 53.

    IUSS Working Group WRB. World reference base for soil resources 2006. Vol. 103 (FAO, 2006).

    Google Scholar 

  • 54.

    Steffens, M., Kölbl, A., Totsche, K. U. & Kögel-Knabner, I. Grazing effects on soil chemical and physical properties in a semiarid steppe of Inner Mongolia (P.R. China). Geoderma 143, 63–72 (2008).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 55.

    Hoffmann, C. et al. Effects of grazing and climate variability on grassland ecosystem functions in Inner Mongolia: Synthesis of a 6-year grazing experiment. J. Arid Environ. 135, 50–63. https://doi.org/10.1016/j.jaridenv.2016.08.003 (2016).

    ADS 
    Article 

    Google Scholar 

  • 56.

    FAO. Guidelines for soil description. 4th edition edn, (FAO, 2006).

    Google Scholar 

  • 57.

    Lenhard, K., Baumgartner, A. & Schwarzmaier, T. Independent laboratory characterization of NEO HySpex imaging spectrometers VNIR-1600 and SWIR-320m-e. IEEE Trans. Geosci. Remote Sens. 53, 1828–1841. https://doi.org/10.1109/TGRS.2014.2349737 (2015).

    ADS 
    Article 

    Google Scholar 

  • 58.

    Peddle, D. R., White, H. P., Soffer, R. J., Miller, J. R. & LeDrew, E. F. Reflectance processing of remote sensing spectroradiometer data. Comput. Geosci. 27, 203–213 (2001).

    ADS 
    Article 

    Google Scholar 

  • 59.

    Rogass, C. et al. Translational imaging spectroscopy for proximal sensing. Sensors 17, 1857 (2017).

    Article 

    Google Scholar 

  • 60.

    Steffens, M., Kölbl, A. & Kögel-Knabner, I. Alteration of soil organic matter pools and aggregation in semi-arid steppe topsoils as driven by organic matter input. Eur. J. Soil Sci. 60, 198–212. https://doi.org/10.1111/j.1365-2389.2008.01104.x (2009).

    CAS 
    Article 

    Google Scholar 

  • 61.

    Golchin, A., Oades, J. M., Skjemstad, J. O. & Clarke, P. Soil-structure and carbon cycling. Aust. J. Soil Res. 32, 1043–1068 (1994).

    Article 

    Google Scholar 

  • 62.

    Christensen, B. T. Physical fractionation of soil and structural and functional complexity in organic matter turnover. Eur. J. Soil Sci. 52, 345–353 (2001).

    CAS 
    Article 

    Google Scholar 

  • 63.

    Schmidt, M. W. I., Rumpel, C. & Kögel-Knabner, I. Evaluation of an ultrasonic dispersion procedure to isolate primary organomineral complexes from soils. Eur. J. Soil Sci. 50, 87–94 (1999).

    Article 

    Google Scholar 

  • 64.

    Steffens, M. et al. Spatial variability of topsoils and vegetation in a grazed steppe ecosystem in Inner Mongolia (PR China). J. Plant Nutr. Soil Sci. 172, 78–90. https://doi.org/10.1002/jpln.200700309 (2009).

    CAS 
    Article 

    Google Scholar 

  • 65.

    Six, J., Gregorich, E. & Koegel-Knabner, I. Landmark Papers: No. 1. Tisdall, J. M. & Oades, J. M. 1982. Organic matter and water-stable aggregates in soils. Journal of Soil Science, 33, 141–163 Commentary on the impact of the impact of Tisdall & Oades (1982): by J. Six, E. G. Gregorich & I. Kogel-Knabner. European Journal of Soil Science 63, 3–7 (2012).

  • 66.

    Six, J., Bossuyt, H., Degryze, S. & Denef, K. A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res. 79, 7–31 (2004).

    Article 

    Google Scholar 

  • 67.

    Wiesmeier, M. et al. Aggregate stability and physical protection of soil organic carbon in semi-arid steppe soils. Eur. J. Soil Sci. 63, 22–31. https://doi.org/10.1111/j.1365-2389.2011.01418.x (2012).

    CAS 
    Article 

    Google Scholar 

  • 68.

    McSherry, M. E. & Ritchie, M. E. Effects of grazing on grassland soil carbon: a global review. Glob. Change Biol. 19, 1347–1357. https://doi.org/10.1111/gcb.12144 (2013).

    ADS 
    Article 

    Google Scholar 

  • 69.

    Viscarra Rossel, R. A. & Behrens, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158, 46–54. https://doi.org/10.1016/j.geoderma.2009.12.025 (2010).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 70.

    Ben-Dor, E., Inbar, Y. & Chen, Y. The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400–2500 nm) during a controlled decomposition process. Remote Sens. Environ. 61, 1–15 (1997).

    ADS 
    Article 

    Google Scholar 

  • 71.

    Delegido, J., Verrelst, J., Rivera, J. P., Ruiz-Verdu, A. & Moreno, J. Brown and green LAI mapping through spectral indices. Int. J. Appl. Earth Obs. Geoinf. 35, 350–358. https://doi.org/10.1016/j.jag.2014.10.001 (2015).

    ADS 
    Article 

    Google Scholar 

  • 72.

    Viscarra Rossel, R. A., McGlynn, R. N. & McBratney, A. B. Determing the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy. Geoderma 137, 70–82. https://doi.org/10.1016/j.geoderma.2006.07.004 (2006).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 73.

    Ben-Dor, E. et al. Imaging spectrometry for soil applications. Adv. Agronomy 97, 321. https://doi.org/10.1016/s0065-2113(07)00008-9 (2008).

    CAS 
    Article 

    Google Scholar 


  • Source: Ecology - nature.com

    Smarter regulation of global shipping emissions could improve air quality and health outcomes

    Resource–diversity relationships in bacterial communities reflect the network structure of microbial metabolism