Articles | Volume 7, issue 1
https://doi.org/10.5194/soil-7-193-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-7-193-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
14 Jun 2021
Original research article |
| 14 Jun 2021
| 14 Jun 2021
Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library
Anatol Helfenstein
CORRESPONDING AUTHOR
Department of Environmental Systems Science, Swiss Federal Institute of Technology, ETH Zurich, Universitätsstrasse 2, 8092 Zurich, Switzerland
Soil Geography and Landscape Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Philipp Baumann
Department of Environmental Systems Science, Swiss Federal Institute of Technology, ETH Zurich, Universitätsstrasse 2, 8092 Zurich, Switzerland
Raphael Viscarra Rossel
School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, Perth, Western Australia, Australia
Andreas Gubler
Swiss Soil Monitoring Network (NABO), Agroscope, Reckenholzstrasse 191, 8046 Zurich, Switzerland
Stefan Oechslin
School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences BFH, Bern, Switzerland
Johan Six
Department of Environmental Systems Science, Swiss Federal Institute of Technology, ETH Zurich, Universitätsstrasse 2, 8092 Zurich, Switzerland
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Kristof Van Oost and Johan Six
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Charlotte Decock, Juhwan Lee, Matti Barthel, Elizabeth Verhoeven, Franz Conen, and Johan Six
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Zefang Shen, Haylee D'Agui, Lewis Walden, Mingxi Zhang, Tsoek Man Yiu, Kingsley Dixon, Paul Nevill, Adam Cross, Mohana Matangulu, Yang Hu, and Raphael A. Viscarra Rossel
SOIL, 8, 467–486, https://doi.org/10.5194/soil-8-467-2022, https://doi.org/10.5194/soil-8-467-2022, 2022
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We compared miniaturised visible and near-infrared spectrometers to a portable visible–near-infrared instrument, which is more expensive. Statistical and machine learning algorithms were used to model 29 key soil health indicators. Accuracy of the miniaturised spectrometers was comparable to the portable system. Soil spectroscopy with these tiny sensors is cost-effective and could diagnose soil health, help monitor soil rehabilitation, and deliver positive environmental and economic outcomes.
Tegawende Léa Jeanne Ilboudo, Lucien NGuessan Diby, Delwendé Innocent Kiba, Tor Gunnar Vågen, Leigh Ann Winowiecki, Hassan Bismarck Nacro, Johan Six, and Emmanuel Frossard
EGUsphere, https://doi.org/10.5194/egusphere-2022-209, https://doi.org/10.5194/egusphere-2022-209, 2022
Preprint withdrawn
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Our results showed that at landscape level SOC stock variability was mainly explained by clay content. We found significant linear positive relationships between VC and SOC stocks for the land uses annual croplands, perennial croplands, grasslands and bushlands without soil depth restrictions until 110 cm. We concluded that in the forest-savanna transition zone, soil properties and topography determine land use, which in turn affects the stocks of SOC and TN and to some extent the VC stocks.
Yuanyuan Yang, Zefang Shen, Andrew Bissett, and Raphael A. Viscarra Rossel
SOIL, 8, 223–235, https://doi.org/10.5194/soil-8-223-2022, https://doi.org/10.5194/soil-8-223-2022, 2022
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We present a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. It uses state-of-the-art machine learning with publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared wavelengths. The estimates could serve to supplement the more expensive molecular approaches towards a better understanding of soil fungal abundance and diversity in agronomy and ecology.
Philipp Baumann, Juhwan Lee, Emmanuel Frossard, Laurie Paule Schönholzer, Lucien Diby, Valérie Kouamé Hgaza, Delwende Innocent Kiba, Andrew Sila, Keith Sheperd, and Johan Six
SOIL, 7, 717–731, https://doi.org/10.5194/soil-7-717-2021, https://doi.org/10.5194/soil-7-717-2021, 2021
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This work delivers openly accessible and validated calibrations for diagnosing 26 soil properties based on mid-infrared spectroscopy. These were developed for four regions in Burkina Faso and Côte d'Ivoire, including 80 fields of smallholder farmers. The models can help to site-specifically and cost-efficiently monitor soil quality and fertility constraints to ameliorate soils and yields of yam or other staple crops in the four regions between the humid forest and the northern Guinean savanna.
Laura Summerauer, Philipp Baumann, Leonardo Ramirez-Lopez, Matti Barthel, Marijn Bauters, Benjamin Bukombe, Mario Reichenbach, Pascal Boeckx, Elizabeth Kearsley, Kristof Van Oost, Bernard Vanlauwe, Dieudonné Chiragaga, Aimé Bisimwa Heri-Kazi, Pieter Moonen, Andrew Sila, Keith Shepherd, Basile Bazirake Mujinya, Eric Van Ranst, Geert Baert, Sebastian Doetterl, and Johan Six
SOIL, 7, 693–715, https://doi.org/10.5194/soil-7-693-2021, https://doi.org/10.5194/soil-7-693-2021, 2021
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We present a soil mid-infrared library with over 1800 samples from central Africa in order to facilitate soil analyses of this highly understudied yet critical area. Together with an existing continental library, we demonstrate a regional analysis and geographical extrapolation to predict total carbon and nitrogen. Our results show accurate predictions and highlight the value that the data contribute to existing libraries. Our library is openly available for public use and for expansion.
Juhwan Lee, Raphael A. Viscarra Rossel, Mingxi Zhang, Zhongkui Luo, and Ying-Ping Wang
Biogeosciences, 18, 5185–5202, https://doi.org/10.5194/bg-18-5185-2021, https://doi.org/10.5194/bg-18-5185-2021, 2021
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We performed Roth C simulations across Australia and assessed the response of soil carbon to changing inputs and future climate change using a consistent modelling framework. Site-specific initialisation of the C pools with measurements of the C fractions is essential for accurate simulations of soil organic C stocks and composition at a large scale. With further warming, Australian soils will become more vulnerable to C loss: natural environments > native grazing > cropping > modified grazing.
Sebastian Doetterl, Rodrigue K. Asifiwe, Geert Baert, Fernando Bamba, Marijn Bauters, Pascal Boeckx, Benjamin Bukombe, Georg Cadisch, Matthew Cooper, Landry N. Cizungu, Alison Hoyt, Clovis Kabaseke, Karsten Kalbitz, Laurent Kidinda, Annina Maier, Moritz Mainka, Julia Mayrock, Daniel Muhindo, Basile B. Mujinya, Serge M. Mukotanyi, Leon Nabahungu, Mario Reichenbach, Boris Rewald, Johan Six, Anna Stegmann, Laura Summerauer, Robin Unseld, Bernard Vanlauwe, Kristof Van Oost, Kris Verheyen, Cordula Vogel, Florian Wilken, and Peter Fiener
Earth Syst. Sci. Data, 13, 4133–4153, https://doi.org/10.5194/essd-13-4133-2021, https://doi.org/10.5194/essd-13-4133-2021, 2021
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The African Tropics are hotspots of modern-day land use change and are of great relevance for the global carbon cycle. Here, we present data collected as part of the DFG-funded project TropSOC along topographic, land use, and geochemical gradients in the eastern Congo Basin and the Albertine Rift. Our database contains spatial and temporal data on soil, vegetation, environmental properties, and land management collected from 136 pristine tropical forest and cropland plots between 2017 and 2020.
Philipp Baumann, Anatol Helfenstein, Andreas Gubler, Armin Keller, Reto Giulio Meuli, Daniel Wächter, Juhwan Lee, Raphael Viscarra Rossel, and Johan Six
SOIL, 7, 525–546, https://doi.org/10.5194/soil-7-525-2021, https://doi.org/10.5194/soil-7-525-2021, 2021
Short summary
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We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 16 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to a fast chemical diagnosis. Our data-driven reduction of the library has the potential to accurately monitor carbon at the plot scale.
Mario Reichenbach, Peter Fiener, Gina Garland, Marco Griepentrog, Johan Six, and Sebastian Doetterl
SOIL, 7, 453–475, https://doi.org/10.5194/soil-7-453-2021, https://doi.org/10.5194/soil-7-453-2021, 2021
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In deeply weathered tropical rainforest soils of Africa, we found that patterns of soil organic carbon stocks differ between soils developed from geochemically contrasting parent material due to differences in the abundance of organo-mineral complexes, the presence/absence of chemical stabilization mechanisms of carbon with minerals and the presence of fossil organic carbon from sedimentary rocks. Physical stabilization mechanisms by aggregation provide additional protection of soil carbon.
Sophie F. von Fromm, Alison M. Hoyt, Markus Lange, Gifty E. Acquah, Ermias Aynekulu, Asmeret Asefaw Berhe, Stephan M. Haefele, Steve P. McGrath, Keith D. Shepherd, Andrew M. Sila, Johan Six, Erick K. Towett, Susan E. Trumbore, Tor-G. Vågen, Elvis Weullow, Leigh A. Winowiecki, and Sebastian Doetterl
SOIL, 7, 305–332, https://doi.org/10.5194/soil-7-305-2021, https://doi.org/10.5194/soil-7-305-2021, 2021
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We investigated various soil and climate properties that influence soil organic carbon (SOC) concentrations in sub-Saharan Africa. Our findings indicate that climate and geochemistry are equally important for explaining SOC variations. The key SOC-controlling factors are broadly similar to those for temperate regions, despite differences in soil development history between the two regions.
Simon Baumgartner, Marijn Bauters, Matti Barthel, Travis W. Drake, Landry C. Ntaboba, Basile M. Bazirake, Johan Six, Pascal Boeckx, and Kristof Van Oost
SOIL, 7, 83–94, https://doi.org/10.5194/soil-7-83-2021, https://doi.org/10.5194/soil-7-83-2021, 2021
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We compared stable isotope signatures of soil profiles in different forest ecosystems within the Congo Basin to assess ecosystem-level differences in N cycling, and we examined the local effect of topography on the isotopic signature of soil N. Soil δ15N profiles indicated that the N cycling in in the montane forest is more closed, whereas the lowland forest and Miombo woodland experienced a more open N cycle. Topography only alters soil δ15N values in forests with high erosional forces.
Zhongkui Luo, Raphael A. Viscarra-Rossel, and Tian Qian
Biogeosciences, 18, 2063–2073, https://doi.org/10.5194/bg-18-2063-2021, https://doi.org/10.5194/bg-18-2063-2021, 2021
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Using the data from 141 584 whole-soil profiles across the globe, we disentangled the relative importance of biotic, climatic and edaphic variables in controlling global SOC stocks. The results suggested that soil properties and climate contributed similarly to the explained global variance of SOC in four sequential soil layers down to 2 m. However, the most important individual controls are consistently soil-related, challenging current climate-driven framework of SOC dynamics.
Simon Baumgartner, Matti Barthel, Travis William Drake, Marijn Bauters, Isaac Ahanamungu Makelele, John Kalume Mugula, Laura Summerauer, Nora Gallarotti, Landry Cizungu Ntaboba, Kristof Van Oost, Pascal Boeckx, Sebastian Doetterl, Roland Anton Werner, and Johan Six
Biogeosciences, 17, 6207–6218, https://doi.org/10.5194/bg-17-6207-2020, https://doi.org/10.5194/bg-17-6207-2020, 2020
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Soil respiration is an important carbon flux and key process determining the net ecosystem production of terrestrial ecosystems. The Congo Basin lacks studies quantifying carbon fluxes. We measured soil CO2 fluxes from different forest types in the Congo Basin and were able to show that, even though soil CO2 fluxes are similarly high in lowland and montane forests, the drivers were different: soil moisture in montane forests and C availability in the lowland forests.
Cited articles
Araújo, S. R., Wetterlind, J., Demattê, J. A. M., and Stenberg, B.:
Improving the Prediction Performance of a Large Tropical Vis-NIR
Spectroscopic Soil Library from Brazil by Clustering into Smaller Subsets
or Use of Data Mining Calibration Techniques, Eur. J. Soil
Sci., 65, 718–729, https://doi.org/10.1111/ejss.12165, 2014. a
Bader, C., Müller, M., Szidat, S., Schulin, R., and Leifeld, J.: Response
of Peat Decomposition to Corn Straw Addition in Managed Organic Soils,
Geoderma, 309, 75–83, https://doi.org/10.1016/j.geoderma.2017.09.001, 2018. a
Baumann, P.: Simplerspec, GitHub, available at:
https://github.com/philipp-baumann/simplerspec (last access: 12 November 2020), 2020. a
Baumann, P., Helfenstein, A., Gubler, A., Keller, A., Meuli, R. G., Wächter, D., Lee, J., Viscarra Rossel, R., and Six, J.: Developing the Swiss soil spectral library for local estimation and monitoring, SOIL Discuss. [preprint], https://doi.org/10.5194/soil-2020-105, in review, 2021. a, b, c, d, e, f, g, h, i, j
Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., Roger, J.-M., and
McBratney, A.: Critical Review of Chemometric Indicators Commonly Used for
Assessing the Quality of the Prediction of Soil Attributes by NIR
Spectroscopy, TrAC Trends Anal. Chem., 29, 1073–1081,
https://doi.org/10.1016/j.trac.2010.05.006, 2010. a
Bornemann, L., Welp, G., and Amelung, W.: Particulate Organic Matter at the
Field Scale: Rapid Acquisition Using Mid-Infrared Spectroscopy,
Soil Sci. Soc. Am. J., 74, 1147–1156,
https://doi.org/10.2136/sssaj2009.0195, 2010. a, b
Brodský, L., Klement, A., Penížek, V., Kodešová, R., and
Borůvka, L.: Building Soil Spectral Library of the Czech Soils for
Quantitative Digital Soil Mapping, Soil Water Res., 6, 165–172,
https://doi.org/10.17221/24/2011-SWR, 2011. a
Brodský, L., Vašát, R., Klement, A., Zádorová, T., and
Jakšík, O.: Uncertainty Propagation in VNIR Reflectance
Spectroscopy Soil Organic Carbon Mapping, Geoderma, 199, 54–63,
https://doi.org/10.1016/j.geoderma.2012.11.006, 2013. a
Brown, D. J.: Using a Global VNIR Soil-Spectral Library for Local Soil
Characterization and Landscape Modeling in a 2nd-Order Uganda Watershed,
Geoderma, 140, 444–453, https://doi.org/10.1016/j.geoderma.2007.04.021, 2007. a
Burgos, S., Nussbaum, M., Tatti, D., Kellermann, L., and Oechslin, S.:
Bodenkartierung St. Galler Rheintal, Zwischenbericht, Berner
Fachhochschule, Hochschule für Agrar-, Forst- und
Lebensmittelwissenschaften HAFL, Zollikofen, 2018. a
Calderón, F. J., Reeves, J. B., Collins, H. P., and Paul, E. A.: Chemical
Differences in Soil Organic Matter Fractions Determined by
Diffuse-Reflectance Mid-Infrared Spectroscopy, Soil Sci.
Soc. Am. J., 75, 568–579, https://doi.org/10.2136/sssaj2009.0375, 2011. a, b
Cardelli, V., Weindorf, D. C., Chakraborty, S., Li, B., De Feudis, M., Cocco,
S., Agnelli, A., Choudhury, A., Ray, D. P., and Corti, G.: Non-Saturated Soil
Organic Horizon Characterization via Advanced Proximal Sensors, Geoderma,
288, 130–142, https://doi.org/10.1016/j.geoderma.2016.10.036, 2017. a
Clairotte, M., Grinand, C., Kouakoua, E., Thébault, A., Saby, N. P.,
Bernoux, M., and Barthès, B. G.: 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. a, b, c
Dangal, S., Sanderman, J., Wills, S., and Ramirez-Lopez, L.: Accurate and
Precise Prediction of Soil Properties from a Large Mid-Infrared
Spectral Library, Soil Sys., 3, 11, https://doi.org/10.3390/soilsystems3010011,
2019. a, b, c, d
Demattê, J. A., Dotto, A. C., Paiva, A. F., Sato, M. V., Dalmolin, R. S.,
de Araújo, M. d. S. B., da Silva, E. B., Nanni, M. R., ten Caten,
A., Noronha, N. C., Lacerda, M. P., de Araújo Filho, J. C., Rizzo, R.,
Bellinaso, H., Francelino, M. R., Schaefer, C. E., Vicente, L. E., dos
Santos, U. J., de Sá Barretto Sampaio, E. V., Menezes, R. S., de
Souza, J. J. L., Abrahão, W. A., Coelho, R. M., Grego, C. R., Lani,
J. L., Fernandes, A. R., Gonçalves, D. A., Silva, S. H., de Menezes,
M. D., Curi, N., Couto, E. G., dos Anjos, L. H., Ceddia, M. B., Pinheiro,
É. F., Grunwald, S., Vasques, G. M., Marques Júnior, J., da Silva,
A. J., Barreto, M. C. d. V., Nóbrega, G. N., da Silva, M. Z., de
Souza, S. F., Valladares, G. S., Viana, J. H. M., da Silva Terra, F.,
Horák-Terra, I., Fiorio, P. R., da Silva, R. C., Frade Júnior,
E. F., Lima, R. H., Alba, J. M. F., de Souza Junior, V. S., Brefin, M. D.
L. M. S., Ruivo, M. D. L. P., Ferreira, T. O., Brait, M. A., Caetano, N. R.,
Bringhenti, I., de Sousa Mendes, W., Safanelli, J. L., Guimarães,
C. C., Poppiel, R. R., e Souza, A. B., Quesada, C. A., and do Couto, H.
T. Z.: The Brazilian Soil Spectral Library (BSSL): A General
View, Application and Challenges, Geoderma, 354, 113793,
https://doi.org/10.1016/j.geoderma.2019.05.043, 2019. a
Ferré, M., Engel, S., and Gsottbauer, E.: Which Agglomeration Payment
for a Sustainable Management of Organic Soils in Switzerland?
– An Experiment Accounting for Farmers' Cost
Heterogeneity, Ecol. Econ., 150, 24–33,
https://doi.org/10.1016/j.ecolecon.2018.03.028, 2018. a, b
Gholizadeh, A., Borůvka, L., Saberioon, M., and Vašát, R.: A
Memory-Based Learning Approach as Compared to Other Data Mining
Algorithms for the Prediction of Soil Texture Using Diffuse
Reflectance Spectra, Remote Sens., 8, 341, https://doi.org/10.3390/rs8040341, 2016. a
Gogé, F., Joffre, R., Jolivet, C., Ross, I., and Ranjard, L.: Optimization
Criteria in Sample Selection Step of Local Regression for Quantitative
Analysis of Large Soil NIRS Database, Chemometrics and Intelligent
Laboratory Systems, 110, 168–176, https://doi.org/10.1016/j.chemolab.2011.11.003,
2012. a
Guerrero, C., Wetterlind, J., Stenberg, B., Mouazen, A. M.,
Gabarrón-Galeote, M. A., Ruiz-Sinoga, J. D., Zornoza, R., and
Viscarra Rossel, R. A.: Do We Really Need Large Spectral Libraries for Local
Scale SOC Assessment with NIR Spectroscopy?, Soil and Tillage
Research, 155, 501–509, https://doi.org/10.1016/j.still.2015.07.008, 2016. a, b
Guillou, F. L., Wetterlind, W., Viscarra Rossel, R. A., Hicks, W., Grundy, M.,
and Tuomi, S.: How Does Grinding Affect the Mid-Infrared Spectra of Soil and
Their Multivariate Calibrations to Texture and Organic Carbon?, Soil
Res., 53, 913, https://doi.org/10.1071/SR15019, 2015. a, b, c
Hartigan, J. A. and Wong, M. A.: Algorithm AS 136: A K-Means
Clustering Algorithm, J. Roy. Stat. Soc. Ser. C, 28, 100–108, https://doi.org/10.2307/2346830, 1979. a
Hastie, T., Tibshirani, R., and Friedman, J. H.: The Elements of Statistical
Learning: Data Mining, Inference, and Prediction, Springer Series in
Statistics, Springer, New York, NY, 2nd ed edn., 2009. a
ICRAF: A Globally Distributed Soil Spectral Library Visible Near Infrared
Diffuse Reflectance Spectra – World Agroforestry Centre (ICRAF) –
ISRIC – World Soil Information, available at:
http://www.worldagroforestry.org/sd/landhealth/soil-plant-spectral-diagnostics-laboratory/soil-spectra-library (last access: 10 September 2020),
2020. a
IUSS Working Group WRB: World Reference Base for Soil Resources 2014.
International Soil Classification System for Naming Soils and Creating
Legends for Soil Maps, World Soil Resources Reports No. 106, FAO,
Rome, 2014. a
Janik, L. J. and Skjemstad, J. O.: Characterization and Analysis of Soils Using
Mid-Infrared Partial Least-Squares .2. Correlations with Some Laboratory
Data, Soil Res., 33, 637–650, https://doi.org/10.1071/sr9950637, 1995. a
Janik, L. J., Skjemstad, J. O., and Merry, R. H.: Can Mid Infrared Diffuse
Reflectance Analysis Replace Soil Extractions?, Aust. J.
Exp. Agr., 38, 681, https://doi.org/10.1071/EA97144, 1998. a
Joosten, H.: The Global Peatland CO2 Picture: Peatland Status and Drainage
Related Emissions in All Countries of the World, Wetlands International,
p. 36, 2010. a
Kennard, R. W. and Stone, L. A.: Computer Aided Design of Experiments,
Technometrics, 11, 137–148, https://doi.org/10.1080/00401706.1969.10490666, 1969. a, b
Knadel, M., Deng, F., Thomsen, A., and Greve, M.: Development of a Danish
National Vis-NIR Soil Spectral Library for Soil Organic Carbon
Determination, pp. 403–408, CRC Press, 2012. a
Leifeld, J. and Menichetti, L.: The Underappreciated Potential of Peatlands in
Global Climate Change Mitigation Strategies, Nat. Commun., 9, 1071,
https://doi.org/10.1038/s41467-018-03406-6, 2018. a
Leifeld, J., Bassin, S., and Fuhrer, J.: Carbon Stocks in Swiss
Agricultural Soils Predicted by Land-Use, Soil Characteristics, and Altitude,
Agr. Ecosyst. Environ., 105, 255–266,
https://doi.org/10.1016/j.agee.2004.03.006, 2005. a
Leifeld, J., Müller, M., and Fuhrer, J.: Peatland Subsidence and Carbon
Loss from Drained Temperate Fens, Soil Use Manage., 27, 170–176,
https://doi.org/10.1111/j.1475-2743.2011.00327.x, 2011. a
Madari, B. E., Reeves, J. B., Machado, P. L., Guimarães, C. M., Torres, E.,
and McCarty, G. W.: Mid- and near-Infrared Spectroscopic Assessment of Soil
Compositional Parameters and Structural Indices in Two Ferralsols,
Geoderma, 136, 245–259, https://doi.org/10.1016/j.geoderma.2006.03.026, 2006. a, b, c
Mallavan, B., Minasny, B., and McBratney, A.: Homosoil, a Methodology for
Quantitative Extrapolation of Soil Information Across the Globe,
in: Digital Soil Mapping: Bridging Research, Environmental
Application, and Operation, edited by: Boettinger, J. L., Howell, D. W.,
Moore, A. C., Hartemink, A. E., and Kienast-Brown, S., Progress in Soil
Science, pp. 137–150, Springer Netherlands, Dordrecht,
https://doi.org/10.1007/978-90-481-8863-5_12, 2010. a
Nash, J. E. and Sutcliffe, J. V.: River Flow Forecasting through Conceptual
Models Part I – A Discussion of Principles, J.
Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970. a
Nocita, M., Stevens, A., Toth, G., Panagos, P., van Wesemael, B., and
Montanarella, L.: 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. a, b
Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., Bachmann, M.,
Barthès, B., Ben Dor, E., Brown, D. J., Clairotte, M., Csorba, A.,
Dardenne, P., Demattê, J. A., Genot, V., Guerrero, C., Knadel, M.,
Montanarella, L., Noon, C., Ramirez-Lopez, L., Robertson, J., Sakai, H.,
Soriano-Disla, J. M., Shepherd, K. D., Stenberg, B., Towett, E. K., Vargas,
R., and Wetterlind, J.: Soil Spectroscopy: An Alternative to Wet
Chemistry for Soil Monitoring, Adv. Agr., 132,
139–159, https://doi.org/10.1016/bs.agron.2015.02.002, 2015. a, b
Noellemeyer, E. and Six, J.: Basic Principles of Soil Carbon Management
for Multiple Ecosystem Benefits, in: Soil Carbon: Science, Management,
and Policy for Multiple Benefits, no. volume 71 in SCOPE Series, CAB
International, Wallingford, Oxfordshire, UK, Boston, MA, USA, 2015. a
Padarian, J., Minasny, B., and McBratney, A. B.: Using Deep Learning to Predict
Soil Properties from Regional Spectral Data, Geoderma Regional, 16, e00198,
https://doi.org/10.1016/j.geodrs.2018.e00198, 2019b. a, b, c
Pan, S. J. and Yang, Q.: A Survey on Transfer Learning, IEEE Trans.
Knowl. Data Eng., 22, 1345–1359, https://doi.org/10.1109/TKDE.2009.191, 2010. a
Parish, F., Sirin, A., Charman, D., Joosten, H., Minayeva, T., Silvius, M., and
Stringer, L.: Assessment on Peatlands, Biodiversity and Climate
Change: Main Report, Tech. rep., Global Environment Centre, Kuala
Lumpur and Wetlands International, Wageningen, 2008. a
Ramirez-Lopez, L., Behrens, T., Schmidt, K., Rossel, R. V., Demattê, J.,
and Scholten, T.: Distance and Similarity-Search Metrics for Use with Soil
Vis-NIR Spectra, Geoderma, 199, 43–53,
https://doi.org/10.1016/j.geoderma.2012.08.035, 2013. a
Ramirez-Lopez, L., Wadoux, A. M. J.-C., Franceschini, M. H. D., Terra, F. S.,
Marques, K. P. P., Sayão, V. M., and Demattê, J. A. M.: Robust Soil
Mapping at the Farm Scale with Vis-NIR Spectroscopy, Eur.
J. Soil Sci., 70, 378–393, https://doi.org/10.1111/ejss.12752, 2019. a
Reeves, J. B. and Smith, D. B.: The Potential of Mid- and near-Infrared Diffuse
Reflectance Spectroscopy for Determining Major- and Trace-Element
Concentrations in Soils from a Geochemical Survey of North America,
Appl. Geochem., 24, 1472–1481, https://doi.org/10.1016/j.apgeochem.2009.04.017,
2009. a, b
Savitzky, A. and Golay, M.: Smoothing and Differentiation of Data By
Simplified Least Squares Procedures, Anal. Chem., 36, 1627,
https://doi.org/10.1021/ac60214a047, 1964. a, b
Schmidt, M. W. I., Torn, M. S., Abiven, S., Dittmar, T., Guggenberger, G.,
Janssens, I. A., Kleber, M., Kögel-Knabner, I., Lehmann, J., Manning,
D. A. C., Nannipieri, P., Rasse, D. P., Weiner, S., and Trumbore, S. E.:
Persistence of Soil Organic Matter as an Ecosystem Property, Nature, 478,
49–56, https://doi.org/10.1038/nature10386, 2011. a
Seidel, M., Hutengs, C., Ludwig, B., Thiele-Bruhn, S., and Vohland, M.:
Strategies for the Efficient Estimation of Soil Organic Carbon at the Field
Scale with Vis-NIR Spectroscopy: Spectral Libraries and Spiking vs.
Local Calibrations, Geoderma, 354, 113856,
https://doi.org/10.1016/j.geoderma.2019.07.014, 2019. a
Shepherd, K. D. and Walsh, M. G.: Development of Reflectance Spectral
Libraries for Characterization of Soil Properties, Soil Sci.
Soc. Am. J., 66, 988–998, https://doi.org/10.2136/sssaj2002.9880, 2002. a
Shi, Z., Wang, Q., Peng, J., Ji, W., Liu, H., Li, X., and Viscarra Rossel,
R. A.: Development of a National VNIR Soil-Spectral Library for Soil
Classification and Prediction of Organic Matter Concentrations, Sci. China
Earth Sci., 57, 1671–1680, https://doi.org/10.1007/s11430-013-4808-x, 2014. a
Sila, A. M., Shepherd, K. D., and Pokhariyal, G. P.: Evaluating the Utility of
Mid-Infrared Spectral Subspaces for Predicting Soil Properties, Chemometr. Intell. Lab., 153, 92–105,
https://doi.org/10.1016/j.chemolab.2016.02.013, 2016. a
Stenberg, B., Viscarra Rossel, R. A., Mouazen, A. M., and Wetterlind, J.:
Chapter Five – Visible and Near Infrared Spectroscopy in Soil
Science, in: Advances in Agronomy, edited by: Sparks, D. L., vol. 107,
pp. 163–215, Academic Press, https://doi.org/10.1016/S0065-2113(10)07005-7, 2010. a
Stevens, A., Nocita, M., Tóth, G., Montanarella, L., and van Wesemael,
B.: Prediction of Soil Organic Carbon at the European Scale by
Visible and Near InfraRed Reflectance Spectroscopy, PLoS ONE, 8,
e66409, https://doi.org/10.1371/journal.pone.0066409, 2013. a, b, c
Tiessen, H., Cuevas, E., and Chacon, P.: The Role of Soil Organic Matter in
Sustaining Soil Fertility, Nature, 371, 783–785, https://doi.org/10.1038/371783a0,
1994. a
Viscarra Rossel, R., Walvoort, D., McBratney, A., Janik, L., and Skjemstad, J.:
Visible, near Infrared, Mid Infrared or Combined Diffuse Reflectance
Spectroscopy for Simultaneous Assessment of Various Soil Properties,
Geoderma, 131, 59–75, https://doi.org/10.1016/j.geoderma.2005.03.007, 2006. a
Viscarra Rossel, R., Behrens, T., Ben-Dor, E., Brown, D., Demattê, J.,
Shepherd, K., Shi, Z., Stenberg, B., Stevens, A., Adamchuk, V., Aïchi,
H., Barthès, B., Bartholomeus, H., Bayer, A., Bernoux, M., Böttcher,
K., Brodský, L., Du, C., Chappell, A., Fouad, Y., Genot, V., Gomez, C.,
Grunwald, S., Gubler, A., Guerrero, C., Hedley, C., Knadel, M., Morrás,
H., Nocita, M., Ramirez-Lopez, L., Roudier, P., Campos, E. R., Sanborn, P.,
Sellitto, V., Sudduth, K., Rawlins, B., Walter, C., Winowiecki, L., Hong, S.,
and Ji, W.: A Global Spectral Library to Characterize the World's Soil,
Earth-Sci. Rev., 155, 198–230, https://doi.org/10.1016/j.earscirev.2016.01.012,
2016a. a, b, c
Viscarra Rossel, R., Brus, D., Lobsey, C., Shi, Z., and McLachlan, G.: Baseline
Estimates of Soil Organic Carbon by Proximal Sensing: Comparing
Design-Based, Model-Assisted and Model-Based Inference, Geoderma, 265,
152–163, https://doi.org/10.1016/j.geoderma.2015.11.016, 2016b. a
Viscarra Rossel, R. A. and 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. a
Viscarra Rossel, R. A., Jeon, Y. S., Odeh, I. O. A., and McBratney, A. B.:
Using a Legacy Soil Sample to Develop a Mid-IR Spectral Library, Soil
Res., 46, 1, https://doi.org/10.1071/SR07099, 2008. a, b
Viscarra Rossel, R. A., McBratney, A. B., and Minasny, B.: Preface, in:
Proximal Soil Sensing, Progress in Soil Science, pp. i–xxiv,
Springer, Dordrecht New York, 2010. a
Wetterlind, J. and Stenberg, B.: Near-Infrared Spectroscopy for within-Field
Soil Characterization: Small Local Calibrations Compared with National
Libraries Spiked with Local Samples, Eur. J. Soil Sci., 61,
823–843, https://doi.org/10.1111/j.1365-2389.2010.01283.x, 2010. a, b
Wijewardane, N. K., Ge, Y., Wills, S., and Libohova, Z.: Predicting
Physical and Chemical Properties of US Soils with a
Mid-Infrared Reflectance Spectral Library, Soil Sci. Soc.
Am. J., 82, 722–731, https://doi.org/10.2136/sssaj2017.10.0361, 2018. a, b, c
Wilks, D.: Chapter 8: Forecast Verification, in: Statistical Methods in
the Atmospheric Sciences, vol. 100, pp. 301–394, Elsevier, third edn.,
https://doi.org/10.1016/B978-0-12-385022-5.00008-7, 2011. a
Williams, P. and Norris, K.: Near-Infrared Technology in the Agricultural and
Food Industries, Near-infrared technology in the agricultural and food
industries, American Association of Cereal Chemists, Inc., St. Paul, Minnesota, USA, ISBN 0-913250-49-X, Record Nr.: 19892442443, 1987. a
Wold, H.: 11 – Path Models with Latent Variables: The NIPALS
Approach**NIPALS = Nonlinear Iterative PArtial Least Squares, in:
Quantitative Sociology, edited by: Blalock, H. M., Aganbegian, A.,
Borodkin, F. M., Boudon, R., and Capecchi, V., International Perspectives
on Mathematical and Statistical Modeling, pp. 307–357, Academic
Press, https://doi.org/10.1016/B978-0-12-103950-9.50017-4, 1975. a
Wold, S., Martens, H., and Wold, H.: The Multivariate Calibration Problem in
Chemistry Solved by the PLS Method, in: Matrix Pencils, edited by:
Kågström, B. and Ruhe, A., vol. 973, pp. 286–293, Springer Berlin
Heidelberg, Berlin, Heidelberg, https://doi.org/10.1007/BFb0062108, 1983. a
Wold, S., Ruhe, A., Wold, H., and Dunn, W., I.: The Collinearity Problem in
Linear Regression. The Partial Least Squares (PLS) Approach
to Generalized Inverses, SIAM J. Sci. and Stat. Comput., 5, 735–743,
https://doi.org/10.1137/0905052, 1984. a
Wold, S., Sjöström, M., and Eriksson, L.: PLS-Regression: A Basic
Tool of Chemometrics, Chemometr. Intell. Lab., 58,
109–130, https://doi.org/10.1016/S0169-7439(01)00155-1, 2001. a
Zobeck, T. M., Baddock, M., Scott Van Pelt, R., Tatarko, J., and
Acosta-Martinez, V.: Soil Property Effects on Wind Erosion of Organic
Soils, Aeolian Res., 10, 43–51, https://doi.org/10.1016/j.aeolia.2012.10.005,
2013. a
Short summary
In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at new and very different locations. The importance of this finding is that it requires less time-consuming lab work than calibrating a new model for every local application, while still remaining similar to or more accurate than local models. Furthermore, we show that this method even works for predicting (drained) peat soils, using a SSL with mostly mineral soils containing much less soil carbon.
In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at...
