Articles | Volume 7, issue 2
https://doi.org/10.5194/soil-7-525-2021
© 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-7-525-2021
© 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
| 
18 Aug 2021
Original research article |
| 18 Aug 2021
| 18 Aug 2021
Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring
Philipp Baumann
CORRESPONDING AUTHOR
Institute of Agricultural Sciences, Department of Environmental Systems Science (D-USYS), ETH Zürich, Zurich, Switzerland
Swiss Competence Center for Soils (KOBO), School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences BFH, Bern, Switzerland
Anatol Helfenstein
Institute of Agricultural Sciences, Department of Environmental Systems Science (D-USYS), ETH Zürich, Zurich, Switzerland
Soil Geography and Landscape Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Andreas Gubler
Swiss Soil Monitoring Network (NABO), Agroscope, Reckenholzstrasse 191, 8046 Zurich, Switzerland
Armin Keller
Swiss Competence Center for Soils (KOBO), School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences BFH, Bern, Switzerland
Reto Giulio Meuli
Swiss Soil Monitoring Network (NABO), Agroscope, Reckenholzstrasse 191, 8046 Zurich, Switzerland
Daniel Wächter
Swiss Soil Monitoring Network (NABO), Agroscope, Reckenholzstrasse 191, 8046 Zurich, Switzerland
Juhwan Lee
Department of Smart Agro-industry, Gyeongsang National University, Jinju 52725, Republic of Korea
Raphael Viscarra Rossel
Soil and Landscape Science, School of Molecular and Life Sciences, Curtin University, GPO Box U1987, Perth WA 6845, Australia
Johan Six
Institute of Agricultural Sciences, Department of Environmental Systems Science (D-USYS), ETH Zürich, Zurich, Switzerland
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This preprint is open for discussion and under review for SOIL (SOIL).
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To improve soil health and increase crop yields, organic matter is commenly added to arable soils. Studying the effect of different organic amenmends on soil organic carbon sequestration in four long-term field trials in Kenya, we found that only a small portion (< 7 %) of added carbon was stabilised. Moreover, this was only observed in the top 15 cm of the soil. These results underline the challenges associated with increasing the organic carbon content of tropical arable soils.
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Preprint archived
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We characterised the chemical and mineral composition of soil organic carbon fractions with mid-infrared spectroscopy. We identified unique and shared features of the spectra of carbon fractions, and the interactions between their organic and mineral components. These interactions are key to the persistence of C in soils, and we propose that mid-infrared spectroscopy could help to infer stability of soil C.
Moritz Laub, Marc Corbeels, Antoine Couëdel, Samuel Mathu Ndungu, Monicah Wanjiku Mucheru-Muna, Daniel Mugendi, Magdalena Necpalova, Wycliffe Waswa, Marijn Van de Broek, Bernard Vanlauwe, and Johan Six
SOIL, 9, 301–323, https://doi.org/10.5194/soil-9-301-2023, https://doi.org/10.5194/soil-9-301-2023, 2023
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In sub-Saharan Africa, long-term low-input maize cropping threatens soil fertility. We studied how different quality organic inputs combined with mineral N fertilizer could counteract this. Farmyard manure was the best input to counteract soil carbon loss; mineral N fertilizer had no effect on carbon. Yet, the rates needed to offset soil carbon losses are unrealistic for farmers (>10 t of dry matter per hectare and year). Additional agronomic measures may be needed.
Kristof Van Oost and Johan Six
Biogeosciences, 20, 635–646, https://doi.org/10.5194/bg-20-635-2023, https://doi.org/10.5194/bg-20-635-2023, 2023
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The direction and magnitude of the net erosion-induced land–atmosphere C exchange have been the topic of a big scientific debate for more than a decade now. Many have assumed that erosion leads to a loss of soil carbon to the atmosphere, whereas others have shown that erosion ultimately leads to a carbon sink. Here, we show that the soil carbon erosion source–sink paradox is reconciled when the broad range of temporal and spatial scales at which the underlying processes operate are considered.
Charlotte Decock, Juhwan Lee, Matti Barthel, Elizabeth Verhoeven, Franz Conen, and Johan Six
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-221, https://doi.org/10.5194/bg-2022-221, 2022
Preprint withdrawn
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One of the least well understood processes in the nitrogen (N) cycle is the loss of nitrogen gas (N2), referred to as total denitrification. This is mainly due to the difficulty of quantifying total denitrification in situ. In this study, we developed and tested a novel modeling approach to estimate total denitrification over the depth profile, based on concentrations and isotope values of N2O. Our method will help close N budgets and identify management strategies that reduce N pollution.
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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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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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
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Short summary
Short summary
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.
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.
Anatol Helfenstein, Philipp Baumann, Raphael Viscarra Rossel, Andreas Gubler, Stefan Oechslin, and Johan Six
SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, https://doi.org/10.5194/soil-7-193-2021, 2021
Short summary
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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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Ambroise, C. and McLachlan, G. J.: Selection Bias in Gene Extraction on the
Basis of Microarray Gene-Expression Data, P. Natl. Acad. Sci. USA, 99, 6562–6566, https://doi.org/10.1073/pnas.102102699, 2002. a
Angelopoulou, T., Balafoutis, A., Zalidis, G., and Bochtis, D.: From
Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon
Estimation – A Review, Sustainability, 12, 443,
https://doi.org/10.3390/su12020443, 2020. a
Awiti, A. O., Walsh, M. G., Shepherd, K. D., and Kinyamario, J.: Soil condition classification using infrared spectroscopy: A proposition for assessment of soil condition along a tropical forest-cropland chronosequence, Geoderma, 143, 73–84, https://doi.org/10.1016/j.geoderma.2007.08.021, 2008. a
Baumann, P.: philipp-baumann/simplerspec: Beta release simplerspec 0.1.0 for
zenodo, Zenodo [code], https://doi.org/10.5281/zenodo.3303637, 2019. a
Bellman, R.: Adaptive Control Processes: A Guided Tour, Princeton University
Press, available at: http://www.jstor.org/stable/j.ctt183ph6v (last access: 16 August 2021), 1961. a
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-Trend. Anal. Chem., 29, 1073–1081,
https://doi.org/10.1016/j.trac.2010.05.006, 2010. a
Briedis, C., Baldock, J., de Moraes Sá, J. C., dos Santos, J. B., and
Milori, D. M. B. P.: Strategies to Improve the Prediction of Bulk Soil and
Fraction Organic Carbon in Brazilian Samples by Using an Australian
National Mid-Infrared Spectral Library, Geoderma, 373, 114401,
https://doi.org/10.1016/j.geoderma.2020.114401, 2020. a
Bui, E. N., Henderson, B. L., and Viergever, K.: Knowledge Discovery from
Models of Soil Properties Developed through Data Mining, Ecol. Model., 191, 431–446, https://doi.org/10.1016/j.ecolmodel.2005.05.021, 2006. a
Bundesamt für Umwelt (BAFU): Biodiversitätsmonitoring Schweiz BDM, Koordinationsstelle BDM 2014: Biodiversitätsmonitoring Schweiz
BDM. Beschreibung der Methoden und Indikatoren, Umwelt-Wissen Nr. 1410, 104 pp., Bundesamt für Umwelt, Bern, Switzerland, available at: https://www.bafu.admin.ch/bafu/de/home/themen/biodiversitaet/publikationen-studien/publikationen/biodiversitaetsmonitoring.html (last access: 16 August 2021), 2014. a
Clairotte, M., Grinand, C., Kouakoua, E., Thébault, A., Saby, N. P. A.,
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
Dangal, S. R. S., Sanderman, J., Wills, S., and Ramirez-Lopez, L.: Accurate and Precise Prediction of Soil Properties from a Large Mid-Infrared Spectral Library, Soil Syst., 3, 11, https://doi.org/10.3390/soilsystems3010011,
2019. a, b, c
Deng, F., Minasny, B., Knadel, M., McBratney, A., Heckrath, G., and Greve,
M. H.: Using Vis-NIR Spectroscopy for Monitoring Temporal Changes
in Soil Organic Carbon, Soil Sci., 178, 389–399,
https://doi.org/10.1097/SS.0000000000000002, 2013. a
Desaules, A., Ammann, S., and Schwab, P.: Advances in long-term soil-pollution monitoring of Switzerland, J. Plant Nutr. Soil Sc., 173, 525–535, https://doi.org/10.1002/jpln.200900269, 2010. a, b
Dietterich, T. G., Wettschereck, D., Atkeson, C. G., and Moore, A. W.:
Memory-Based Methods for Regression and Classification, in: Advances in
Neural Information Processing Systems 6, 7th NIPS Conference, 29 November–2 December 1993, Denver, Colorado, USA, edited by: Cowan, J. D., Tesauro, G., and Alspector, J., 1165–1166, Morgan Kaufmann, 1993. a
Dokuchaev, V.: Report to the Transcaucasian Statistical Committee on Land
Evaluation in General and Especially for the Transcaucasia.
Horizontal and Vertical Soil Zones, Off. Press
Civ, Affairs Commander-in-Chief Cacasus, Tiflis, Russia, 1899 (in Russian). a
Dowle, M. and Srinivasan, A.: data.table: Extension of “data.frame”, r package version 1.12.8,
available at: https://CRAN.R-project.org/package=data.table (last access: 16 August 2021), 2019. a
England, J. R. and Viscarra Rossel, R. A.: Proximal sensing for soil carbon accounting, SOIL, 4, 101–122, https://doi.org/10.5194/soil-4-101-2018, 2018. a
Friedman, J., Hastie, T., and Tibshirani, R.: The elements of statistical
learning, Springer series in statistics Springer, Berlin, 2nd edn., available at: http://statweb.stanford.edu/~tibs/book/preface.ps (last access: 16 August 2021),
2008. a
Grêt-Regamey, A., Kool, S., Bühlmann, L., and Kissling, S.: Eine
Bodenagenda für die Raumplanung, Thematische Synthese TS3 des Nationalen
Forschungsprogramms “Nachhaltige Nutzung der Ressource
Boden” (NFP 68), Swiss National Science Foundation (SNF),
Bern, Switzerland, 2018. a
Gubler, A., Wächter, D., and Schwab, P.: Homogenisation of Series of Soil
Organic Carbon: Harmonising Results by Wet Oxidation (Swiss Standard
Method) and Dry Combustion, Agroscope Science, 62, 1–9, available at: https://ira.agroscope.ch/en-US/publication/37689
(last access: 16 August 2021), 2018. a
Guerrero, C., Zornoza, R., Gómez, I., and Mataix-Beneyto, J.: Spiking of NIR Regional Models Using Samples from Target Sites: Effect of Model Size on Prediction Accuracy, Geoderma, 158, 66–77,
https://doi.org/10.1016/j.geoderma.2009.12.021, 2010. a
Guerrero, C., Stenberg, B., Wetterlind, J., Viscarra Rossel, R. A., Maestre,
F. T., Mouazen, A. M., Zornoza, R., Ruiz-Sinoga, J. D., and Kuang, B.:
Assessment of Soil Organic Carbon at Local Scale with Spiked NIR
Calibrations: Effects of Selection and Extra-Weighting on the Spiking Subset:
Spiking and Extra-Weighting to Improve Soil Organic Carbon Predictions
with NIR, Eur. J. Soil Sci., 65, 248–263,
https://doi.org/10.1111/ejss.12129, 2014. a, b
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 Till. Res., 155, 501–509,
https://doi.org/10.1016/j.still.2015.07.008, 2016. a, b
Guyon, I., Weston, J., Barnhill, S., and Vapnik, V.: Gene Selection for
Cancer Classification Using Support Vector Machines, Mach.
Learn., 46, 389–422, https://doi.org/10.1023/A:1012487302797, 2002. a
Hand, D. J. and Vinciotti, V.: Local Versus Global Models for
Classification Problems: Fitting Models Where it Matters,
Am. Stat., 57, 124–131, https://doi.org/10.1198/0003130031423, 2003. a, b
Helfenstein, A., Baumann, P., Viscarra Rossel, R., Gubler, A., Oechslin, S., and Six, J.: Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library, SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, 2021. a
Helfenstein, A., Baumann, P., Viscarra Rossel, R., Gubler, A., Oechslin, S., and Six, J.: Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library, SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, 2021. a
Hong, Y., Chen, S., Liu, Y., Zhang, Y., Yu, L., Chen, Y., Liu, Y., Cheng, H.,
and Liu, Y.: Combination of Fractional Order Derivative and Memory-Based
Learning Algorithm to Improve the Estimation Accuracy of Soil Organic Matter
by Visible and near-Infrared Spectroscopy, CATENA, 174, 104–116,
https://doi.org/10.1016/j.catena.2018.10.051, 2019. a
Hubert, M. and Debruyne, M.: Minimum Covariance Determinant, WIREs
Computational Statistics, 2, 36–43, https://doi.org/10.1002/wics.61, 2010. 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, Aust. J. Soil Res., 33, 637–650,
https://doi.org/10.1071/sr9950637, 1995. a, b, c
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
Jenny, H.: Factors of Soil Formation, McGraw-Hill Book Co., New York, USA, 1941. a
Keller, A., Franzen, J., Knüsel, P., Papritz, A., and Zürrer, M.:
Bodeninformations-Plattform Schweiz (BIP-CH), Thematische Synthese TS4 des
Nationalen Forschungsprogramms “Nachhaltige Nutzung der
Ressource Boden” (NFP 68), Swiss National Science Foundation
(SNF), Bern, Switzerland, 2018. a
Kuhn, M.: caret: Classification and Regression Training, r package version
6.0-85, available at: https://CRAN.R-project.org/package=caret (last access: 16 August 2021), 2020. a
Lin, J.-H. and Vitter, J. S.: A Theory for Memory-Based Learning,
Mach. Learn., 17, 143–167, https://doi.org/10.1023/A:1022667616941, 1994. a, b
Liu, L., Ji, M., and Buchroithner, M.: Transfer Learning for Soil
Spectroscopy Based on Convolutional Neural Networks and Its
Application in Soil Clay Content Mapping Using Hyperspectral Imagery,
Sensors, 18, 3169, https://doi.org/10.3390/s18093169, 2018. a
Lobsey, C. R., Viscarra Rossel, R. A., Roudier, P., and Hedley, C. B.:
Rs-Local Data-Mines Information from Spectral Libraries to Improve
Local Calibrations: Rs-Local Improves Local Spectroscopic
Calibrations, Eur. J. Soil Sci., 68, 840–852, https://doi.org/10.1111/ejss.12490,
2017. a, b, c, d, e, f, g
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
Meuli, R. G., Wächter, D., Schwab, P., Kohli, L., and Zimmermann, R.:
Connecting biodiversity monitoring with soil inventory data – a Swiss case study, BGS Bulletin, 38, 65–69, 2017. a
Miller, B. A., Koszinski, S., Wehrhan, M., and Sommer, M.: Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks, SOIL, 1, 217–233, https://doi.org/10.5194/soil-1-217-2015, 2015. a
Nguyen, T., Janik, L., and Raupach, M.: Diffuse Reflectance Infrared Fourier
Transform (DRIFT) Spectroscopy in Soil Studies, Soil Res., 29, 49,
https://doi.org/10.1071/SR9910049, 1991. a
Nocita, M., Stevens, A., van Wesemael, B., Brown, D. J., Shepherd, K. D.,
Towett, E., Vargas, R., and Montanarella, L.: Soil Spectroscopy: An
Opportunity to Be Seized, Glob. Change Biol., 21, 10–11,
https://doi.org/10.1111/gcb.12632, 2015. a
Ogen, Y., Zaluda, J., Francos, N., Goldshleger, N., and Ben-Dor, E.:
Cluster-Based Spectral Models for a Robust Assessment of Soil Properties,
Geoderma, 340, 175–184, https://doi.org/10.1016/j.geoderma.2019.01.022, 2019. a
Padarian, J., Minasny, B., and McBratney, A. B.: Transfer Learning to Localise a Continental Soil Vis-NIR Calibration Model, Geoderma, 340, 279–288, https://doi.org/10.1016/j.geoderma.2019.01.009, 2019a. a, b, c
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, d
Pan, S. J. and Yang, Q.: A Survey on Transfer Learning, IEEE
T. Knowl. Data En., 22, 1345–1359,
https://doi.org/10.1109/TKDE.2009.191, 2010. a, b
Parikh, S. J., Goyne, K. W., Margenot, A. J., Mukome, F. N. D., and
Calderón, F. J.: Chapter One – Soil Chemical Insights Provided
through Vibrational Spectroscopy, in: Advances in Agronomy, edited by: Sparks, D. L., Academic Press, vol. 126, 1–148,
https://doi.org/10.1016/B978-0-12-800132-5.00001-8, 2014. a
Peng, Y., Xiong, X., Adhikari, K., Knadel, M., Grunwald, S., and Greve, M. H.: Modeling Soil Organic Carbon at Regional Scale by Combining
Multi-Spectral Images with Laboratory Spectra, 10, e0142295,
https://doi.org/10.1371/journal.pone.0142295, 2015. a
Pratt, L. and Thrun, S.: Guest Editors' Introduction, Mach. Learn., 28, 5–5, https://doi.org/10.1023/A:1007322005825, 1997. a
Pratt, L. Y., Pratt, L. Y., Hanson, S. J., Giles, C. L., and Cowan, J. D.:
Discriminability-Based Transfer between Neural Networks, Adv. Neur. In., 5, 204–211, 1993. a
Quinlan, J.: Combining Instance-Based and Model-Based Learning,
Machine Learning Proceedings, 1993, 236–243,
https://doi.org/10.1016/B978-1-55860-307-3.50037-X, 1993. a
Quinlan, J. R.: Learning with Continuous Classes, Proceedings of Australian Joint Conference on Artificial Intelligence, 16–18 November 1992, Hobart, Australia, 343–348, 1992. a
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria,
available at: https://www.R-project.org/ (last access: 16 August 2021), 2019. a
Ramirez-Lopez, L., Behrens, T., Schmidt, K., Stevens, A., Demattê, J. A. M.,
and Scholten, T.: The Spectrum-Based Learner: A New Local Approach for
Modeling Soil Vis–NIR Spectra of Complex Datasets, Geoderma, 195–196,
268–279, https://doi.org/10.1016/j.geoderma.2012.12.014, 2013. a, b, c, d, e, f, g
Rehbein, K., Sprecher, C., and Keller, A.: Übersicht Stand
Bodenkartierung in Der Schweiz. Ergänzung Des
Bodenkartierungskataloges Schweiz Um Bodeninformationen Aus
Meliorationsprojekten, Servicestelle NABODAT, Agroscope, Zürich, Switzerland,
2020. a
Rousseeuw, P. J.: Least Median of Squares Regression, J. Am. Stat. Assoc., 79, 871–880, https://doi.org/10.1080/01621459.1984.10477105, 1984. a
Savitzky, A. and Golay, M. J. E.: Smoothing and Differentiation of Data by Simplified Least Squares Procedures, Anal. Chem., 36,
1627–1639, https://doi.org/10.1021/ac60214a047, 1964. 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
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, b, c, d
Skjemstad, J. and Dalal, R.: Spectroscopic and Chemical Differences in Organic Matter of Two Vertisols Subjected to Long Periods of Cultivation, Soil Res., 25, 323, https://doi.org/10.1071/SR9870323, 1987. a
Solomatine, D.: Combining Machine Learning and Domain Knowledge in
Modular Modelling, in: Practical Hydroinformatics: Computational
Intelligence and Technological Developments in Water Applications,
edited by: Abrahart, R. J., See, L. M., and Solomatine, D. P., Water Science and Technology Library, Springer, Berlin, Heidelberg, 333–345,
https://doi.org/10.1007/978-3-540-79881-1_24, 2008. a
Soriano-Disla, J. M., Janik, L. J., Viscarra Rossel, R. A., Macdonald, L. M., and 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. a
Stanfill, C. and Waltz, D.: Toward Memory-Based Reasoning, Commun. ACM, 29, 1213–1228, https://doi.org/10.1145/7902.7906, 1986. a
Stenberg, B. and Rossel, R. V.: Diffuse Reflectance Spectroscopy for
High-Resolution Soil Sensing, in: Proximal Soil Sensing, edited
by: Viscarra Rossel, R. A., McBratney, A. B., and Minasny, B., Progress in
Soil Science, Springer Netherlands, Dordrech, the Netherlands, 29–47,
https://doi.org/10.1007/978-90-481-8859-8_3, 2010. a, b
Stevens, A. and Ramirez-Lopez, L.: An introduction to the prospectr package, r package version 0.1.3, available at: https://cran.r-project.org/web/packages/prospectr/vignettes/prospectr.html (last access: 16 August 2021), 2013. 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
Thrun, S. and Pratt, L. (Eds.): Learning to Learn, Springer US, Boston, MA, https://doi.org/10.1007/978-1-4615-5529-2, 1998. a
Tsakiridis, N. L., Keramaris, K. D., Theocharis, J. B., and Zalidis, G. C.:
Simultaneous prediction of soil properties from VNIR-SWIR spectra using a
localized multi-channel 1-D convolutional neural network, Geoderma, 367,
114208, https://doi.org/10.1016/j.geoderma.2020.114208, 2020. a, b, c, d
Tziolas, N., Tsakiridis, N., Ben-Dor, E., Theocharis, J., and Zalidis, G.: A
Memory-Based Learning Approach Utilizing Combined Spectral Sources and
Geographical Proximity for Improved VIS-NIR-SWIR Soil Properties
Estimation, Geoderma, 340, 11–24, https://doi.org/10.1016/j.geoderma.2018.12.044,
2019. a
Varmuza, K. and Filzmoser, P.: Introduction to Multivariate Statistical
Analysis in Chemometrics, CRC Press, https://doi.org/10.1201/9781420059496,
2016. 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,
2016. a, b, c
Viscarra Rossel, R. A. and McBratney, A. B.: Soil chemical analytical accuracy and costs: implications from precision agriculture, Aust. J. Exp. Agr., 38, 765, https://doi.org/10.1071/EA97158, 1998. a, b
Viscarra Rossel, R. A., Lobsey, C. R., Sharman, C., Flick, P., and 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. a
Wang, Y. and Witten, I. H.: Induction of model trees for predicting continuous classes, Working Paper 96/23, University of Waikato, Department of Computer Science, Hamilton, New Zealand, available at: https://researchcommons.waikato.ac.nz/handle/10289/1183 (last access: 16 August 2021), 1996. a
Wickham, H.: tidyverse: Easily Install and Load the “Tidyverse”, r package
version 1.3.0, available at: https://CRAN.R-project.org/package=tidyverse (last access: 16 August 2021), 2019. 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., Springer Berlin Heidelberg, vol. 973, 286–293, https://doi.org/10.1007/BFb0062108, 1983. a
Wolpert, D. and Macready, W.: No Free Lunch Theorems for Optimization, IEEE
T. Evolut. Comput., 1, 67–82, https://doi.org/10.1109/4235.585893, 1997. a
Wolpert, D. H.: The Lack of A Priori Distinctions Between Learning
Algorithms, Neural Comput., 8, 1341–1390,
https://doi.org/10.1162/neco.1996.8.7.1341, 1996. a
Short summary
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.
We developed the Swiss mid-infrared spectral library and a statistical model collection across...
