Articles | Volume 18, issue 1
https://doi.org/10.5194/essd-18-219-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-219-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
08 Jan 2026
Data description paper |
| 08 Jan 2026
| 08 Jan 2026
Austrian NIR soil spectral library for soil health assessments
Julia Fohrafellner
CORRESPONDING AUTHOR
Department for Soil Health and Plant Nutrition, Austrian Agency for Health and Food Safety (AGES), Vienna, 1220, Austria
Maximilian Lippl
Department for Feed Analysis and Quality Testing, Austrian Agency for Health and Food Safety (AGES), Vienna, 1220, Austria
Armin Bajraktarevic
Department for Soil Health and Plant Nutrition, Austrian Agency for Health and Food Safety (AGES), Vienna, 1220, Austria
Andreas Baumgarten
Department for Soil Health and Plant Nutrition, Austrian Agency for Health and Food Safety (AGES), Vienna, 1220, Austria
Heide Spiegel
Department for Soil Health and Plant Nutrition, Austrian Agency for Health and Food Safety (AGES), Vienna, 1220, Austria
Robert Körner
Department for Soil Health and Plant Nutrition, Austrian Agency for Health and Food Safety (AGES), Vienna, 1220, Austria
Taru Sandén
Department for Soil Health and Plant Nutrition, Austrian Agency for Health and Food Safety (AGES), Vienna, 1220, Austria
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Cited articles
AGES soil box: https://www.ages.at/en/environment/soil/soil-survey#c6108, last access: 18 July 2025.
Barnes, R. J., Dhanoa, M. S., and Lister, S. J.: Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra, Appl. Spectrosc., 43, 772–777, https://doi.org/10.1366/0003702894202201, 1989.
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 in Analytical Chemistry, 29, 1073–1081, https://doi.org/10.1016/j.trac.2010.05.006, 2010.
Bieber, M.: VisNIR spectroscopy of agricultural soils for assessing carbon fractions and management practices, Master Thesis, BOKU University, Vienna, https://resolver.obvsg.at/urn:nbn:at:at-ubbw:1-26536 (last access: 19 December 2025), 2023.
Chang, C.-W., Laird, D. A., Mausbach, M. J., and Hurburgh, C. R.: Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties, Soil Science Society of America Journal, 65, 480–490, https://doi.org/10.2136/sssaj2001.652480x, 2001.
Cornu, S., Keesstra, S., Bispo, A., Fantappie, M., van Egmond, F., Smreczak, B., Wawer, R., Pavlů, L., Sobocká, J., Bakacsi, Z., Farkas-Iványi, K., Molnár, S., Møller, A. B., Madenoglu, S., Feiziene, D., Oorts, K., Schneider, F., da Conceição Gonçalves, M., Mano, R., Garland, G., Skalský, R., O'Sullivan, L., Kasparinskis, R., and Chenu, C.: National soil data in EU countries, where do we stand?, Eur. J. Soil Sci., 74, https://doi.org/10.1111/ejss.13398, 2023.
Duckworth, J.: Mathematical data preprocessing, in: Near-Infrared Spectroscopy in Agriculture, edited by: Roberts, C., Workman, J., and Reeves, J., ASA-CSSA-SSSA, Madison, WI., 115–132, https://doi.org/10.2134/agronmonogr44, 2004.
European Commission: EU Soil Strategy for 2030 – Reaping the benefits of healthy soils for people, food, nature and climate, COM/2021/699 final, https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=COM:2021:699:FIN (last access: 19 December 2025), 2021.
European Commission: Proposal for a directive of the European parliament and of the council on soil monitoring and resilience (Soil Monitoring Law), COM/2023/416 final, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52023PC0416 (last access: 19 December 2025), 2023.
European Commission, Directorate General for Research Innovation, Veerman, C., Pinto Correia, T., Bastioli, C., Biro, B., Bouma, J., Cienciala, E., Emmett, B., Frison, E., Grand, A., Hristov, L., Kriaučiūnienė, Z., Pogrzeba, M., Soussana, J., Vela, C., and Wittkowski, R.: Caring for soil is caring for life – Ensure 75 % of soils are healthy by 2030 for healthy food, people, nature and climate – Interim report of the mission board for soil health and food, Publications Office, https://doi.org/10.2777/918775, 2020.
FAO: A primer on soil analysis using visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy, Rome, https://doi.org/10.4060/cb9005en, 2022.
Fernandez Ugalde, O., Scarpa, S., Orgiazzi, A., Panagos, P., Van Liedekerke, M., Marechal, A., and Jones, A.: LUCAS 2018 Soil Module. Presentation of dataset and results, Luxembourg, https://doi.org/10.2760/215013, 2022.
Feudale, R. N., Woody, N. A., Tan, H., Myles, A. J., Brown, S. D., and Ferré, J.: Transfer of multivariate calibration models: a review, Chemometr. Intell. Lab., 64, 181–192, https://doi.org/10.1016/S0169-7439(02)00085-0, 2002.
Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., Körner, R., and Sandén, T.: Dataset to: Austrian NIR Soil Spectral Library for Soil Health Assessments (Version 4), Zenodo [data set], https://doi.org/10.5281/zenodo.15772618, 2025.
Francos, N., Heller-Pearlshtien, D., Demattê, J. A. M., Van Wesemael, B., Milewski, R., Chabrillat, S., Tziolas, N., Sanz Diaz, A., Yagüe Ballester, M. J., Gholizadeh, A., Ben-Dor, E., and Lisetskii, F.: A Spectral Transfer Function to Harmonize Existing Soil Spectral Libraries Generated by Different Protocols, Appl. Environ. Soil Sci., 2023, 1–17, https://doi.org/10.1155/2023/4155390, 2023.
Gholizadeh, A., Boruvka, L., Saberioon, M., and 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.
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.
Jaconi, A., Vos, C., and 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, 2019a.
Jaconi, A., Poeplau, C., Ramirez-Lopez, L., Van Wesemael, B., and Don, A.: Log-ratio transformation is the key to determining soil organic carbon fractions with near-infrared spectroscopy, Eur. J. Soil Sci., 70, 127–139, https://doi.org/10.1111/ejss.12761, 2019b.
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.
Lehmann, J., Bossio, D. A., Kogel-Knabner, I., and Rillig, M. C.: The concept and future prospects of soil health, Nat. Rev. Earth Environ., 1, 544–553, https://doi.org/10.1038/s43017-020-0080-8, 2020.
Ludwig, B., Murugan, R., Parama, V. R. R., and Vohland, M.: Accuracy of Estimating Soil Properties with Mid-Infrared Spectroscopy: Implications of Different Chemometric Approaches and Software Packages Related to Calibration Sample Size, Soil Science Society of America Journal, 83, 1542–1552, https://doi.org/10.2136/sssaj2018.11.0413, 2019.
Ludwig, B., Greenberg, I., Vohland, M., and Michel, K.: Optimised use of data fusion and memory-based learning with an Austrian soil library for predictions with infrared data, Eur. J. Soil Sci., 74, https://doi.org/10.1111/ejss.13394, 2023.
Ma, Y., Roudier, P., Kumar, K., Palmada, T., Grealish, G., Carrick, S., Lilburne, L., and Triantafilis, J.: A soil spectral library of New Zealand, Geoderma Reg., 35, https://doi.org/10.1016/j.geodrs.2023.e00726, 2023.
Matson, A., Fantappie, M., Campbell, G. A., Miranda-Velez, J. F., Faber, J. H., Gomes, L. C., Hessel, R., Lana, M., Mocali, S., Smith, P., Robinson, D. A., Bispo, A., van Egmond, F., Keesstra, S., Saby, N. P. A., Smreczak, B., Froger, C., Suleymanov, A., and Chenu, C.: Four approaches to setting soil health targets and thresholds in agricultural soils, J. Environ. Manage., 371, 123141, https://doi.org/10.1016/j.jenvman.2024.123141, 2024.
Metzger, M. J.: The Environmental Stratification of Europe, Datashare [data set], https://doi.org/10.7488/ds/2356, 2018.
Metzger, M. J., Bunce, R. G. H., Jongman, R. H. G., Mücher, C. A., and Watkins, J. W.: A climatic stratification of the environment of Europe, Global Ecol. Biogeogr., 14, 549–563, https://doi.org/10.1111/j.1466-822X.2005.00190.x, 2005.
Minasny, B. and McBratney, A. B.: Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy, Chemometr. Intell. Lab., 94, 72–79, https://doi.org/10.1016/j.chemolab.2008.06.003, 2008.
Minasny, B., Bandai, T., Ghezzehei, T. A., Huang, Y.-C., Ma, Y., McBratney, A. B., Ng, W., Norouzi, S., Padarian, J., Rudiyanto, Sharififar, A., Styc, Q., and Widyastuti, M.: Soil Science-Informed Machine Learning, Geoderma, 452, https://doi.org/10.1016/j.geoderma.2024.117094, 2024.
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. M., 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. Agron., 139–159, https://doi.org/10.1016/bs.agron.2015.02.002, 2015.
Park, S., Jeon, S., Kwon, N.-H., Kwon, M., Shin, J.-H., Kim, W.-C., and Lee, J. G.: Application of near-infrared spectroscopy to predict chemical properties in clay rich soil: A review, Eur. J. Agron., 159, https://doi.org/10.1016/j.eja.2024.127228, 2024.
Peng, Y., Ben-Dor, E., Biswas, A., Chabrillat, S., Demattê, J. A. M., Ge, Y., Gholizadeh, A., Gomez, C., Guerrero, C., Herrick, J., Maynard, J. J., Mouazen, A. M., Ma, Y., McBratney, A. B., Minasny, B., Ramirez-Lopez, L., Robertson, A. H. J., Viscarra Rossel, R. A., Shi, Z., Stenberg, B., Wadoux, A. M. J. C., Winowiecki, L. A., and Zhang, G.: Spectroscopic solutions for generating new global soil information, Innov., 6, 100839, https://doi.org/10.1016/j.xinn.2025.100839, 2025.
Recena, R., Fernández-Cabanás, V. M., and Delgado, A.: Soil fertility assessment by Vis-NIR spectroscopy: Predicting soil functioning rather than availability indices, Geoderma, 337, 368–374, https://doi.org/10.1016/j.geoderma.2018.09.049, 2019.
Rinot, O., Levy, G. J., Steinberger, Y., Svoray, T., and Eshel, G.: Soil health assessment: A critical review of current methodologies and a proposed new approach, Sci. Total Environ., 648, 1484–1491, https://doi.org/10.1016/j.scitotenv.2018.08.259, 2019.
Safanelli, J. L., Hengl, T., Parente, L. L., Minarik, R., Bloom, D. E., Todd-Brown, K., Gholizadeh, A., Mendes, W. S., and Sanderman, J.: Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagement, PLoS One, 20, e0296545, https://doi.org/10.1371/journal.pone.0296545, 2025.
Shen, R. F. and Teng, Y.: The frontier of soil science: Soil health, Pedosphere, 33, 6–7, https://doi.org/10.1016/j.pedsph.2022.06.007, 2023.
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, 2013.
Stenberg, B., Viscarra Rossel, R. A., Mouazen, A. M., and Wetterlind, J.: Visible and Near Infrared Spectroscopy in Soil Science, Adv. Agron., 163–215, https://doi.org/10.1016/s0065-2113(10)07005-7, 2010.
Stevens, A. and Ramirez Lopez, L.: An introduction to the prospectr package. Documentation for package “prospectr” version 0.2.7, https://search.r-project.org/CRAN/refmans/prospectr/html/00Index.html (last access: 19 December 2025), 2024.
Stevens, A. and Ramirez-Lopez, L.: An introduction to the prospectr package. R package Vignette, R package version 0.2.8., https://cran.r-project.org/web/packages/prospectr/vignettes/prospectr.html (last access: 19 December 2025), 2025.
Tatzber, M., Schlatter, N., Baumgarten, A., Dersch, G., Körner, R., Lehtinen, T., Unger, G., Mifek, E., and Spiegel, H.: KMnO4 determination of active carbon for laboratory routines: three long-term field experiments in Austria, Soil Res., 53, https://doi.org/10.1071/sr14200, 2015.
Tavakoli, H., Correa, J., Sabetizade, M., and Vogel, S.: Predicting key soil properties from Vis-NIR spectra by applying dual-wavelength indices transformations and stacking machine learning approaches, Soil Till. Res., 229, 105684, https://doi.org/10.1016/j.still.2023.105684, 2023.
VDLUFA: Methodenbuch Band 1: Die Untersuchung von Böden, ISBN: 3922712592, 1991.
Viscarra Rossel, R. A., Walvoort, D. J. J., McBratney, A. B., Janik, L. J., and Skjemstad, J. O.: 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.
Viscarra Rossel, R. A., Chappell, A., De Caritat, P., and McKenzie, N. J.: On the soil information content of visible–near infrared reflectance spectra, Eur. J. Soil Sci., 62, 442–453, https://doi.org/10.1111/j.1365-2389.2011.01372.x, 2011.
Viscarra Rossel, R. A., Behrens, T., Ben-Dor, E., Brown, D. J., Demattê, J. A. M., Shepherd, K. D., Shi, Z., Stenberg, B., Stevens, A., Adamchuk, V., Aïchi, H., Barthès, B. G., Bartholomeus, H. M., Bayer, A. D., Bernoux, M., Böttcher, K., Brodský, L., Du, C. W., Chappell, A., Fouad, Y., Genot, V., Gomez, C., Grunwald, S., Gubler, A., Guerrero, C., Hedley, C. B., Knadel, M., Morrás, H. J. M., Nocita, M., Ramirez-Lopez, L., Roudier, P., Campos, E. M. R., Sanborn, P., Sellitto, V. M., Sudduth, K. A., Rawlins, B. G., Walter, C., Winowiecki, L. A., Hong, S. Y., 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.
Viscarra Rossel, R. A., Behrens, T., Ben-Dor, E., Chabrillat, S., Demattê, J. A. M., Ge, Y., Gomez, C., Guerrero, C., Peng, Y., Ramirez-Lopez, L., Shi, Z., Stenberg, B., Webster, R., Winowiecki, L., and Shen, Z.: Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century, Eur. J. Soil Sci., 73, e13271, https://doi.org/10.1111/ejss.13271, 2022.
Wade, J., Culman, S. W., Gasch, C. K., Lazcano, C., Maltais-Landry, G., Margenot, A. J., Martin, T. K., Potter, T. S., Roper, W. R., Ruark, M. D., Sprunger, C. D., and Wallenstein, M. D.: Rigorous, empirical, and quantitative: a proposed pipeline for soil health assessments, Soil Biol. Biochem., 170, https://doi.org/10.1016/j.soilbio.2022.108710, 2022.
Short summary
The first openly accessible Austrian near-infrared (NIR) Soil Spectral Library was developed, including over 2100 samples covering all Austrian environmental zones. At present, the predictive accuracy for most properties was insufficient compared to routine laboratory analyses; however, several key properties showed predictive potential. We encourage using the open Library as a foundation for further spectral analysis and modelling and we support future soil health assessments via spectroscopy.
The first openly accessible Austrian near-infrared (NIR) Soil Spectral Library was developed,...
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