Soil spectroscopy is a widely used method for estimating soil properties that are important to environmental and agricultural monitoring. However, a bottleneck to its more widespread adoption is the need for establishing large reference datasets for training machine learning (ML) models, which are called soil spectral libraries (SSLs). Similarly, the prediction capacity of new samples is also subject to the number and diversity of soil types and conditions represented in the SSLs. To help bridge this gap and enable hundreds of stakeholders to collect more affordable soil data by leveraging a centralized open resource, the Soil Spectroscopy for Global Good initiative has created the Open Soil Spectral Library (OSSL). In this paper, we describe the procedures for collecting and harmonizing several SSLs that are incorporated into the OSSL, followed by exploratory analysis and predictive modeling. The results of 10-fold cross-validation with refitting show that, in general, mid-infrared (MIR)-based models are significantly more accurate than visible and near-infrared (VisNIR) or near-infrared (NIR) models. From independent model evaluation, we found that Cubist comes out as the best-performing ML algorithm for the calibration and delivery of reliable outputs (prediction uncertainty and representation flag). Although many soil properties are well predicted, total sulfur, extractable sodium, and electrical conductivity performed poorly in all spectral regions, with some other extractable nutrients and physical soil properties also performing poorly in one or two spectral regions (VisNIR or NIR). Hence, the use of predictive models based solely on spectral variations has limitations. This study also presents and discusses several other open resources that were developed from the OSSL, aspects of opening data, current limitations, and future development. With this genuinely open science project, we hope that OSSL becomes a driver of the soil spectroscopy community to accelerate the pace of scientific discovery and innovation.

Czech University of Life Sciences Prague Prague Czech Republic

OpenGeoHub foundation Wageningen the Netherlands

The Food and Agriculture Organization of the United Nations Rome Italy

University of Florida Gainesville FL United States of America

Woodwell Climate Research Center Falmouth MA United States of America

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Tóth G, Hermann T, da Silva MR, Montanarella L. Monitoring soil for sustainable development and land degradation neutrality. Environmental Monitoring and Assessment. 2018;190(2). doi: 10.1007/s10661-017-6415-3 PubMed DOI PMC

McBratney A, Field DJ, Koch A. The dimensions of soil security. Geoderma. 2014;213:203–213. doi: 10.1016/j.geoderma.2013.08.013 DOI

Bradford MA, Carey CJ, Atwood L, Bossio D, Fenichel EP, Gennet S, et al.. Soil carbon science for policy and practice. Nature Sustainability. 2019;2(12):1070–1072. doi: 10.1038/s41893-019-0431-y DOI

Nature editors. Safeguarding our soils. Nature Communications. 2017;8(1). doi: 10.1038/s41467-017-02070-6 PubMed DOI PMC

Rojas RV, Achouri M, Maroulis J, Caon L. Healthy soils: a prerequisite for sustainable food security. Environmental Earth Sciences. 2016;75(3). doi: 10.1007/s12665-015-5099-7 DOI

Shepherd KD, Ferguson R, Hoover D, van Egmond F, Sanderman J, Ge Y. A global soil spectral calibration library and estimation service. Soil Security. 2022;7:100061. doi: 10.1016/j.soisec.2022.100061 DOI

Wadoux AMJC, Heuvelink GBM, Lark RM, Lagacherie P, Bouma J, Mulder VL, et al.. Ten challenges for the future of pedometrics. Geoderma. 2021;401:115155. doi: 10.1016/j.geoderma.2021.115155 DOI

Hendriks CMJ, Stoorvogel JJ, Lutz F, Claessens L. When can legacy soil data be used, and when should new data be collected instead? Geoderma. 2019;348:181–188. doi: 10.1016/j.geoderma.2019.04.026 DOI

Viscarra-Rossel RA, Behrens T, Ben-Dor E, Chabrillat S, Demattê JAM, Ge Y, et al.. Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century. European Journal of Soil Science. 2022;73(4). doi: 10.1111/ejss.13271 DOI

Li S, Viscarra Rossel RA, Webster R. The cost-effectiveness of reflectance spectroscopy for estimating soil organic carbon. European Journal of Soil Science. 2021;73(1). doi: 10.1111/ejss.13202 DOI

Ben-Dor E, Banin A. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Science Society of America Journal. 1995;59(2):364–372. doi: 10.2136/sssaj1995.03615995005900020014x DOI

Nocita M, Stevens A, van Wesemael B, Aitkenhead M, Bachmann M, Barthès B, et al.. Soil spectroscopy: an alternative to wet chemistry for soil monitoring. Advances in agronomy. 2015;132:139–159. doi: 10.1016/bs.agron.2015.02.002 DOI

Ben-Dor E, Irons J, Epema G. Soil reflectance. In: Rencz AN, Ryerson RA, editors. Remote Sensing for the Earth Sciences: Manual of Remote Sensing. vol. 3. John Wiley and Sons; 1999. p. 111–188.

Viscarra-Rossel R, Behrens T, Ben-Dor E, Brown D, Demattê J, Shepherd KD, et al.. A global spectral library to characterize the world’s soil. Earth-Science Reviews. 2016;155:198–230. doi: 10.1016/j.earscirev.2016.01.012 DOI

Soriano-Disla JM, Janik LJ, Viscarra Rossel RA, Macdonald LM, McLaughlin MJ. The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties. Applied Spectroscopy Reviews. 2013;49(2):139–186. doi: 10.1080/05704928.2013.811081 DOI

Mastorakis G. Human-like machine learning: limitations and suggestions. CoRR. 2018;abs/1811.06052.

Demattê JA, Paiva AFdS, Poppiel RR, Rosin NA, Ruiz LFC, Mello FAdO, et al.. The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication. Remote Sensing. 2022;14(3):740. doi: 10.3390/rs14030740 DOI

Burgelman JC, Pascu C, Szkuta K, Von Schomberg R, Karalopoulos A, Repanas K, et al.. Open Science, Open Data, and Open Scholarship: European Policies to Make Science Fit for the Twenty-First Century. Frontiers in Big Data. 2019;2. doi: 10.3389/fdata.2019.00043 PubMed DOI PMC

McKiernan EC, Bourne PE, Brown CT, Buck S, Kenall A, Lin J, et al.. How open science helps researchers succeed. eLife. 2016;5. doi: 10.7554/eLife.16800 PubMed DOI PMC

Soil Spectroscopy for Global Good. Soil Spectroscopy website; 2024. Available from: https://soilspectroscopy.org/.

Soil Spectroscopy for Global Good. Open Soil Spectral Library; 2024. Available from: https://soilspectroscopy.github.io/ossl-manual/.

Safanelli JL, Sanderman J, Bloom D, Todd-Brown K, Parente LL, Hengl T, et al.. An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter. Geoderma. 2023;440:116724. doi: 10.1016/j.geoderma.2023.116724 DOI

Garrity D, Bindraban P. A globally distributed soil spectral library visible near infrared diffuse reflectance spectra. Nairobi, Kenya: ICRAF (World Agroforestry Centre) / ISRIC (World Soil Information) Spectral Library; 2004. Available from: 10.34725/DVN/MFHA9C. DOI

Soil Spectroscopy for Global Good. Soil Spectroscopy GitHub repositories; 2024. Available from: https://github.com/soilspectroscopy.

United States Department of Agriculture, National Cooperative Soil Survey. National Cooperative Soil Survey Lab Data Mart (Lab Data); 2023. Available from: https://ncsslabdatamart.sc.egov.usda.gov/querypage.aspx.

Seybold CA, Ferguson R, Wysocki D, Bailey S, Anderson J, Nester B, et al.. Application of Mid-Infrared Spectroscopy in Soil Survey. Soil Science Society of America Journal. 2019;83(6):1746–1759. doi: 10.2136/sssaj2019.06.0205 DOI

Wijewardane NK, Ge Y, Wills S, Libohova Z. Predicting physical and chemical properties of US soils with a mid-infrared reflectance spectral library. Soil Science Society of America Journal. 2018;82(3):722–731. doi: 10.2136/sssaj2017.10.0361 DOI

Wills S, Loecke T, Sequeira C, Teachman G, Grunwald S, West LT. Overview of the U.S. Rapid Carbon Assessment Project: Sampling Design, Initial Summary and Uncertainty Estimates. In: Hartemink A, McSweeney K, editors. Soil Carbon. Progress in Soil Science. Springer International Publishing; 2014. p. 95–104. Available from: 10.1007/978-3-319-04084-4_10. DOI

Wijewardane NK, Ge Y, Wills S, Loecke T. Prediction of Soil Carbon in the Conterminous United States: Visible and Near Infrared Reflectance Spectroscopy Analysis of the Rapid Carbon Assessment Project. Soil Science Society of America Journal. 2016;80(4):973–982. doi: 10.2136/sssaj2016.02.0052 DOI

World Agroforestry (ICRAF), International Soil Reference and Information Centre (ISRIC). ICRAF-ISRIC Soil Spectral Library; 2021. Available from: https://data.worldagroforestry.org/citation?persistentId=doi:10.34725/DVN/MFHA9C. DOI

Vagen TG, Winowiecki LA, Desta L, Tondoh EJ, Weullow E, Shepherd K, et al. Mid-Infrared Spectra (MIRS) from ICRAF Soil and Plant Spectroscopy Laboratory: Africa Soil Information Service (AfSIS) Phase I 2009-2013; 2020. Available from: 10.34725/DVN/QXCWP1. DOI

Hengl T, Miller MA, Križan J, Shepherd KD, Sila A, Kilibarda M, et al.. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Scientific Reports. 2021;11(1):1–18. doi: 10.1038/s41598-021-85639-y PubMed DOI PMC

Jones A, Fernandez-Ugalde O, Scarpa S. LUCAS 2015 topsoil survey: presentation of dataset and results. European Commission. Joint Research Centre. Publications Office; 2020. Available from: https://data.europa.eu/doi/10.2760/616084. DOI

Summerauer L, Baumann P, Ramirez-Lopez L, Barthel M, Bauters M, Bukombe B, et al.. The central African soil spectral library: a new soil infrared repository and a geographical prediction analysis. SOIL. 2021;7(2):693–715. doi: 10.5194/soil-7-693-2021 DOI

Garrett LG, Sanderman J, Palmer DJ, Dean F, Patel S, Bridson JH, et al.. Mid-infrared spectroscopy for planted forest soil and foliage nutrition predictions, New Zealand case study. Trees, Forests and People. 2022;8:100280. doi: 10.1016/j.tfp.2022.100280 DOI

Schiedung M, Bellè SL, Malhotra A, Abiven S. Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada. CATENA. 2022;213:106194. doi: 10.1016/j.catena.2022.106194 DOI

Jović B, Ćirić V, Kovačević M, Šeremešić S, Kordić B. Empirical equation for preliminary assessment of soil texture. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2019;206:506–511. doi: 10.1016/j.saa.2018.08.039 PubMed DOI

Sanderman J, Smith C, Safanelli JL, Mitu SM, Ge Y, Murad O, et al.. Near-infrared (NIR) soil spectral library using the NeoSpectra Handheld NIR Analyzer by Si-Ware; 2023. Available from: https://zenodo.org/record/7600137.

Francos N, Gholizadeh A, Demattê JAM, Ben-Dor E. Effect of the internal soil standard on the spectral assessment of clay content. Geoderma. 2022;420:115873. doi: 10.1016/j.geoderma.2022.115873 DOI

Cools N, Delanote V, Scheldeman X, Quataert P, Vos BD, Roskams P. Quality assurance and quality control in forest soil analyses: a comparison between European soil laboratories. Accreditation and Quality Assurance. 2004;9:688–694. doi: 10.1007/s00769-004-0856-4 DOI

Pimstein A, Notesco G, Ben-Dor E. Performance of Three Identical Spectrometers in Retrieving Soil Reflectance under Laboratory Conditions. Soil Science Society of America Journal. 2011;75:746–759. doi: 10.2136/sssaj2010.0174 DOI

FAO Global Soil Partnership. Standard Operating Procedures (SOPs); 2023. Available from: https://www.fao.org/global-soil-partnership/glosolan-old/soil-analysis/standard-operating-procedures/en/.

Soil Survey Staff. Kellogg Soil Survey Laboratory methods manual. Soil Survey Investigations Report No. 42, Version 6.0. U.S. Department of Agriculture, Natural Resources Conservation Service.; 2022. Available from: https://www.nrcs.usda.gov/resources/guides-and-instructions/kssl-guidance.

ISRIC World Soil Information. International Soil Standards; 2023. Available from: https://www.isric.org/international-soil-standards.

Bispo A, Andersen L, Angers DA, Bernoux M, Brossard M, Cécillon L, et al.. Accounting for Carbon Stocks in Soils and Measuring GHGs Emission Fluxes from Soils: Do We Have the Necessary Standards? Frontiers in Environmental Science. 2017;5. doi: 10.3389/fenvs.2017.00041 DOI

Stevens A, Ramirez-Lopez L. An introduction to the prospectr package; 2022.

Lang M, Binder M, Richter J, Schratz P, Pfisterer F, Coors S, et al.. mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software. 2019. doi: 10.21105/joss.01903 DOI

Quinlan J. Learning with continuous classes. In: Proc. 5th Australian Joint Conference on Artificial Intelligence, Tasmania, 1992; 1992. p. 343–348.

Quinlan J. Combining instance-based and model-based learning. In: Proc. Tenth Int. Conference on Machine Learning; 1993. p. 236–243.

Dangal S, Sanderman J, Wills S, Ramirez-Lopez L. Accurate and Precise Prediction of Soil Properties from a Large Mid-Infrared Spectral Library. Soil Systems. 2019;3(1):11. doi: 10.3390/soilsystems3010011 DOI

Minasny B, McBratney AB. Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy. Chemometrics and Intelligent Laboratory Systems. 2008;94(1):72–79. doi: 10.1016/j.chemolab.2008.06.003 DOI

Barnes RJ, Dhanoa MS, Lister SJ. Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra. Applied Spectroscopy. 1989;43(5):772–777. doi: 10.1366/0003702894202201 DOI

Barra I, Haefele SM, Sakrabani R, Kebede F. Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances—A review. TrAC Trends in Analytical Chemistry. 2021;135:116166. doi: 10.1016/j.trac.2020.116166 DOI

Vestergaard RJ, Vasava H, Aspinall D, Chen S, Gillespie A, Adamchuk V, et al.. Evaluation of Optimized Preprocessing and Modeling Algorithms for Prediction of Soil Properties Using VIS-NIR Spectroscopy. Sensors. 2021;21(20):6745. doi: 10.3390/s21206745 PubMed DOI PMC

Yang L, Shami A. On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing. 2020;415:295–316. doi: 10.1016/j.neucom.2020.07.061 DOI

de Santana FB, Hall RL, Lowe V, Browne MA, Grunsky EC, Fitzsimons MM, et al.. A systematic approach to predicting and mapping soil particle size distribution from unknown samples using large mid-infrared spectral libraries covering large-scale heterogeneous areas. Geoderma. 2023;434:116491. doi: 10.1016/j.geoderma.2023.116491 DOI

Chang CW, Laird DA, Mausbach MJ, Hurburgh CR. Near-Infrared Reflectance Spectroscopy-Principal Components Regression Analyses of Soil Properties. Soil Science Society of America Journal. 2001;65(2):480–490. doi: 10.2136/sssaj2001.652480x DOI

Jackson JE, Mudholkar GS. Control Procedures for Residuals Associated With Principal Component Analysis. Technometrics. 1979;21(3):341–349. doi: 10.1080/00401706.1979.10489779 DOI

Laursen K, Rasmussen MA, Bro R. Comprehensive control charting applied to chromatography. Chemometrics and Intelligent Laboratory Systems. 2011;107(1):215–225. doi: 10.1016/j.chemolab.2011.04.002 DOI

Wadoux AMJC, Malone B, McBratney AB, Fajardo M, Minasny B. Soil Spectral Inference with R: Analysing Digital Soil Spectra Using the R Programming Environment. Progress in Soil Science. Springer International Publishing; 2021.

Norinder U, Carlsson L, Boyer S, Eklund M. Introducing Conformal Prediction in Predictive Modeling. A Transparent and Flexible Alternative to Applicability Domain Determination. Journal of Chemical Information and Modeling. 2014;54(6):1596–1603. doi: 10.1021/ci5001168 PubMed DOI

Cortés-Ciriano I, van Westen GJP, Bouvier G, Nilges M, Overington JP, Bender A, et al.. Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel. Bioinformatics. 2015;32(1):85–95. doi: 10.1093/bioinformatics/btv529 PubMed DOI PMC

Geladi P, Kowalski BR. Partial least-squares regression: a tutorial. Analytica Chimica Acta. 1986;185:1–17. doi: 10.1016/0003-2670(86)80028-9 DOI

Sanderman J, Savage K, Dangal SRS, Duran G, Rivard C, Cavigelli MA, et al.. Can Agricultural Management Induced Changes in Soil Organic Carbon Be Detected Using Mid-Infrared Spectroscopy? Remote Sensing. 2021;13(12):2265. doi: 10.3390/rs13122265 DOI

Soil Spectroscopy for Global Good. Open Soil Spectral Library (OSSL) Explorer application; 2024. Available from: https://explorer.soilspectroscopy.org/.

Soil Spectroscopy for Global Good. Open Soil Spectral Library (OSSL) Engine application; 2024. Available from: https://engine.soilspectroscopy.org/.

Stoner ER, Baumgardner M. Characteristic variations in reflectance of surface soils. Soil Science Society of America Journal. 1981;45(6):1161–1165. doi: 10.2136/sssaj1981.03615995004500060031x DOI

Margenot AJ, Calderón FJ, Goyne KW, Mukome FND, Parikh SJ. IR Spectroscopy, Soil Analysis Applications. In: Lindon JC, Tranter GE, Koppenaal DW, editors. Encyclopedia of Spectroscopy and Spectrometry. 3rd ed. Academic Press; 2017. p. 448–454. Available from: https://www.sciencedirect.com/science/article/pii/B9780124095472121705.

Viscarra-Rossel RA, Walvoort DJJ, McBratney AB, Janik LJ, Skjemstad JO. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma. 2006;131(1–2):59–75. doi: 10.1016/j.geoderma.2005.03.007 DOI

Ng W, Minasny B, Jeon SH, McBratney A. Mid-infrared spectroscopy for accurate measurement of an extensive set of soil properties for assessing soil functions. Soil Security. 2022;6:100043. doi: 10.1016/j.soisec.2022.100043 DOI

De Maesschalck R, Jouan-Rimbaud D, Massart DL. The Mahalanobis distance. Chemometrics and Intelligent Laboratory Systems. 2000;50(1):1–18. doi: 10.1016/S0169-7439(99)00047-7 DOI

Todd-Brown KEO, Abramoff RZ, Beem-Miller J, Blair HK, Earl S, Frederick KJ, et al.. Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential. Biogeosciences. 2022;19(14):3505–3522. doi: 10.5194/bg-19-3505-2022 DOI

GDAL/OGR contributors. GDAL/OGR Geospatial Data Abstraction Software Library; 2023. Available from: https://gdal.org.

Telenius A. Biodiversity information goes public: GBIF at your service. Nordic Journal of Botany. 2011;29(3):378–381. doi: 10.1111/j.1756-1051.2011.01167.x DOI

Karlen DL, Rice CW. Soil Degradation: Will Humankind Ever Learn? Sustainability. 2015;7(9):12490–12501. doi: 10.3390/su70912490 DOI

Gonzalez-Roglich M, Zvoleff A, Noon M, Liniger H, Fleiner R, Harari N, et al.. Synergizing global tools to monitor progress towards land degradation neutrality: Trends. Earth and the World Overview of Conservation Approaches and Technologies sustainable land management database. Environmental Science & Policy. 2019;93:34–42. doi: 10.1016/j.envsci.2018.12.019 DOI

Comino F, Aranda V, García-Ruiz R, Ayora-Cañada MJ, Domínguez-Vidal A. Infrared spectroscopy as a tool for the assessment of soil biological quality in agricultural soils under contrasting management practices. Ecological Indicators. 2018;87:117–126. doi: 10.1016/j.ecolind.2017.12.046 DOI

Hong Y, Sanderman J, Hengl T, Chen S, Wang N, Xue J, et al.. Potential of globally distributed topsoil mid-infrared spectral library for organic carbon estimation. CATENA. 2024;235:107628. doi: 10.1016/j.catena.2023.107628 DOI

Zhou Y, Chen S, Hu B, Ji W, Li S, Hong Y, et al.. Global soil salinity prediction by open soil Vis-NIR spectral library. Remote Sens (Basel). 2022;14(21):5627. doi: 10.3390/rs14215627 DOI

Hutengs C, Seidel M, Oertel F, Ludwig B, Vohland M. In situ and laboratory soil spectroscopy with portable visible-to-near-infrared and mid-infrared instruments for the assessment of organic carbon in soils. Geoderma. 2019;355:113900. doi: 10.1016/j.geoderma.2019.113900 DOI

Priori S, Mzid N, Pascucci S, Pignatti S, Casa R. Performance of a Portable FT-NIR MEMS Spectrometer to Predict Soil Features. Soil Systems. 2022;6(3). doi: 10.3390/soilsystems6030066 DOI

Fonseca AA, Pasquini C, Soares EMB. Large-scale measurement of soil organic carbon using compact near-infrared spectrophotometers: effect of soil sample preparation and the use of local modelling. Environ Sci: Adv. 2023;2:1372–1381.

Stevens A, van Wesemael B, Bartholomeus H, Rosillon D, Tychon B, Ben-Dor E. Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils. Geoderma. 2008;144(1–2):395–404. doi: 10.1016/j.geoderma.2007.12.009 DOI

Horta A, Malone B, Stockmann U, Minasny B, Bishop T, McBratney A, et al.. Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review. Geoderma. 2015;241:180–209. doi: 10.1016/j.geoderma.2014.11.024 DOI

Jia X, O’Connor D, Shi Z, Hou D. VIRS based detection in combination with machine learning for mapping soil pollution. Environmental Pollution. 2021;268:115845. doi: 10.1016/j.envpol.2020.115845 PubMed DOI

Vergopolan N, Chaney NW, Pan M, Sheffield J, Beck HE, Ferguson CR, et al.. SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US. Scientific data. 2021;8(1):264. doi: 10.1038/s41597-021-01050-2 PubMed DOI PMC

Karyotis K, Tsakiridis NL, Tziolas N, Samarinas N, Kalopesa E, Chatzimisios P, et al.. On-Site Soil Monitoring Using Photonics-Based Sensors and Historical Soil Spectral Libraries. Remote Sensing. 2023;15(6). doi: 10.3390/rs15061624 DOI

Murad M, Jones E, Minasny B, McBratney A, Wijewardane N, Ge Y. Assessing a VisNIR penetrometer system for in-situ estimation of soil organic carbon under variable soil moisture conditions. Biosystems Engineering. 2022;224:197–212. doi: 10.1016/j.biosystemseng.2022.10.011 DOI

Rizzo R, Wadoux AMJC, Demattê JAM, Minasny B, Barrón V, Ben-Dor E, et al.. Remote sensing of the Earth’s soil color in space and time. Remote Sensing of Environment. 2023;299:113845. doi: 10.1016/j.rse.2023.113845 DOI

Demattê JA, Safanelli JL, Poppiel RR, Rizzo R, Silvero NEQ, de Sousa Mendes W, et al.. Bare earth’s surface spectra as a proxy for soil resource monitoring. Scientific reports. 2020;10(1):1–11. doi: 10.1038/s41598-020-61408-1 PubMed DOI PMC

Luce MS, Ziadi N, Rossel RAV. GLOBAL-LOCAL: A new approach for local predictions of soil organic carbon content using large soil spectral libraries. Geoderma. 2022;425:116048. doi: 10.1016/j.geoderma.2022.116048 DOI

Ramirez-Lopez L, Behrens T, Schmidt K, Stevens A, Demattê JAM, Scholten T. The spectrum-based learner: A new local approach for modeling soil vis–NIR spectra of complex datasets. Geoderma. 2013;195–196:268–279. doi: 10.1016/j.geoderma.2012.12.014 DOI

Ng W, Minasny B, Jones E, McBratney A. To spike or to localize? Strategies to improve the prediction of local soil properties using regional spectral library. Geoderma. 2022;406:115501. doi: 10.1016/j.geoderma.2021.115501 DOI

Minarik R, Safanelli JL, Hengl T, Sanderman J. SS4GG hackathon: NIR soil spectroscopy modeling. Kaggle; 2023. Available from: https://www.kaggle.com/competitions/ss4gg-hackathon-nir-neospectra.