Articles | Volume 16, issue 5
https://doi.org/10.5194/essd-16-2367-2024
© Author(s) 2024. 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-16-2367-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 May 2024
Data description paper |
| 16 May 2024
| 16 May 2024
European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions
Songchao Chen
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311215, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
Zhongxing Chen
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311215, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
Xianglin Zhang
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311215, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
UMR ECOSYS, AgroParisTech, INRAE, Universiteé Paris-Saclay, Palaiseau 91120, France
Zhongkui Luo
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
Calogero Schillaci
European Commission, Joint Research Centre, Ispra, 21026, Italy
Dominique Arrouays
INRAE, Info&Sols, Orléans 45075, France
Anne Christine Richer-de-Forges
INRAE, Info&Sols, Orléans 45075, France
Zhou Shi
CORRESPONDING AUTHOR
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
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Cited articles
Abdelbaki, A. M.: Evaluation of pedotransfer functions for predicting soil bulk density for U. S. soils, Ain Shams Eng. J., 9, 1611–1619, https://doi.org/10.1016/j.asej.2016.12.002, 2018.
Adams, W. A.: The effect of organic matter on the bulk and true densities of some uncultivated podzolic soils, J. Soil Sci., 24, 10–17, 1973.
Armas, D., Guevara, M., Bezares, F., Vargas, R., Durante, P., Osorio, V., Jiménez, W., and Oyonarte, C.: Harmonized Soil Database of Ecuador (HESD): data from 2009 to 2015, Earth Syst. Sci. Data, 15, 431–445, https://doi.org/10.5194/essd-15-431-2023, 2023.
Atwood, T. B., Connolly, R. M., Almahasheer, H., Carnell, P. E., Duarte, C. M., Ewers Lewis, C. J., and Lovelock, C. E.: Global patterns in mangrove soil carbon stocks and losses, Nat. Clim. Change, 7, 523–528, https://doi.org/10.1038/nclimate3326, 2017.
Augusto, L. and Boča, A.: Tree functional traits, forest biomass, and tree species diversity interact with site properties to drive forest soil carbon, Nat. Commun., 13, 1097, https://doi.org/10.1038/s41467-022-28748-0, 2022.
Bates, D. M. and Watts, D. G.: Nonlinear regression analysis and its applications: Nonlinear regression analysis and its applications, Wiley Series in Probability and Statistics, John Wiley & Sons, Inc., New York, United States, https://doi.org/10.1002/9780470316757, 1988.
Batjes, N. H., Ribeiro, E., and van Oostrum, A.: Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019), Earth Syst. Sci. Data, 12, 299–320, https://doi.org/10.5194/essd-12-299-2020, 2020.
Benites, V. M., Machado, P. L. O. A., Fidalgo, E. C. C., Coelho, M. R., and Madari, B. E.: Pedotransfer functions for estimating soil bulk density from existing soil survey reports in Brazil, Geoderma, 139, 90–97, https://doi.org/10.1016/j.geoderma.2007.01.005, 2007.
Bondi, G., Creamer, R., Ferrari, A., Fenton, O., and Wall, D.: Using machine learning to predict soil bulk density on the basis of visual parameters: Tools for in-field and post-field evaluation, Geoderma, 318, 137–147, https://doi.org/10.1016/j.geoderma.2017.11.035, 2018.
Chen, S., Richer-de-Forges, A. C., Saby, N. P. A., Martin, M. P., Walter, C., and Arrouays, D.: Building a pedotransfer function for soil bulk density on regional dataset and testing its validity over a larger area, Geoderma, 312, 52–63, https://doi.org/10.1016/j.geoderma.2017.10.009, 2018.
Chen, S., Xu, H., Xu, D., Ji, W., Li, S., Yang, M., Hu, B., Zhou, Y., Wang, N., Arrouays, D., and Shi, Z.: Evaluating validation strategies on the performance of soil property prediction from regional to continental spectral data, Geoderma, 400, 115159, https://doi.org/10.1016/j.geoderma.2021.115159, 2021.
Chen, S., Arrouays, D., Mulder, V. L., Poggio, L., Minasny, B., Roudier, P., Libohova, Z., Lagacherie, P., Shi, Z., Hannam, J., Meersmans, J., Richer-de-Forges, A. C., and Walter, C.: Digital mapping of GlobalSoilMap soil properties at a broad scale: A review, Geoderma, 409, 115567, https://doi.org/10.1016/j.geoderma.2021.115567, 2022.
Chen, S., Chen, Z., Zhang, X., Luo, Z., Schillaci, C., Arrouays, D., Richer-de-Forges, A. C., and Shi, Z.: European soil bulk density and organic carbon stock database using LUCAS Soil 2018 [Data set], Zenodo, https://doi.org/10.5281/zenodo.10211884, 2023.
Chen, Z., Shuai, Q., Shi, Z., Arrouays, D., Richer-de-Forges, A. C., and Chen, S.: National-scale mapping of soil organic carbon stock in France: New insights and lessons learned by direct and indirect approaches, Soil Environ. Health, 1, 100049, https://doi.org/10.1016/j.seh.2023.100049, 2023.
Cotrufo, M. F., Ranalli, M. G., Haddix, M. L., Six, J., and Lugato, E.: Soil carbon storage informed by particulate and mineral-associated organic matter, Nat. Geosci., 12, 989–994, https://doi.org/10.1038/s41561-019-0484-6, 2019.
Dam, R. F., Mehdi, B. B., Burgess, M. S. E., Madramootoo, C. A., Mehuys, G. R., and Callum, I. R.: Soil bulk density and crop yield under eleven consecutive years of corn with different tillage and residue practices in a sandy loam soil in central Canada, Soil Till. Res., 84, 41–53, https://doi.org/10.1016/j.still.2004.08.006, 2005.
Dawson, J. J. C. and Smith, P.: Carbon losses from soil and its consequences for land-use management, Sci. Total Environ., 382, 165–190, https://doi.org/10.1016/j.scitotenv.2007.03.023, 2007.
De Rosa, D., Ballabio, C., Lugato, E., Fasiolo, M., Jones, A., and Panagos, P.: Soil organic carbon stocks in European croplands and grasslands: How much have we lost in the past decade?, Glob. Change Biol., 30, e16992, https://doi.org/10.1111/gcb.16992, 2023.
Elzhov, T. V., Mullen, K. M., Spiess, A. N., and Bolker, B.: minpack.lm: R Interface to the Levenberg–Marquardt Nonlinear Least-Squares Algorithm Found in MINPACK, Plus Support for Bounds, https://cran.r-project.org/web/packages/minpack.lm/index.html (last access: 15 August 2023), 2015.
European Commission: LUCAS 2009 TOPSOIL data, European Commission [data set], https://esdac.jrc.ec.europa.eu/content/lucas-2009-topsoil-data (last access: 1 June 2023), 2013.
European Commission: LUCAS 2015 TOPSOIL data, European Commission [data set], https://esdac.jrc.ec.europa.eu/content/lucas2015-topsoil-data (last access: 1 June 2023), 2020.
European Commission: LUCAS 2018 TOPSOIL data, European Commission [data set], https://esdac.jrc.ec.europa.eu/content/lucas-2018-topsoil-data (last access: 1 June 2023), 2022.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The shuttle radar topography mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Fernández-Ugalde O., Orgiazzi A., Jones A., Lugato E., Panagos P.: LUCAS 2018 – SOIL COMPONENT: Sampling Instructions for Surveyors, JRC technical report, EUR 28501 EN, European Commission, Joint Research Centre, Ispra, Italy, https://doi.org/10.2760/023673, 2017.
Fernández-Ugalde, O., Orgiazzi, A., Marechal, A., Jones, A., Scarpa, S., Panagos, P., and Van Liedekerke, M.: LUCAS 2018 soil module: presentation of dataset and results, Publications Office of the European Union, Luxembourg, https://doi.org/10.2760/215013, 2022.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315, https://doi.org/10.1002/joc.5086, 2017.
Ghehi, N. G., Nemes, A., Verdoodt, A., Van Ranst, E., Cornelis, W. M., and Boeckx, P.: Nonparametric techniques for predicting soil bulk density of tropical rainforest topsoils in Rwanda, Soil Sci. Soc. Am. J., 76, 1172–1183, https://doi.org/10.2136/sssaj2011.0330, 2012.
Gupta, A., Vasava, H. B., Das, B. S., and Choubey, A. K.: Local modeling approaches for estimating soil properties in selected Indian soils using diffuse reflectance data over visible to near-infrared region, Geoderma, 325, 59–71, https://doi.org/10.1016/j.geoderma.2018.03.025, 2018.
Gupta, S. C. and Larson, W. E.: Estimating soil-waster retention characteristics from particle-size distribution, organic-matter percent, and bulk-density, Water Resour. Res., 15, 1633–1635, https://doi.org/10.1029/WR015i006p01633, 1979.
Hollis, J. M., Hannam, J., and Bellamy, P. H.: Empirically-derived pedotransfer functions for predicting bulk density in European soils, Eur. J. Soil Sci., 63, 96–109, https://doi.org/10.1111/j.1365-2389.2011.01412.x, 2012.
Hu, B., Xie, M., Shi, Z., Li, H., Chen, S., Wang, Z., Zhou, Y., Ni, H., Geng, Y., Zhu, Q., and Zhang, X.: Fine-resolution mapping of cropland topsoil pH of Southern China and its environmental application, Geoderma, 442, 116798, https://doi.org/10.1016/j.geoderma.2024.116798, 2024.
Jalabert, S. S. M., Martin, M. P., Renaud, J. P., Boulonne, L., Jolivet, C., Montanarella, L., and Arrouays, D.: Estimating forest soil bulk density using boosted regression modelling, Soil Use Manage., 26, 516–528, https://doi.org/10.1111/j.1475-2743.2010.00305.x, 2010.
Katuwal, S., Knadel, M., Norgaard, T., Moldrup, P., Greve, M. H., and de Jonge, L. W.: Predicting the dry bulk density of soils across Denmark: Comparison of single-parameter, multi-parameter, and vis-NIR based models, Geoderma, 361, 114080, https://doi.org/10.1016/j.geoderma.2019.114080, 2020.
Lal, R.: Digging deeper: A holistic perspective of factors affecting soil organic carbon sequestration in agroecosystems, Glob. Change Biol., 24, 3285–3301, https://doi.org/10.1111/gcb.14054, 2018.
Lark, R. M., Rawlins, B. G., Robinson, D. A., Lebron, I., and Tye, A. M.: Implications of short-range spatial variation of soil bulk density for adequate field-sampling protocols: methodology and results from two contrasting soils, Eur. J. Soil Sci., 65, 803–814, https://doi.org/10.1111/ejss.12178, 2014.
Lemercier, B., Lagacherie, P., Amelin, J., Sauter, J., Pichelin, P., Richer-de-Forges, A. C., and Arrouays, D.: Multiscale evaluations of global, national and regional digital soil mapping products in France, Geoderma, 425, 116052, https://doi.org/10.1016/j.geoderma.2022.116052, 2022.
Li, S., Li, Q., Wang, C., Li, B., Gao, X., Li, Y., and Wu, D.: Spatial variability of soil bulk density and its controlling factors in an agricultural intensive area of Chengdu Plain, Southwest China, J. Integr. Agr., 18, 290–300, https://doi.org/10.1016/S2095-3119(18)61930-6, 2019.
Liu, Y., Chen, S., Yu, Q., Cai, Z., Zhou, Q., Bellingrath-Kimura, S. D., and Wu, W.: Improving digital mapping of soil organic matter in cropland by incorporating crop rotation, Geoderma, 438, 116620, https://doi.org/10.1016/j.geoderma.2023.116620, 2023.
Maestre, F. T., Benito, B. M., Berdugo, M., Concostrina-Zubiri, L., Delgado-Baquerizo, M., Eldridge, D. J., Guirado, E., Gross, N., Kéfi, S., Bagousse-Pinguet, Y. L., Ochoa-Hueso, R., and Soliveres, S.: Biogeography of global drylands, New Phytol., 231, 540–558, https://doi.org/10.1111/nph.17395, 2021.
Makovníková, J., Širáň, M., Houšková, B., Pálka, B., and Jones, A.: Comparison of different models for predicting soil bulk density. Case study–Slovakian agricultural soils, Int. Agrophys., 31, 491–498, https://doi.org/10.1515/intag-2016-0079, 2017.
Meyer, H. and Pebesma, E.: Predicting into unknown space? Estimating the area of applicability of spatial prediction models, Methods Ecol. Evol., 12, 1620–1633, https://doi.org/10.1111/2041-210X.13650, 2021.
Munera-Echeverri, J.-L., Martin, M. P., Boulonne, L., Saby, N. P. A., and Arrouays, D.: Assessing carbon stock changes in French top soils in croplands and grasslands: comparison of fixed depth and equivalent soil mass. 22th World Congress of Soil Sciences, Jul 2022, Glasgow, United Kingdom, https://doi.org/10.1111/ejss.12002, 2022.
Nasta, P., Palladino, M., Sica, B., Pizzolante, A., Trifuoggi, M., Toscanesi, M., Giarra, A., D'Auria, J., Nicodemo, F., Mazzitelli, C., Lazzaro, U., Fiore, D. P., and Romano, N.: Evaluating pedotransfer functions for predicting soil bulk density using hierarchical mapping information in Campania, Italy, Geoderma Reg., 21, e00267, https://doi.org/10.1016/j.geodrs.2020.e00267, 2020.
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.
Orgiazzi, A., Panagos, P., Fernández-Ugalde, O., Wojda, P., Labouyrie, M., Ballabio, C., Franco, A., Pistocchi, A., Montanarella, L and Jones, A.: LUCAS Soil Biodiversity and LUCAS Soil Pesticides, new tools for research and policy development, Eur. J. Soil Sci., 73, e13299, https://doi.org/10.1111/ejss.13299, 2022.
Pacini, L., Yunta, F., Jones, A., Montanarella, L., Barrè, P., Saia, S., Chen, S., and Schillaci, C.: Fine earth soil bulk density at 0.2 m depth from Land Use and Coverage Area Frame Survey (LUCAS) soil 2018, Eur. J. Soil Sci., 74, e13391, https://doi.org/10.1111/ejss.13391, 2023.
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, 2019.
Palladino, M., Romano, N., Pasolli, E., and Nasta, P.: Developing pedotransfer functions for predicting soil bulk density in Campania, Geoderma, 412, 115726, https://doi.org/10.1016/j.geoderma.2022.115726, 2022.
Palmtag, J., Obu, J., Kuhry, P., Richter, A., Siewert, M. B., Weiss, N., Westermann, S., and Hugelius, G.: A high spatial resolution soil carbon and nitrogen dataset for the northern permafrost region based on circumpolar land cover upscaling, Earth Syst. Sci. Data, 14, 4095–4110, https://doi.org/10.5194/essd-14-4095-2022, 2022.
Panagos, P., Van Liedekerke, M., Borrelli, P., Köninger, J., Ballabio, C., Orgiazzi, A., Lugato, E., Liakos, L., Hervas, J., Jones, A and Montanarella, L.: European Soil Data Centre 2.0: Soil data and knowledge in support of the EU policies, Eur. J. Soil Sci., 73, e13315, https://doi.org/10.1111/ejss.13315, 2022.
Panagos, P., De Rosa, D., Liakos, L., Labouyrie, M., Borrelli, P., and Ballabio, C.: Soil bulk density assessment in Europe, Agr. Ecosyst. Environ., 364, 108907, https://doi.org/10.1016/j.agee.2024.108907, 2024.
Patton, N. R., Lohse, K. A., Seyfried, M., Will, R., and Benner, S. G.: Lithology and coarse fraction adjusted bulk density estimates for determining total organic carbon stocks in dryland soils, Geoderma, 337, 844–852, https://doi.org/10.1016/j.geoderma.2018.10.036, 2019.
Perie, C. and Ouimet, R.: Organic carbon, organic matter and bulk density relationships in boreal forest soils, Can. J. Soil Sci., 88, 315–325, https://doi.org/10.4141/cjss06008, 2008.
Poeplau, C., Vos, C., and Don, A.: Soil organic carbon stocks are systematically overestimated by misuse of the parameters bulk density and rock fragment content, SOIL, 3, 61–66, https://doi.org/10.5194/soil-3-61-2017, 2017.
Pribyl, D. W.: A critical review of the conventional SOC to SOM conversion factor, Geoderma, 156, 75–83, https://doi.org/10.1016/j.geoderma.2010.02.003, 2010.
Rabot, E., Wiesmeier, M., Schlüter, S., and Vogel, H. J.: Soil structure as an indicator of soil functions: A review, Geoderma, 314, 122–137, https://doi.org/10.1016/j.geoderma.2017.11.009, 2018.
Ramcharan, A., Hengl, T., Beaudette, D., and Wills, S.: A soil bulk density pedotransfer function based on machine learning: A case study with the ncss soil characterization database, Soil Sci. Soc. Am. J., 81, 1279–1287, https://doi.org/10.2136/sssaj2016.12.0421, 2017.
Rawls, W. J. and Brakensiek, D. L.: Prediction of soil water properties for hydrologic modeling, ASCE, in: Proceedings of a Symposium Watershed Management in the Eighties, New York, 30 April–1 May 1985, edited by: Jones, E. B. and Ward, T. J., 293–299, 1985.
Richer-de-Forges, A. C., Arrouays, D., Poggio, L., Chen, S., Lacoste, M., and Minasny, B.: Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of particle size distribution. A case study in central France, Pedosphere, 33, 731–743, https://doi.org/10.1016/j.pedsph.2022.07.009, 2023.
Sanderman, J., Savage, K., and Dangal, S. R.: Mid-infrared spectroscopy for prediction of soil health indicators in the United States, Soil Sci. Soc. Am. J., 84, 251–261, https://doi.org/10.1002/saj2.20009, 2020.
Schillaci, C., Perego, A., Valkama, E., Märker, M., Saia, S., Veronesi, F., Lipani, A., Lombardo, L., Tadiello, T., Gamper, G. A., Tedone, L., Moss, C., Pareja-Serrano, E., Amato, G., Kühl, K., Dămătîrcă, C., Cogato, A., Mzid, N., Eeswaran, R., Rabelo, M., Sperandio, G., Bosino, A., Bufalini, M., Tunçay, T., Ding, J., Fiorentini, M., Tiscornia, G., Conradt, S., Botta, M., and Acutis, M.: New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems, Sci. Total Environ., 780, 146609, https://doi.org/10.1016/j.scitotenv.2021.146609, 2021.
Schrumpf, M., Schulze, E. D., Kaiser, K., and Schumacher, J.: How accurately can soil organic carbon stocks and stock changes be quantified by soil inventories?, Biogeosciences, 8, 1193–1212, https://doi.org/10.5194/bg-8-1193-2011, 2011.
Shiri, J., Keshavarzi, A., Kisi, O., Karimi, S., and Iturraran-Viveros, U.: Modeling soil bulk density through a complete data scanning procedure: Heuristic alternatives, J. Hydrol., 549, 592–602, https://doi.org/10.1016/j.jhydrol.2017.04.035, 2017.
Sprengel, C.: Ueber Pflanzenhumus, Humussaüre und humussaure Salze, Archiv für die Gesammte Naturlehre, 8, 145–220, 1826.
Sun, W., Canadell, J. G., Yu, L., Yu, L., Zhang, W., Smith, P., Fischer, T., and Huang, Y.: Climate drives global soil carbon sequestration and crop yield changes under conservation agriculture, Glob. Change Biol., 26, 3325–3335, https://doi.org/10.1111/gcb.15001, 2020.
Taalab, K., Corstanje, R., Mayr, T. M., Whelan, M. J., and Creamer, R. E: The application of expert knowledge in Bayesian networks to predict soil bulk density at the landscape scale, Eur. J. Soil Sci., 66, 930–941, https://doi.org/10.1111/ejss.12282, 2015.
Tao, F., Huang, Y., Hungate, B. A., Manzoni, S., Frey, S. D., Schmidt, M. W. I., Reichstein, M., Carvalhais, N., Ciais, P., Jiang, L., Lehmann, J., Wang, Y., Houlton, B. Z., Ahrens, B., Mishra, U., Hugelius, G., Hocking, T. D., Lu, X., Shi, Z., Viatkin, K., Vargas, R., Yigini, Y., Omutom C., Malik, A. A., Peralta, G., Cuevas-Corona, R., Paolo, L. E. D., Luotto, I., Liao, C., Liang, Y., Saynes, V. S., Huang, X., and Luo, Y.: Microbial carbon use efficiency promotes global soil carbon storage, Nature, 618, 981–985, https://doi.org/10.1038/s41586-023-06042-3, 2023.
Tautges, N. E., Chiartas, J. L., Gaudin, A. C., O'Geen, A. T., Herrera, I., and Scow, K. M.: Deep soil inventories reveal that impacts of cover crops and compost on soil carbon sequestration differ in surface and subsurface soils, Glob. Change Biol., 25, 3753–3766, https://doi.org/10.1111/gcb.14762, 2019.
Tifafi, M., Guenet, B., and Hatté, C.: Large Differences in Global and Regional Total Soil Carbon Stock Estimates Based on SoilGrids, HWSD, and NCSCD: Intercomparison and Evaluation Based on Field Data From USA, England, Wales, and France: Differences in total SOC stock estimates, Global Biogeochem. Cy., 32, 42–56, https://doi.org/10.1002/2017GB005678, 2018.
Van Bemmelen, J. M.: Über die Bestimmung des Wassers, des Humus, des Schwefels, der in den colloïdalen Silikaten gebundenen Kieselsäure, des Mangans u.s.w. im Ackerboden, Die Landwirthschaftlichen Versuchs-Stationen, 37, 279–290, 1890.
Van Looy, K., Bouma, J., Herbst, M., Koestel, J., Minasny, B., Mishra, U., Montzka, C., Nemes, A., Pachepsky, Y. A., Padarian, J., Schaap, M. G., Tóth, B., Verhoef, A., Vanderborght, J., van der Ploeg, M. J., Weihermüller, L., Zacharias, S., Zhang, Y., and Vereecken, H.: Pedotransfer functions in Earth system science: Challenges and perspectives, Rev. Geophys., 55, 1199–1256, https://doi.org/10.1002/2017RG000581, 2017.
Wang, M., Guo, X., Zhang, S., Xiao, L., Mishra, U., Yang, Y., Zhu, B., Wang, G., Mao, X., Qian, T., Jiang, T., Shi, Z., and Luo, Z.: Global soil profiles indicate depth-dependent soil carbon losses under a warmer climate, Nat. Commun., 13, 5514, https://doi.org/10.1038/s41467-022-33278-w, 2022.
Wang, N., Chen, S., Huang, J., Frappart, F., Taghizadeh, R., Zhang, X., Wigneron, J. P., Xue, J., Xiao, Y., Peng, J., and Shi, Z.: Global Soil Salinity Estimation at 10 m Using Multi-source Remote Sensing, J. Remote Sens., https://doi.org/10.34133/remotesensing.0130, 2024.
Wang, Y., Luo, G., Li, C., Ye, H., Shi, H., Fan, B., Zhang, W., Zhang, C., Xie, M., and Zhang, Y.: Effects of land clearing for agriculture on soil organic carbon stocks in drylands: A meta-analysis, Glob. Change Biol., 29, 547–562, https://doi.org/10.1111/gcb.16481, 2023.
Wendt, J. W. and Hauser, S.: An equivalent soil mass procedure for monitoring soil organic carbon in multiple soil layers, Eur. J. Soil Sci., 64, 58–65, https://doi.org/10.1111/ejss.12002, 2013.
Wiesmeier, M., Spörlein, P., Geuß, U., Hangen, E., Haug, S., Reischl, A., Schilling, B., von Lützow, M., and Kögel-Knabner, I.: Soil organic carbon stocks in southeast Germany (Bavaria) as affected by land use, soil type and sampling depth, Glob. Change Biol., 18, 2233–2245, https://doi.org/10.1111/j.1365-2486.2012.02699.x, 2012.
Xiao, Y., Xue, J., Zhang, X., Wang, N., Hong, Y., Jiang, Y., Zhou, Y., Teng, H., Hu, B., Lugato, E., Richer-de-Forges, A. C., Arrouays, D., Shi, Z., and Chen, S.: Improving pedotransfer functions for predicting soil mineral associated organic carbon by ensemble machine learning, Geoderma, 428, 116208, https://doi.org/10.1016/j.geoderma.2022.116208, 2022a.
Xiao, Y., Xue, J., Zhang, X., Wang, N., Hong, Y., Jiang, Y., Zhou, Y., Teng, H., Hu, B., Lugato, E., Richer-de-Forges, A.C., Arrouays, D., Shi, Z., and Chen, S.: Forward Recursive Feature Selection, Zenodo [software], https://doi.org/10.5281/zenodo.7141020, 2022b.
Yi, X., Li, G., and Yin, Y.: Pedotransfer functions for estimating soil bulk density: A case study in the three-river headwater region of Qinghai Province, China, Pedosphere, 26, 362–373, https://doi.org/10.1016/S1002-0160(15)60049-2, 2016.
Yost, J. L. and Hartemink, A. E.: How deep is the soil studied–an analysis of four soil science journals, Plant Soil, 452, 5–18, https://doi.org/10.1007/s11104-020-04550-z, 2020.
Zhang, X., Chen, S., Xue, J., Wang, N., Xiao, Y., Chen, Q., Hong, Y., Zhou, Y., Teng, H., Hu, B., Zhuo, Z., Ji, W., Huang, Y., Gou, Y., Richer-de-Forges, A. C., Arrouays, D., and Shi, Z.: Improving model parsimony and accuracy by modified greedy feature selection in digital soil mapping, Geoderma, 432, 116383, https://doi.org/10.1016/j.geoderma.2023.116383, 2023.
Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J.: Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization, ACM T. Math. Software, 23, 550–560, https://doi.org/10.1145/279232.279236, 1997.
Zomer, R. J., Xu, J., and Trabucco, A.: Version 3 of the global aridity index and potential evapotranspiration database, Sci. Data, 9, 409–409, https://doi.org/10.1038/s41597-022-01493-1, 2022.
Short summary
A new dataset for topsoil bulk density (BD) and soil organic carbon (SOC) stock (0–20 cm) across Europe using machine learning was generated. The proposed approach performed better in BD prediction and slightly better in SOC stock prediction than earlier-published PTFs. The outcomes present a meaningful advancement in enhancing the accuracy of BD, and the resultant topsoil BD and SOC stock datasets across Europe enable more precise soil hydrological and biological modeling.
A new dataset for topsoil bulk density (BD) and soil organic carbon (SOC) stock (0–20 cm) across...
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