Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-2941-2024
© 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-16-2941-2024
© 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
| 
25 Jun 2024
Data description paper |
| 25 Jun 2024
| 25 Jun 2024
BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands
Anatol Helfenstein
CORRESPONDING AUTHOR
Soil Geography and Landscape Group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Vera L. Mulder
Soil Geography and Landscape Group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Mirjam J. D. Hack-ten Broeke
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Maarten van Doorn
Nutriënten Management Instituut, Nieuwe Kanaal 7C, 6709 PA, Wageningen, the Netherlands
Environmental Systems Analysis Group, Wageningen University & Research, P.O. Box 47, 6700 AA, Wageningen, the Netherlands
Kees Teuling
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Dennis J. J. Walvoort
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Gerard B. M. Heuvelink
Soil Geography and Landscape Group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
ISRIC – World Soil Information, P.O. Box 353, 6700 AJ, Wageningen, the Netherlands
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We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 16 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to a fast chemical diagnosis. Our data-driven reduction of the library has the potential to accurately monitor carbon at the plot scale.
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Cited articles
Aarts, N. and Leeuwis, C.: The Politics of Changing the Dutch Agri-Food System, Journal of Political Sociology, 1, 1, https://doi.org/10.54195/jps.14922, 2023. a
Adhikari, K., Kheir, R. B., Greve, M. B., Bøcher, P. K., Malone, B. P., Minasny, B., McBratney, A. B., and Greve, M. H.: High-Resolution 3-D Mapping of Soil Texture in Denmark, Soil Sci. Soc. Am. J., 77, 860–876, https://doi.org/10.2136/sssaj2012.0275, 2013. a
Aitchison, J.: The statistical analysis of compositional data, Chapman and Hall, London, 1986. a
Akpa, S. I. C., Odeh, I. O. A., Bishop, T. F. A., and Hartemink, A. E.: Digital Mapping of Soil Particle-Size Fractions for Nigeria, Soil Sci. Soc. Am. J., 78, 1953–1966, https://doi.org/10.2136/sssaj2014.05.0202, 2014. a
Amirian-Chakan, A., Minasny, B., Taghizadeh-Mehrjardi, R., Akbarifazli, R., Darvishpasand, Z., and Khordehbin, S.: Some practical aspects of predicting texture data in digital soil mapping, Soil Till. Res., 194, 104289, https://doi.org/10.1016/j.still.2019.06.006, 2019. a, b
Appelhans, T., Detsch, F., Reudenbach, C., and Woellauer, S.: mapview: Interactive viewing of spatial data in R, Tech. rep., https://github.com/r-spatial/mapview (last access: 15 January 2024), 2023. a
Arets, E. J. M. M., Kolk, J. W. H. v. d., Hengeveld, G. M., Lesschen, J. P., Kramer, H., Kuikman, P. J., and Schelhaas, N. J.: Greenhouse gas reporting for the LULUCF sector in the Netherlands: methodological background, update 2020, WOt-technical report 168, Statutory Research Tasks Unit for Nature & the Environment (WOT Natuur & Milieu), Wageningen, the Netherlands, WOT Natuur & Milieu, https://doi.org/10.18174/517340, 2020. a, b
Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G. B. M., Hong, S. Y., Lagacherie, P., Lelyk, G., McBratney, A. B., McKenzie, N. J., Mendonca-Santos, M. d. L., Minasny, B., Montanarella, L., Odeh, I. O. A., Sanchez, P. A., Thompson, J. A., and Zhang, G.-L.: Chapter Three – GlobalSoilMap: Toward a Fine-Resolution Global Grid of Soil Properties, in: Advances in Agronomy, edited by: Sparks, D. L., vol. 125, 93–134, Academic Press, https://doi.org/10.1016/B978-0-12-800137-0.00003-0, 2014a. a, b, c, d
Arrouays, D., McBratney, A., Minasny, B., Hempel, J., Heuvelink, G. B. M., MacMillan, R. A., Hartemink, A., Lagacherie, P., and McKenzie, N.: The GlobalSoilMap project specifications, in: Proceedings of the 1st GlobalSoilMap Conference, 9–12, https://doi.org/10.1201/b16500-4, 2015. a, b, c, d
Arrouays, D., Leenaars, J. G. B., Richer-de Forges, A. C., Adhikari, K., Ballabio, C., Greve, M., Grundy, M., Guerrero, E., Hempel, J., Hengl, T., Heuvelink, G. B. M., Batjes, N., Carvalho, E., Hartemink, A., Hewitt, A., Hong, S.-Y., Krasilnikov, P., Lagacherie, P., Lelyk, G., Libohova, Z., Lilly, A., McBratney, A., McKenzie, N., Vasquez, G. M., Mulder, V. L., Minasny, B., Montanarella, L., Odeh, I., Padarian, J., Poggio, L., Roudier, P., Saby, N., Savin, I., Searle, R., Solbovoy, V., Thompson, J., Smith, S., Sulaeman, Y., Vintila, R., Rossel, R. V., Wilson, P., Zhang, G.-L., Swerts, M., Oorts, K., Karklins, A., Feng, L., Ibelles Navarro, A. R., Levin, A., Laktionova, T., Dell'Acqua, M., Suvannang, N., Ruam, W., Prasad, J., Patil, N., Husnjak, S., Pásztor, L., Okx, J., Hallett, S., Keay, C., Farewell, T., Lilja, H., Juilleret, J., Marx, S., Takata, Y., Kazuyuki, Y., Mansuy, N., Panagos, P., Van Liedekerke, M., Skalsky, R., Sobocka, J., Kobza, J., Eftekhari, K., Alavipanah, S. K., Moussadek, R., Badraoui, M., Da Silva, M., Paterson, G., Gonçalves, M. d. C., Theocharopoulos, S., Yemefack, M., Tedou, S., Vrscaj, B., Grob, U., Kozák, J., Boruvka, L., Dobos, E., Taboada, M., Moretti, L., and Rodriguez, D.: Soil legacy data rescue via GlobalSoilMap and other international and national initiatives, GeoResJ, 14, 1–19, https://doi.org/10.1016/j.grj.2017.06.001, 2017. a
Arrouays, D., Mulder, V. L., and Richer-de Forges, A. C.: Soil mapping, digital soil mapping and soil monitoring over large areas and the dimensions of soil security – A review, Soil Security, 5, 100018, https://doi.org/10.1016/j.soisec.2021.100018, 2021. a
Bakker, J., Dessel, B. v., and Zadelhoff, F. V.: Natuurwaardenkaart 1988: natuurgebieden, bossen en natte gronden in Nederland, 266862, s-Gravenhage SDU, ISBN 90-12-06089-3, https://library.wur.nl/WebQuery/hydrotheek/266862 (last access: 16 January 2024), 1989. a
Baltensweiler, A., Walthert, L., Hanewinkel, M., Zimmermann, S., and Nussbaum, M.: Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland, Geoderma Regional, 27, e00437, https://doi.org/10.1016/j.geodrs.2021.e00437, 2021. a
Baumann, P., Helfenstein, A., Gubler, A., Keller, A., Meuli, R. G., Wächter, D., Lee, J., Viscarra Rossel, R., and Six, J.: Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring, SOIL, 7, 525–546, https://doi.org/10.5194/soil-7-525-2021, 2021. a
Been, T. H., Kempenaar, C., van Evert, F. K., Hoving, I. E., Kessel, G. J. T., Dantuma, W., Booij, J. A., Molendijk, L. P. G., Sijbrandij, F. D., and van Boheemen, K.: Akkerweb and farmmaps: Development of Open Service Platforms for Precision Agriculture, in: Precision Agriculture: Modelling, edited by: Cammarano, D., van Evert, F. K., and Kempenaar, C., Progress in Precision Agriculture, 269–293, Springer International Publishing, Cham, ISBN 978-3-031-15258-0, https://doi.org/10.1007/978-3-031-15258-0_16, 2023. a
Behrens, T., Förster, H., Scholten, T., Steinrücken, U., Spies, E.-D., and Goldschmitt, M.: Digital soil mapping using artificial neural networks, J. Plant Nutr. Soil Sci., 168, 21–33, https://doi.org/10.1002/jpln.200421414, 2005. a
Behrens, T., Schmidt, K., MacMillan, R. A., and Viscarra Rossel, R. A.: Multi-scale digital soil mapping with deep learning, Sci. Rep., 8, 15244, https://doi.org/10.1038/s41598-018-33516-6, 2018a. a
Behrens, T., Schmidt, K., Rossel, R. A. V., Gries, P., Scholten, T., and MacMillan, R. A.: Spatial modelling with Euclidean distance fields and machine learning, Eur. J. Soil Sci., 69, 757–770, https://doi.org/10.1111/ejss.12687, 2018b. a
BIJ12: Informatiemodel Natuur (IMNa), https://www.bij12.nl/onderwerpen/natuur-en-landschap/digitale-keten-natuur-ketensamenwerking/informatiemodel-natuur-imna/ (last access: 17 January 2024), 2019. a
Bouma, J. and Hartemink, A. E.: Soil science and society in the Dutch context, Netherlands Journal of Agricultural Science, 50, 133–140, 2003. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Breiman, L.: Manual on Setting Up, Using, and Understanding Random Forests v3.1, Technical report ftp://ftp.stat.berkeley.edu/pub/users/breiman/Using_random_forests_v3.1.pdf (last access: 14 January 2024), 2002. a
Brouwer, F. and Walvoort, D.: Basisregistratie Ondergrond (BRO) - actualisatie bodemkaart: Herkartering van de bodem in Eemland, WOt-technical report 155, WOT Natuur & Milieu, Wageningen, https://doi.org/10.18174/494728, 2019. a
Brouwer, F. and Walvoort, D.: Basisregistratie Ondergrond (BRO) Actualisatie bodemkaart: Herkartering van de veengebieden aan de flanken van de Utrechtse Heuvelrug, WOt-technical report 177, WOT Natuur & Milieu, Wageningen, https://doi.org/10.18174/521574, 2020. a
Brouwer, F., de Vries, F. d., and Walvoort, D. J. J.: Basisregistratie Ondergrond (BRO) actualisatie bodemkaart: Herkartering van de bodem in Flevoland, WOt technical report 143, WOT Natuur & Milieu, WUR, Wageningen, https://library.wur.nl/WebQuery/wurpubs/549064 (last access: 13 January 2024), 2018. a, b, c, d, e
Brouwer, F., Maas, G., Teuling, K., Harkema, T., and Verzandvoort, S.: Bodemkaart en Geomorfologische Kaart van Nederland: actualisatie 2020-2021 en toepassing: deelgebieden Gelderse Vallei-Zuid en -West en Veluwe-Zuid, WOt-rapport 134, WOT Natuur & Milieu, Wageningen, https://doi.org/10.18174/557455, 2021. a
Brouwer, F., Assinck, F., Harkema, T., Teuling, K., and Walvoort, D.: Actualisatie van de bodemkaart in degemeente Vijfheerenlanden: herkartering van de verbreiding van veen, WOt-rapport 151, WOT Natuur & Milieu, Wageningen, https://research.wur.nl/en/publications/actualisatie-van-de-bodemkaart-in-degemeente-vijfheerenlanden-her (last access: 25 January 2024), 2023. a, b
Brus, D., Hengl, T., Heuvelink, G., Kempen, B., Mulder, T. V., Olmedo, G. F., Poggio, L., Ribeiro, E., and Omuto, C. T.: Soil Organic Carbon Mapping Cookbook, edited by: Yigini, Y., Baritz, R., and Vargas, R. R., FAO, Rome, 1st edn., ISBN 978-92-5-130440-2, 2017. a
Brus, D. J.: Sampling for digital soil mapping: A tutorial supported by R scripts, Geoderma, 338, 464–480, https://doi.org/10.1016/j.geoderma.2018.07.036, 2019. a
Brus, D. J., Vašát, R., Heuvelink, G. B. M., Knotters, M., Vries, F. d., and Walvoort, D. J. J.: Towards a Soil Information System with quantified accuracy. A prototype for mapping continuous soil properties, Statutory Research Tasks Unit for Nature and the Environment 197, Alterra, Wageningen, 2009. a, b, c, d, e, f, g, h
Brus, D. J., Kempen, B., and Heuvelink, G. B. M.: Sampling for validation of digital soil maps, Eur. J. Soil Sci., 62, 394–407, https://doi.org/10.1111/j.1365-2389.2011.01364.x, 2011. a, b, c, d
Buchanan, S., Triantafilis, J., Odeh, I. O. A., and Subansinghe, R.: Digital soil mapping of compositional particle-size fractions using proximal and remotely sensed ancillary data, Geophysics, 77, WB201–WB211, https://doi.org/10.1190/geo2012-0053.1, 2012. a
Buringh, P., Stuer, G. G. L., and Vink, P.: Some techniques and methods of soil survey in the Netherlands, Netherlands Journal of Agricultural Science, 10, 17, 1962. a
BZK: Ministerie van Binnenlandse Zaken en Koninkrijksrelaties (BZK): Uitvoeringsregeling Meststoffenwet, https://wetten.overheid.nl/BWBR0018989/2022-11-17#Hoofdstuk9_Paragraaf8_Artikel103a (last access: 17 January 2024), 2022. a
Carré, F., McBratney, A. B., and Minasny, B.: Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping, Geoderma, 141, 1–14, https://doi.org/10.1016/j.geoderma.2007.01.018, 2007. a
Chagas, C. D. S., de Carvalho Junior, W., Bhering, S. B., and Calderano Filho, B.: Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions, CATENA, 139, 232–240, https://doi.org/10.1016/j.catena.2016.01.001, 2016. a
Chen, S., Arrouays, D., Leatitia Mulder, V., 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. a, b, c, d, e, f, g, h, i
Chen, S., Saby, N. P. A., Martin, M. P., Barthès, B. G., Gomez, C., Shi, Z., and Arrouays, D.: Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping, Geoderma, 433, 116467, https://doi.org/10.1016/j.geoderma.2023.116467, 2023. a
Clement, J.: GIS Vierde Bosstatistiek: Gebruikersdocumentatie, Documentatie van bestanden, Tech. rep., Research Instituut voor de Groene Ruimte, Alterra, Wageningen, 2001. a
Cochran, W. G.: Sampling techniques, John Wiley & Sons, New York, 3rd edn., 1977. a
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015. a
de Bakker, H. and Schelling, J.: Systeem van bodemclassificatie voor Nederland: de hogere niveaus: With Engl. summary: A system of soil classification for the Netherlands, Centrum voor Landbouwpublikaties en Landbouwdocumentatie, Wageningen, the Netherlands, second, revised edn., ISBN 978-90-220-0997-0, 1989. a, b, c, d, e
de Bruin, S., Bregt, A., and Ven, M. V. D.: Assessing fitness for use: the expected value of spatial data sets, Int. J. Geogr. Inf., 15, 457–471, https://doi.org/10.1080/13658810110053116, 2001. a
de Bruin, S., Brus, D. J., Heuvelink, G. B. M., van Ebbenhorst Tengbergen, T., and Wadoux, A. M. J.-C.: Dealing with clustered samples for assessing map accuracy by cross-validation, Ecol. Inform., 69, 101665, https://doi.org/10.1016/j.ecoinf.2022.101665, 2022. a, b
de Gruijter, J. J., Walvoort, D. J. J., and van Gams, P. F. M.: Continuous soil maps – a fuzzy set approach to bridge the gap between aggregation levels of process and distribution models, Geoderma, 77, 169–195, https://doi.org/10.1016/S0016-7061(97)00021-9, 1997. a
de Gruijter, J. J., van der Horst, J. B. F., Heuvelink, G. B. M., Knotters, M., and Hoogland, T.: Grondwater opnieuw op de kaart; methodiek voor de actualisering van grondwaterstandsinformatie en perceelsclassificatie naar uitspoelingsgevoeligheid voor nitraat, Alterra-rapport 915, Alterra, Wageningen, https://edepot.wur.nl/26169 (last access: 16 December 2023), 2004. a, b
de Gruijter, J. J., Brus, D., Bierkens, M., and Knotters, M.: Sampling for Natural Resource Monitoring, Springer, The Netherlands, ISBN 978-3-540-33161-2, 2006. a
de Vries, F. and Al, E. J.: De groeiplaatsgeschiktheid voor bosdoeltypen in beeld met ALBOS, Tech. Rep. 234, DLO-Staring Centrum, https://edepot.wur.nl/303208 (last access: 16 December 2023), 1992. a
de Vries, F., de Groot, W., Hoogland, T., and Denneboom, J.: De Bodemkaart van Nederland digitaal;. Toelichting bij inhoud, actualiteit en methodiek en korte beschrijving van additionel informatie, Alterra-rapport 811, Alterra, Research Instituut voor de Groene Ruimte, Wageningen, the Netherlands, 2003. a, b, c, d, e, f, g, h, i, j
de Vries, F., Brus, D. J., Kempen, B., Brouwer, F., and Heidema, A. H.: Actualisatie bodemkaart veengebieden: deelgebied en 2 in Noord Nederland, Alterra-rapport 2556, Alterra, Wageningen, https://edepot.wur.nl/314315 (last access: 16 December 2023), 2014. a
de Vries, F., Walvoort, D., and Brouwer, F.: Basisregistratie Ondergrond (BRO) Actualisatie bodemkaart; Herkartering van de eenheden met slappe kleilagen, Wageningen Environmental Research Rapport 2834, Wageningen Environmental Research, Wageningen, https://doi.org/10.18174/423728, 2017. a
de Vries, F., Brouwer, F., and Walvoort, D.: Basisregistratie Ondergrond (BRO) actualisatie bodemkaart: Herkatering westelijk veengebied Waterschap Drents Overijsselse Delta, Wageningen Environmental Research Rapport 2887, Wageningen Environmental Research, Wageningen, https://doi.org/10.18174/450341, 2018. a
Delmas, M., Saby, N., Arrouays, D., Dupas, R., Lemercier, B., Pellerin, S., and Gascuel-Odoux, C.: Explaining and mapping total phosphorus content in French topsoils, Soil Use Manage., 31, 259–269, https://doi.org/10.1111/sum.12192, 2015. a
Delta Programme: National Delta Programme 2024: Now for the Future, Tech. rep., Ministry of Infrastructure and Water Management, the Ministry of Agriculture, Nature and Food Quality, and the Ministry of the Interior and Kingdom Relations, Den Haag, https://english.deltaprogramma.nl/ (last access: 16 December 2023), 2023. a
Edelmann, C.: Soils of the Netherlands, vol. 53, North-Holland Publishing Company, Amsterdam, https://doi.org/10.1002/jpln.19510530307, 1950. a, b
Erisman, J. W.: Setting ambitious goals for agriculture to meet environmental targets, One Earth, 4, 15–18, https://doi.org/10.1016/j.oneear.2020.12.007, 2021. a
Eurofins Agro: Bemesting Wetgeving, https://www.eurofins-agro.com/nl-nl/bemesting-wetgeving (last access: 16 December 2023), 2024a. a
Eurofins Agro: BemestingsWijzer, https://www.eurofins-agro.com/nl-nl/bemestingswijzer (last access: 16 December 2023), 2024b. a
European Commission: A Soil Deal for Europe: 100 living labs and lighthouses to lead the transition towards healthy soils by 2030, Implementation Plan, European Commission, https://ec.europa.eu/info/sites/default/files/research_and_innovation/funding/documents/soil_mission_implementation_plan_final_for_publication.pdf (last access: 16 December 2023), 2021. a, b, c
Eurostat: Key figures on the European food chain, https://ec.europa.eu/eurostat/en/web/products-key-figures/w/ks-fk-22-001 (last access: 16 December 2023), 2022. a
EZK: Fysisch Geografische Regio's 2013; Ministerie van Economische Zaken en Klimaat (EZK; Ministry of Economic Affairs and Climate), https://nationaalgeoregister.nl/geonetwork/srv/dut/catalog.search#/metadata/c8b5668f-c354-42f3-aafc-d15ae54cf170 (last access: 16 December 2023), 2013. a
EZK: Basisregistratie Gewaspercelen (BRP): 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019. Ministerie van Economische Zaken en Klimaat (EZK; Ministry of Economic Affairs and Climate), Agrarische Areaal Nederland, https://www.pdok.nl/introductie/-/article/basisregistratie-gewaspercelen-brp- (last access: 16 December 2023), 2019. a
Felix, R.: Bodemkartering voor 1943 – het geologisch perspectief, in: Van bodemkaart tot informatiesysteem, edited by: Buurman, P. and Sevink, J., 1–17, Wageningen Pers, Wageningen, 1995. a
Finke, P. A.: On digital soil assessment with models and the Pedometrics agenda, Geoderma, 171–172, 3–15, https://doi.org/10.1016/j.geoderma.2011.01.001, 2012. a
Finke, P. A., de Gruijter, J. J., and Visschers, R.: Status 2001 Landelijke Steekproef Kaarteenheden en toepassingen; Gestructureerde bemonstering en karakterisering Nederlandse bodems, Alterra-rapport 389, Alterra, Research Instituut voor de Groene Ruimte, Wageningen, 2001. a
GDAL/OGR contributors: GDAL/OGR geospatial data abstraction software library, https://gdal.org/ (last access: 16 December 2023), 2023. a
Gregoire, T. G. and Valentine, H. T.: Sampling Strategies for Natural Resources and the Environment, CRC Press, Boca Raton, USA, ISBN 978-0-203-49888-0, 2007. a
Grimm, R. and Behrens, T.: Uncertainty analysis of sample locations within digital soil mapping approaches, Geoderma, 155, 154–163, https://doi.org/10.1016/j.geoderma.2009.05.006, 2010. a
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
Hack-ten Broeke, M., van Beek, C. L., Hoogland, T., Knotters, M., Mol-Dijkstra, J. P., Schils, R., Smit, A., and de Vries, F.: Kaderrichtlijn Bodem; Basismateriaal voor eventuele prioritaire gebieden, Alterra-rapport 2007, Alterra, Wageningen, the Netherlands, 2009. a
Hack-ten Broeke, M. J. D., Mulder, H. M., Bartholomeus, R. P., van Dam, J. C., Holshof, G., Hoving, I. E., Walvoort, D. J. J., Heinen, M., Kroes, J. G., van Bakel, P. J. T., Supit, I., de Wit, A. J. W., and Ruijtenberg, R.: Quantitative land evaluation implemented in Dutch water management, Geoderma, 338, 536–545, https://doi.org/10.1016/j.geoderma.2018.11.002, 2019. a
Hartemink, A. E. and Sonneveld, M. P. W.: Soil maps of The Netherlands, Geoderma, 204–205, 1–9, https://doi.org/10.1016/j.geoderma.2013.03.022, 2013. a
Hastie, T., Tibshirani, R., and Friedman, J. H.: The elements of statistical learning: data mining, inference, and prediction, Springer series in statistics, Springer, New York, NY, 2nd edn., ISBN 978-0-387-84857-0, 978-0-387-84858-7, 2009. a
Hazeu, G., Schuiling, R., Thomas, D., Vittek, M., Storm, M., and Bulens, J. D.: Landelijk Grondgebruiksbestand Nederland 2021 (LGN2021): achtergronden, methodiek en validatie, Rapport 3235, Wageningen Environmental Research, Wageningen, https://research.wur.nl/en/publications/landelijk-grondgebruiksbestand-nederland-2021-lgn2021-achtergrond (last access: 16 December 2023), 2023. a
Hazeu, G. W., Vittek, M., Schuiling, R., Bulens, J. D., Storm, M. H., Roerink, G. J., and Meijninger, W. M. L.: LGN2018: een nieuwe weergave van het grondgebruik in Nederland, Tech. Rep. 3010, Wageningen Environmental Research, Wageningen, https://library.wur.nl/WebQuery/wurpubs/565896 (last access: 16 December 2023), 2020. a, b, c
Helfenstein, A.: 4 Dimensional Information About the Skin of the Earth, Wageningen University & Research [video], https://www.youtube.com/watch?v=ENCYUnqc-wo, last access: 21 June 2024a. a
Helfenstein, A.: BIS-4D. In Earth System Science Data, Zenodo [code], https://doi.org/10.5281/zenodo.12238785, 2024b. 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
Helfenstein, A., Mulder, V. L., Hack-ten Broeke, M. J., van Doorn, M., Teuling, K., Walvoort, D. J., and Heuvelink, G. B.: BIS-4D: Maps of soil properties and their uncertainties at 25 m resolution in the Netherlands, 4TU.ResearchData [data set], https://doi.org/10.4121/0C934AC6-2E95-4422-8360-D3A802766C71, 2024a. a, b, c
Helfenstein, A., Mulder, V. L., Hack-ten Broeke, M. J., van Doorn, M., Teuling, K., Walvoort, D. J., and Heuvelink, G. B.: Spatially explicit environmental variables at 25 m resolution for spatial modelling in the Netherlands, 4TU.ResearchData [data set], https://doi.org/10.4121/6AF610ED-9006-4AC5-B399-4795C2AC01EC, 2024b. a, b
Helfenstein, A., Mulder, V. L., Heuvelink, G. B. M., and Hack-ten Broeke, M. J. D.: Three-dimensional space and time mapping reveals soil organic matter decreases across anthropogenic landscapes in the Netherlands, Commun. Earth Environ., 5, 1–16, https://doi.org/10.1038/s43247-024-01293-y, 2024c. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s
Helfenstein, A., Teuling, K., Walvoort, D. J., Hack-ten Broeke, M. J., Mulder, V. L., van Doorn, M., and Heuvelink, G. B.: Georeferenced point data of soil properties in the Netherlands, 4TU.ResearchData [data set], https://doi.org/10.4121/C90215B3-BDC6-4633-B721-4C4A0259D6DC, 2024d. a
Hengl, T. and MacMillan, R. A.: Predictive Soil Mapping with R, OpenGeoHub foundation, Wageningen, the Netherlands, ISBN 978-0-359-30635-0, http://www.soilmapper.org (last access: 17 January 2024), 2019. a
Hengl, T., Heuvelink, G. B. M., Kempen, B., Leenaars, J. G. B., Walsh, M. G., Shepherd, K. D., Sila, A., MacMillan, R. A., Mendes de Jesus, J., Tamene, L., and Tondoh, J. E.: Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions, PLOS ONE, 10, e0125814, https://doi.org/10.1371/journal.pone.0125814, 2015. a
Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M., and Gräler, B.: Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables, PeerJ, 6, e5518, https://doi.org/10.7717/peerj.5518, 2018. a
Hessel, R., Stolte, J., and Riksen, M. J. P. M.: Huidige maatregelen tegen water- en winderosie in Nederland, Bodem, 21, 11–12, https://research.wur.nl/en/publications/huidige-maatregelen-tegen-water-en-winderosie-in-nederland-2 (last access: 17 January 2024), 2011. a
Heuvelink, G., Brus, D., De Vries, F., Kempen, B., Knotters, M., Vasat, R., and Walvoort, D.: Implications of digital soil mapping for soil information systems, in: 4th Global Workshop on Digital Soil Mapping, p. 6, Rome, Italy, https://edepot.wur.nl/160764 (last access: 17 January 2024), 2010. a
Heuvelink, G. B. M.: Uncertainty quantification of GlobalSoilMap products, in: GlobalSoilMap: Basis of the global spatial soil information system, 335–340, CRC Press, ISBN 978-1-315-77558-6, 2014. a
Heuvelink, G. B. M.: Uncertainty and Uncertainty Propagation in Soil Mapping and Modelling, in: Pedometrics, edited by: McBratney, A. B., Minasny, B., and Stockmann, U., Progress in Soil Science, 439–461, Springer International Publishing, Cham, ISBN 978-3-319-63439-5, https://doi.org/10.1007/978-3-319-63439-5_14, 2018. a, b, c
Heuvelink, G. B. M., Angelini, M. E., Poggio, L., Bai, Z., Batjes, N. H., Bosch, R. v. d., Bossio, D., Estella, S., Lehmann, J., Olmedo, G. F., and Sanderman, J.: Machine learning in space and time for modelling soil organic carbon change, Eur. J. Soil Sci., 72, 1607–1623, https://doi.org/10.1111/ejss.12998, 2020. a
Jukema, G., Ramaekers, P., and Berkhout, P.: De Nederlandse agrarische sector in internationaal verband: Editie 2023, Wageningen Economic Research, ISBN 978-94-6447-546-3, https://doi.org/10.18174/584222, 2023. 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), Schweizerischer Nationalfonds zur Förderung der wissenschaftlichen Forschung (SNF), Bern, 2018. a
Kempen, B., Brus, D. J., Heuvelink, G. B. M., and Stoorvogel, J. J.: Updating the 1:50 000 Dutch soil map using legacy soil data: A multinomial logistic regression approach, Geoderma, 151, 311–326, https://doi.org/10.1016/j.geoderma.2009.04.023, 2009. a, b
Kempen, B., Brus, D. J., and Stoorvogel, J. J.: Three-dimensional mapping of soil organic matter content using soil type–specific depth functions, Geoderma, 162, 107–123, https://doi.org/10.1016/j.geoderma.2011.01.010, 2011. a
Kempen, B., Brus, D. J., and Heuvelink, G. B. M.: Soil type mapping using the generalised linear geostatistical model: A case study in a Dutch cultivated peatland, Geoderma, 189–190, 540–553, https://doi.org/10.1016/j.geoderma.2012.05.028, 2012a. a
Kempen, B., Brus, D. J., Stoorvogel, J. J., Heuvelink, G. B. M., and de Vries, F.: Efficiency Comparison of Conventional and Digital Soil Mapping for Updating Soil Maps, Soil Sci. Soc. Am. J., 76, 2097–2115, https://doi.org/10.2136/sssaj2011.0424, 2012b. a
Kempen, B., Heuvelink, G. B. M., Brus, D., and Walvoort, D.: Towards GlobalSoilMap.net products for The Netherlands, GlobalSoilMap: Basis of the Global Spatial Soil Information System – Proceedings of the 1st GlobalSoilMap Conference, 85–90, https://doi.org/10.1201/b16500-19, 2014. a
Kempen, B., Brus, D. J., and de Vries, F.: Operationalizing digital soil mapping for nationwide updating of the
Keskin, H., Grunwald, S., and Harris, W. G.: Digital mapping of soil carbon fractions with machine learning, Geoderma, 339, 40–58, https://doi.org/10.1016/j.geoderma.2018.12.037, 2019. a
Khaledian, Y. and Miller, B. A.: Selecting appropriate machine learning methods for digital soil mapping, Appl. Math. Model., 81, 401–418, https://doi.org/10.1016/j.apm.2019.12.016, 2020. a
Knotters, M. and Vroon, H. R. J.: The economic value of detailed soil survey in a drinking water collection area in the Netherlands, Geoderma Regional, 5, 44–53, https://doi.org/10.1016/j.geodrs.2015.03.002, 2015. a, b
Knotters, M. and Walvoort, D.: Hoge resolutie, nauwkeurige kaarten?, https://basisregistratieondergrond.nl/doe-mee/begin-dag-bro-tje/bro-tjes-2020/15-oktober-hoge-resolutie-nauwkeurige-kaarten/ (last access: 16 October 2020), 2020. a
Knotters, M., Broeke, M. J. D. H.-t., Hinssen, P. J. W., Kolk, J. W. H. v. d., and Okx, J. P.: Betekenis van BRO/BIS Nederland voor WOT Natuur & Milieu: Een risicoanalyse, WOT Natuur & Milieu, https://research.wur.nl/en/publications/betekenis-van-brobis-nederland-voor-wot-natuur-amp-milieu-een-ris (last access: 17 January 2024), 2015a. a
Knotters, M., Okx, J., Hack-ten Broeke, M., and de Vries, F.: Bodem in beweging: BIS NEderland informeert, Bodem, 25, 11–13, 2015b. a
Kombrink, H., Doornenbal, J. C., Duin, E. J. T., Dulk, M. d., Veen, J. H. t., and Witmans, N.: New insights into the geological structure of the Netherlands; results of a detailed mapping project, Netherlands Journal of Geosciences, 91, 419–446, https://doi.org/10.1017/S0016774600000329, 2012. a
Koomen, A. and Maas, G.: Geomorfologische Kaart Nederland (GKN); Achtergronddocument bij het landsdekkende digitale bestand, Altera-rapport 1039, Alterra, Wageningen, 2004. a
Kortleve, A. J., Mogollón, J. M., Heimovaara, T. J., and Gebert, J.: Topsoil Carbon Stocks in Urban Greenspaces of The Hague, the Netherlands, Urban Ecosystems, 26, 725–742, https://doi.org/10.1007/s11252-022-01315-7, 2023. a
Kroes, J., Van Dam, J., Bartholomeus, R., Groenendijk, P., Heinen, M., Hendriks, R., Mulder, H., Supit, I., and Van Walsum, P.: SWAP version 4; Theory description and user manual, Report 2780, Wageningen Environmental Research, Wageningen, https://doi.org/10.18174/416321, 2017. a
Kuhn, M.: Building Predictive Models in R Using the Caret Package, J. Stat. Softw., 28, 1–26, https://doi.org/10.18637/jss.v028.i05, 2008. a
Kuhn, M.: Classification and Regression Training: package “caret”, GitHub [code], https://github.com/topepo/caret/, 2022. a
Kull, A., Kikas, T., Penu, P., and Kull, A.: Modeling Topsoil Phosphorus – From Observation-Based Statistical Approach to Land-Use and Soil-Based High-Resolution Mapping, Agronomy, 13, 1183, https://doi.org/10.3390/agronomy13051183, 2023. a
Lamigueiro, O. P. and Hijmans, R. J.: Visualization Methods for Raster Data: package “rasterVis”, GitHub [code], https://oscarperpinan.github.io/rastervis/ (last access: 17 January 2024), 2023. a
Lark, R. M. and Bishop, T. F. A.: Cokriging particle size fractions of the soil, Eur. J. Soil Sci., 58, 763–774, https://doi.org/10.1111/j.1365-2389.2006.00866.x, 2007. a
Lehmann, J., Bossio, D. A., Kögel-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. a
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. a
Li, J., Heap, A. D., Potter, A., and Daniell, J. J.: Application of machine learning methods to spatial interpolation of environmental variables, Environ. Model. Softw., 26, 1647–1659, https://doi.org/10.1016/j.envsoft.2011.07.004, 2011. a
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
Loiseau, T., Chen, S., Mulder, V. L., Román Dobarco, M., Richer-de Forges, A. C., Lehmann, S., Bourennane, H., Saby, N. P. A., Martin, M. P., Vaudour, E., Gomez, C., Lagacherie, P., and Arrouays, D.: Satellite data integration for soil clay content modelling at a national scale, Int. J. Appl. Earth Ob., 82, 101905, https://doi.org/10.1016/j.jag.2019.101905, 2019. a
Lookman, R., Vandeweert, N., Merckx, R., and Vlassak, K.: Geostatistical assessment of the regional distribution of phosphate sorption capacity parameters (FeOX and AlOX) in northern Belgium, Geoderma, 66, 285–296, https://doi.org/10.1016/0016-7061(94)00084-N, 1995. a, b
Ma, Y., Minasny, B., McBratney, A., Poggio, L., and Fajardo, M.: Predicting soil properties in 3D: Should depth be a covariate?, Geoderma, 383, 114794, https://doi.org/10.1016/j.geoderma.2020.114794, 2021. a, b
Malone, B., Searle, R., Malone, B., and Searle, R.: Updating the Australian digital soil texture mapping (Part 1*): re-calibration of field soil texture class centroids and description of a field soil texture conversion algorithm, Soil Res., 59, 419–434, https://doi.org/10.1071/SR20283, 2021. a
Malone, B. P., McBratney, A. B., and Minasny, B.: Spatial Scaling for Digital Soil Mapping, Soil Sci. Soc. Am. J., 77, 890–902, https://doi.org/10.2136/sssaj2012.0419, 2013. a
Malone, B. P., Minasny, B., and McBratney, A. B.: Using R for Digital Soil Mapping, Progress in Soil Science, Springer International Publishing, Cham, ISBN 978-3-319-44325-6 978-3-319-44327-0, https://doi.org/10.1007/978-3-319-44327-0, 2017. a
Maring, L., Vries, F. d., Brouwer, F., Groot, H., Kiden, P., Leeters, E. E. J. M., and Mol, G.: IMBOD deelactiviteit 5: inhoudelijke afstemming, Alterra-rapport 1817, Alterra, Wageningen, https://research.wur.nl/en/publications/imbod-deelactiviteit-5-inhoudelijke-afstemming (last access: 17 January 2024), 2009. a, b, c, d
Matos-Moreira, M., Lemercier, B., Dupas, R., Michot, D., Viaud, V., Akkal-Corfini, N., Louis, B., and Gascuel-Odoux, C.: High-resolution mapping of soil phosphorus concentration in agricultural landscapes with readily available or detailed survey data, Eur. J. Soil Sci., 68, 281–294, https://doi.org/10.1111/ejss.12420, 2017. a
McBratney, A., Mendonça Santos, M., and Minasny, B.: On digital soil mapping, Geoderma, 117, 3–52, https://doi.org/10.1016/S0016-7061(03)00223-4, 2003. a, b
McKeague, J. A.: An evaluation of 0.1 m pyrophosphate and pyrophosphate-dithionite in comparison with oxalate as extractants of the accumulation products in podzols and some other soils, Ca. J. Soil Sci., 47, 95–99, https://doi.org/10.4141/cjss67-017, 1967. a
McKeague, J. A., Brydon, J. E., and Miles, N. M.: Differentiation of Forms of Extractable Iron and Aluminum in Soils, Soil Sci. Soc. Am. J., 35, 33–38, https://doi.org/10.2136/sssaj1971.03615995003500010016x, 1971. a
Meinshausen, N.: Quantile Regression Forests, J. Mach. Learn. Res., 7, 983–999, 2006. a
Meyer, H.: “caret” Applications for Spatial-Temporal Models: package “CAST”, GitHub [code], https://github.com/HannaMeyer/CAST (last access: 13 January 2024), 2023. a
Mulder, M., Walvoort, D., Brouwer, F., Tol-Leenders, D. V., and Verzandvoort, S.: Bodemgeschiktheidskaarten voor landbouw in de provincie Noord-Brabant: Een toepassing van Waterwijzer Landbouw, Rapport 3206, Wageningen Environmental Research, Wageningen, the Netherlands, https://research.wur.nl/en/publications/bodemgeschiktheidskaarten-voor-landbouw-in-de-provincie-noord-bra (last access: 18 January 2024), 2022. a
Mulder, V. L., Lacoste, M., Richer-de Forges, A. C., and Arrouays, D.: GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth, Sci. Total Environ., 573, 1352–1369, https://doi.org/10.1016/j.scitotenv.2016.07.066, 2016. a
Møller, A. B., Beucher, A. M., Pouladi, N., and Greve, M. H.: Oblique geographic coordinates as covariates for digital soil mapping, SOIL, 6, 269–289, https://doi.org/10.5194/soil-6-269-2020, 2020. a, b
Neyroud, J.-A. and Lischer, P.: Do different methods used to estimate soil phosphorus availability across Europe give comparable results?, J. Plant Nutr. Soil Sci., 166, 422–431, https://doi.org/10.1002/jpln.200321152, 2003. a
NHI: Nederlands Hydrologisch Instrumentarium (NHI), http://nhi-website-prd.fourdigits.nl:433/en/ (last access: 18 January 2024), 2023. a
Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A.: Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models, SOIL, 3, 191–210, https://doi.org/10.5194/soil-3-191-2017, 2017. a
Nussbaum, M., Zimmermann, S., Walthert, L., and Baltensweiler, A.: Benefits of hierarchical predictions for digital soil mapping – An approach to map bimodal soil pH, Geoderma, 437, 116579, https://doi.org/10.1016/j.geoderma.2023.116579, 2023. a
Odeh, I. O. A., Todd, A. J., and Triantafilis, J.: Spatial prediction of soil particle-size fractions as compositional data, Soil Sci,, 168, 501, https://doi.org/10.1097/01.ss.0000080335.10341.23, 2003. 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
Padarian, J., Minasny, B., and McBratney, A. B.: Using deep learning for digital soil mapping, SOIL, 5, 79–89, https://doi.org/10.5194/soil-5-79-2019, 2019b. a, b
Pahlavan-Rad, M. R. and Akbarimoghaddam, A.: Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran), CATENA, 160, 275–281, https://doi.org/10.1016/j.catena.2017.10.002, 2018. a
Pan, S. J. and Yang, Q.: A Survey on Transfer Learning, IEEE T. Knowl. Data Eng., 22, 1345–1359, https://doi.org/10.1109/TKDE.2009.191, 2010. a
Panagos, P., Montanarella, L., Barbero, M., Schneegans, A., Aguglia, L., and Jones, A.: Soil priorities in the European Union, Geoderma Regional, 29, e00510, https://doi.org/10.1016/j.geodrs.2022.e00510, 2022. a, b
Papadopoulos, G., Edwards, P., and Murray, A.: Confidence estimation methods for neural networks: a practical comparison, IEEE T. Neur. Netw., 12, 1278–1287, https://doi.org/10.1109/72.963764, 2001. a
Pawlowsky-Glahn, V. and Buccianti, A.: Compositional Data Analysis: Theory and Applications, John Wiley & Sons, Ltd, West Sussex, ISBN 978-1-119-97646-2, https://doi.org/10.1002/9781119976462, 2011. a
Pawlowsky-Glahn, V., Egozcue, J. J., and Tolosana-Delgado, R.: Modeling and Analysis of Compositional Data, John Wiley & Sons, West Sussex, ISBN 978-1-118-44306-4, 2015. a
Piikki, K., Wetterlind, J., Söderström, M., and Stenberg, B.: Perspectives on validation in digital soil mapping of continuous attributes – A review, Soil Use Manage., 37, 7–21, https://doi.org/10.1111/sum.12694, 2021. a, b
Poggio, L. and Gimona, A.: 3D mapping of soil texture in Scotland, Geoderma Regional, 9, 5–16, https://doi.org/10.1016/j.geodrs.2016.11.003, 2017. a
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021. a, b, c
QGIS Development Team: QGIS: A Free and Open Source Geographic Information System, https://www.qgis.org (last access: 18 January 2024), 2023. a
R Core Team: R: A language and environment for statistical computing, Tech. rep., R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 18 January 2024), 2023. a
RIVM: Grootschalige Concentratie- en Depositiekaarten Nederland (GCN, GDN), Rijksinstituut voor Volksgezondheid en Milieu (RIVM), https://www.rivm.nl/gcn-gdn-kaarten (last access: 18 January 2024), 2020. a
Román Dobarco, M., Arrouays, D., Lagacherie, P., Ciampalini, R., and Saby, N. P. A.: Prediction of topsoil texture for Region Centre (France) applying model ensemble methods, Geoderma, 298, 67–77, https://doi.org/10.1016/j.geoderma.2017.03.015, 2017. a
Ros, G. H., Verweij, S. E., Janssen, S. J. C., De Haan, J., and Fujita, Y.: An Open Soil Health Assessment Framework Facilitating Sustainable Soil Management, Environ. Sci. Technol., 56, 17375–17384, https://doi.org/10.1021/acs.est.2c04516, 2022. a, b
Ros, G. H., Haan, J. J. d., Fuchs, L. M., and Molendijk, L.: Bodembeoordeling van landbouwgronden voor diverse ecosysteemdiensten: ontwikkeling van de BLN, versie 2.0, Rapport / Wageningen University & Research, Business unit Open Teelten WPR-OT-1030, Wageningen Plant Research, Wageningen, https://doi.org/10.18174/634579, 2023. a, b
Sandri, M. and Zuccolotto, P.: A Bias Correction Algorithm for the Gini Variable Importance Measure in Classification Trees, J. Comput. Graph. Stat., 17, 611–628, https://doi.org/10.1198/106186008X344522, 2008. a
Sandri, M. and Zuccolotto, P.: Analysis and correction of bias in Total Decrease in Node Impurity measures for tree-based algorithms, Stat. Comput., 20, 393–407, https://doi.org/10.1007/s11222-009-9132-0, 2010. a
Schmidinger, J. and Heuvelink, G. B. M.: Validation of uncertainty predictions in digital soil mapping, Geoderma, 437, 116585, https://doi.org/10.1016/j.geoderma.2023.116585, 2023. a, b, c
Schoumans, O. F.: Description of the Phosphorus Sorption and Desorption Processes in Lowland Peaty Clay Soils, Soil Sci., 178, 291, https://doi.org/10.1097/SS.0b013e31829ef054, 2013. a
Schoumans, O. F. and Chardon, W. J.: Phosphate saturation degree and accumulation of phosphate in various soil types in The Netherlands, Geoderma, 237–238, 325–335, https://doi.org/10.1016/j.geoderma.2014.08.015, 2015. a, b, c
Schulte, R. P. O., Bampa, F., Bardy, M., Coyle, C., Creamer, R. E., Fealy, R., Gardi, C., Ghaley, B. B., Jordan, P., Laudon, H., O'Donoghue, C., Ó'hUallacháin, D., O'Sullivan, L., Rutgers, M., Six, J., Toth, G. L., and Vrebos, D.: Making the Most of Our Land: Managing Soil Functions from Local to Continental Scale, Front. Environ. Sci., 3, 81, https://doi.org/10.3389/fenvs.2015.00081, 2015. a
Scull, P., Franklin, J., Chadwick, O. A., and McArthur, D.: Predictive soil mapping: a review, Prog. Phys. Geogr. Earth and Environment, 27, 171–197, https://doi.org/10.1191/0309133303pp366ra, 2003. a, b
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
Sekulić, A., Kilibarda, M., Heuvelink, G. B. M., Nikolić, M., and Bajat, B.: Random Forest Spatial Interpolation, Remote Sens.-Basel, 12, 1687, https://doi.org/10.3390/rs12101687, 2020. a
Stettler, M., Keller, T., Weisskopf, P., Lamandé, M., Lassen, P., and Schjønning, P.: Terranimo – a web-based tool for evaluating soil compaction, Landtechnik, 69, 132–138, 2014. a
Stokstad, E.: Nitrogen crisis threatens Dutch environment – and economy, Science, 366, 1180–1181, https://doi.org/10.1126/science.366.6470.1180, 2019. a
Szatmári, G., Pásztor, L., and Heuvelink, G. B. M.: Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics, Geoderma, 403, 115356, https://doi.org/10.1016/j.geoderma.2021.115356, 2021. a, b
Taghizadeh-mehrjardi, R., Toomanian, N., Khavaninzadeh, A. R., Jafari, A., and Triantafilis, J.: Predicting and mapping of soil particle-size fractions with adaptive neuro-fuzzy inference and ant colony optimization in central Iran, Eur. J. Soil Sci., 67, 707–725, https://doi.org/10.1111/ejss.12382, 2016. a
Teuling, K., Knotters, M., van Tol-Leenders, T. P., Lesschen, J. P., and Reijneveld, J. A.: Nieuwe steekproefomvang voor landelijke monitoring koolstof en bodemkwaliteit, Vervolg op rapportages CC-NL en De staat van de Nederlandse landbouwbodems in 2018, Tech. rep., Wageningen Environmental Research, Wageningen, 2021. a
Tobler, W. R.: A Computer Movie Simulating Urban Growth in the Detroit Region, Economic Geography, 46, 234–240, https://doi.org/10.2307/143141, 1970. a, b
United Nations: Transforming our World: The 2030 Agenda for Sustainable Development, Tech. rep., United Nations, New York, NY, 2015. a
van Asselen, S., Erkens, G., Stouthamer, E., Woolderink, H. A. G., Geeraert, R. E. E., and Hefting, M. M.: The relative contribution of peat compaction and oxidation to subsidence in built-up areas in the Rhine-Meuse delta, The Netherlands, Sci. Total Environ., 636, 177–191, https://doi.org/10.1016/j.scitotenv.2018.04.141, 2018. a
van Dam, J. C., Huygen, J., Wesseling, J. G., Feddes, R. A., and Kabat, P.: Theory of SWAP version 2.0; Simulation of waterflow, solute transport and plant growth in the Soil-Water-Atmosphere-Plant environment, Technical document 45, Wageningen Agricultural University and DLO Winand Staring Centre, Wageningen, the Netherlands, https://library.wur.nl/WebQuery/wurpubs/fulltext/222782 (last access: 19 January 2024), 1997. a
van Delft, B. and Maas, G.: Landschappelijke Bodemkartering (LBK): Achtergronden, toepassingen en technische documentatie, WOt-technical report 248, Wettelijke Onderzoekstaken Natuur & Milieu, Wageningen, https://doi.org/10.18174/641887, 2023. a, b
van den Akker, J. J. H. and Hoogland, T.: Comparison of risk assessment methods to determine the subsoil compaction risk of agricultural soils in The Netherlands, Soil Till. Res., 114, 146–154, https://doi.org/10.1016/j.still.2011.04.002, 2011. a
van den Akker, J. J. H., de Vries, F., Vermeulen, G., Hack-ten Broeke, M., and Schouten, T.: Risico op ondergrondverdichting in het landelijk gebied in kaart, Alterra-rapport 2409, Alterra, Wageningen, the Netherlands, 2012. a
van den Berg, F., Tiktak, A., Hoogland, T., Poot, A., Boesten, J., van der Linden, A. M. A., and Pol, J. W.: An improved soil organic matter map for GeoPEARL_NL: Model description of version 4.4.4 and consequence for the Dutch decision tree on leaching to groundwater, Tech. rep., Wageningen Environmental Research (Alterra), Wageningen, https://doi.org/10.18174/424920, 2017. a, b, c, d, e, f
van den Elsen, E., van Tol-Leenders, D., Teuling, K., Römkens, P., de Haan, J., Korthals, G., and Reijneveld, A.: De staat van de Nederlandse landbouwbodems in 2018: Op basis van beschikbare landsdekkende dataset (CC-NL) en bodem-indicatorenlijst (BLN), Tech. Rep. 3048, Wageningen Environmental Research, Wageningen, https://library.wur.nl/WebQuery/wurpubs/574884 (last access: 19 January 2024), 2020. a, b
van der Meulen, M., Doornenbal, J., Gunnink, J., Stafleu, J., Schokker, J., Vernes, R., van Geer, F., van Gessel, S., van Heteren, S., van Leeuwen, R., Bakker, M., Bogaard, P., Busschers, F., Griffioen, J., Gruijters, S., Kiden, P., Schroot, B., Simmelink, H., van Berkel, W., van der Krogt, R., Westerhoff, W., and van Daalen, T.: 3D geology in a 2D country: perspectives for geological surveying in the Netherlands, Netherlands J. Geosci., 92, 217–241, https://doi.org/10.1017/S0016774600000184, 2013. a
van der Westhuizen, S., Heuvelink, G. B. M., Hofmeyr, D. P., and Poggio, L.: Measurement error-filtered machine learning in digital soil mapping, Spatial Statistics, 47, 100572, https://doi.org/10.1016/j.spasta.2021.100572, 2022. a, b
van der Zee, S., van Riemsdijk, W., and de Haan, F.: Het protokol fosfaatverzadigde gronden I: toelichting, verslagen en mededelingen 1990-1A, Landbouwuniversiteit Wageningen, Landbouwuniversiteit Wageningen, Wageningen, https://edepot.wur.nl/394261 (last access: 19 January 2024), 1990. a
van Doorn, M., Helfenstein, A., Ros, G. H., Heuvelink, G. B. M., van Rotterdam-Los, D. A. M. D., Verweij, S. E., and de Vries, W.: High-resolution digital soil mapping of amorphous iron- and aluminium-(hydr)oxides to guide sustainable phosphorus and carbon management, Geoderma, 443, 116838, https://doi.org/10.1016/j.geoderma.2024.116838, 2024. a
van Leeuwen, C. C. E., Mulder, V. L., Batjes, N. H., and Heuvelink, G. B. M.: Statistical modelling of measurement error in wet chemistry soil data, Eur. J. Soil Sci., 73,e13137, https://doi.org/10.1111/ejss.13137, 2021. a
van Tol-Leenders, D., Knotters, M., Groot, W. d., Gerritsen, P., Reijneveld, A., van Egmond, F., Wösten, H., and Kuikman, P.: Koolstofvoorraad in de bodem van Nederland (1998–2018): CC-NL, Rapport 2974, Wageningen Environmental Research, Wageningen, https://doi.org/10.18174/509781, 2019. a, b
Varón-Ramírez, V. M., Araujo-Carrillo, G. A., and Guevara Santamaría, M. A.: Colombian soil texture: building a spatial ensemble model, Earth Syst. Sci. Data, 14, 4719–4741, https://doi.org/10.5194/essd-14-4719-2022, 2022. a
Vasenev, V. I., Stoorvogel, J. J., Vasenev, I. I., and Valentini, R.: How to map soil organic carbon stocks in highly urbanized regions?, Geoderma, 226–227, 103–115, https://doi.org/10.1016/j.geoderma.2014.03.007, 2014. a
Vasenev, V. I., Varentsov, M., Konstantinov, P., Romzaykina, O., Kanareykina, I., Dvornikov, Y., and Manukyan, V.: Projecting urban heat island effect on the spatial-temporal variation of microbial respiration in urban soils of Moscow megalopolis, Sci. Total Environ., 786, 147457, https://doi.org/10.1016/j.scitotenv.2021.147457, 2021. a
Vaysse, K. and Lagacherie, P.: Using quantile regression forest to estimate uncertainty of digital soil mapping products, Geoderma, 291, 55–64, https://doi.org/10.1016/j.geoderma.2016.12.017, 2017. a
Viscarra Rossel, R. A., Chen, C., Grundy, M. J., Searle, R., Clifford, D., and Campbell, P. H.: The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project, Soil Res., 53, 845, https://doi.org/10.1071/SR14366, 2015. a
Visschers, R., Finke, P. A., and de Gruijter, J. J.: A soil sampling program for the Netherlands, Geoderma, 139, 60–72, https://doi.org/10.1016/j.geoderma.2007.01.008, 2007. a
von Hippel, P. T.: Mean, Median, and Skew: Correcting a Textbook Rule, J. Stat. Educ., 13, 2, https://doi.org/10.1080/10691898.2005.11910556, 2005. a
Vos, P.: Origin of the Dutch coastal landscape: long-term landscape evolution of the Netherlands during the Holocene, described and visualized in national, regional and local palaeogeographical map series, Barkhuis, Groningen, ISBN 978-94-91431-82-1, 2015. a
Vos, P., Meulen, M. V. D., Weerts, H., and Bazelmans, J.: Atlas of the Holocene Netherlands, landscape and habitation since the last ice age, Amsterdam University Press, Amsterdam, https://www.cultureelerfgoed.nl/onderwerpen/bronnen-en-kaarten/overzicht/paleografische-kaarten (last access: 17 March 2020), 2020. a
Wadoux, A. M. J. C.: Using deep learning for multivariate mapping of soil with quantified uncertainty, Geoderma, 351, 59–70, https://doi.org/10.1016/j.geoderma.2019.05.012, 2019. a, b, c
Wadoux, A. M. J.-C. and Heuvelink, G. B. M.: Uncertainty of spatial averages and totals of natural resource maps, Methods Ecol. Evolut., 14, 1320–1332, https://doi.org/10.1111/2041-210X.14106, 2023. a
Wadoux, A. M. J.-C., Padarian, J., and Minasny, B.: Multi-source data integration for soil mapping using deep learning, SOIL, 5, 107–119, https://doi.org/10.5194/soil-5-107-2019, 2019. a
Wadoux, A. M. J. C., Heuvelink, G. B. M., de Bruin, S., and Brus, D. J.: Spatial cross-validation is not the right way to evaluate map accuracy, Ecol. Model., 457, 109692, https://doi.org/10.1016/j.ecolmodel.2021.109692, 2021a. a, b
Wadoux, A. M. J. C., Heuvelink, G. B. M., Lark, R. M., Lagacherie, P., Bouma, J., Mulder, V. L., Libohova, Z., Yang, L., and McBratney, A. B.: Ten challenges for the future of pedometrics, Geoderma, 401, 115155, https://doi.org/10.1016/j.geoderma.2021.115155, 2021b. a, b, c, d
Walvoort, D. J. J. and de Gruijter, J. J.: Compositional Kriging: A Spatial Interpolation Method for Compositional Data, Math. Geol., 33, 951–966, 2001. a
Wamelink, G. W. W., Walvoort, D. J. J., Sanders, M. E., Meeuwsen, H. A. M., Wegman, R. M. A., Pouwels, R., and Knotters, M.: Prediction of soil pH patterns in nature areas on a national scale, Appl. Veg. Sci., 22, 189–199, https://doi.org/10.1111/avsc.12423, 2019. a
Wang, Z. and Shi, W.: Mapping soil particle-size fractions: A comparison of compositional kriging and log-ratio kriging, J. Hydrol., 546, 526–541, https://doi.org/10.1016/j.jhydrol.2017.01.029, 2017. a
Withers, P. J. A., Sylvester-Bradley, R., Jones, D. L., Healey, J. R., and Talboys, P. J.: Feed the Crop Not the Soil: Rethinking Phosphorus Management in the Food Chain, Environ. Sci. Technol., 48, 6523–6530, https://doi.org/10.1021/es501670j, 2014. a
Wright, M. N. and Ziegler, A.: ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R, J. Stat. Softw., 77, 1–17, https://doi.org/10.18637/jss.v077.i01, 2017. a
Short summary
Earth system models and decision support systems greatly benefit from high-resolution soil information with quantified accuracy. Here we introduce BIS-4D, a statistical modeling platform that predicts nine essential soil properties and their uncertainties at 25 m resolution in surface 2 m across the Netherlands. Using machine learning informed by up to 856 000 soil observations coupled with 366 spatially explicit environmental variables, prediction accuracy was the highest for clay, sand and pH.
Earth system models and decision support systems greatly benefit from high-resolution soil...
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