References

Amatulli, Gi., McInerney, D., Sethi, T., Strobl, P. & Domisch, S. 2020. Geomorpho90m - global high-resolution geomorphometry layers. OpenTopography. https://doi.org/10.5069/G91R6NPX

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., 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., Pasztor, 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., Conceicao Gonsalves, M. da, Theocharopoulos, S., Yemefack, M., Tedou, S., Vrscaj, B., Grob, U., Kozak, J., Boruvka, L., Dobos, E., Taboada, M., Moretti, L. & Rodriguez, D. 2017. Soil legacy data rescue via GlobalSoilMap and other international and national initiatives. GeoResJ, 14: 1–19. https://doi.org/https://doi.org/10.1016/j.grj.2017.06.001

Bishop, T.F.A., McBratney, A.B. & Laslett, G.M. 1999. Modelling soil attribute depth functions with equal-area quadratic smoothing splines, 91: 27–45. https://doi.org/10.1016/s0016-7061(99)00003-8

Borden, K.A., Thomas, J.E. & Rivard, C. 2020. Evaluation of diverse cover crop mixtures for use in semi-arid irrigated production systems. Renewable Agriculture and Food Systems, 35(3): 286–296. https://doi.org/10.1017/S1742170518000509

Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. 1984. Classification and regression trees. Belmont, CA, Wadsworth.

Breure, T., Jones, A. & Panagos, P. 2026. Evaluating visible near-infrared spectroscopy in context of a repeated sampling survey across the european union. Geoderma, 465: 117647.

Brevik, E.C., Calzolari, C., Miller, B.A., Pereira, P., Kabala, C., Baumgarten, A. & Jordán, A. 2016. Soil mapping, classification, and pedologic modeling: History and future directions. Geoderma, 264: 256–274. https://doi.org/10.1016/j.geoderma.2015.05.017

Brus, D.J. 2022 b. 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

Brus, D.J. 2022 a. Spatial sampling with r. Boca Raton, FL, Chapman & Hall/CRC Press.

Brus, D.J., Kempen, B. & Heuvelink, G.B.M. 2011 b. Sampling for validation of digital soil maps. European Journal of Soil Science, 62(3): 394–407. https://doi.org/10.1111/j.1365-2389.2011.01364.x

Brus, D.J., Kempen, B. & Heuvelink, G.B.M. 2011 a. 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

Bui, E. 2020. Soil survey as a knowledge system. Geoderma, 359: 113948. https://doi.org/10.1016/j.geoderma.2019.113948

Chen, S., Saby, N.P.A., Martin, M.P., Barthes, B.G., Gomez, C., Shi, Z. & Arrouays, D. 2023. Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping. Geoderma, 433: 116467.

Clay, D.E., Chang, J., Malo, D.D., Carlson, C.G., Reese, C., Clay, S.A., Ellsbury, M. & Berg, B. 2001. Factors influencing spatial variability of soil apparent electrical conductivity. Communications in Soil Science and Plant Analysis, 32(19-20): 2993–3008. https://doi.org/10.1081/CSS-120001104

Dangal, S.R., Sanderman, J., Wills, S. & Ramirez-Lopez, L. 2019. Accurate and precise prediction of soil properties from a large mid-infrared spectral library. Soil Systems, 3(1): 11.

Gobin, A., Campling, P. & Feyen, J. 2001. Soil-landscape modelling to quantify spatial variability of soil texture. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(1): 41–45. https://doi.org/10.1016/S1464-1909(01)85011-7

Gruijter, J.J. de, Brus, D.J., Bierkens, M.F.P. & Knotters, M. 2006. Sampling for natural resource monitoring. Berlin, Springer.

Gupta, S. & Hengl, T. 2019. Soil available water capacity in mm derived for 5 standard layers (0-10, 10-30, 30-60, 60-100 and 100-200 cm) at 250 m resolution. Zenodo. https://doi.org/10.5281/zenodo.2629148

Hengl, T., Heuvelink, G.B.M. & Rossiter, D.G. 2007. About regression-kriging: From equations to case studies. Computers & Geosciences, 33: 1301–1315. https://doi.org/10.1016/j.cageo.2007.05.001

Hengl, T. & MacMillan, R.A. 2019. Predictive soil mapping with r. Wageningen, the Netherlands, OpenGeoHub foundation. (also available at https://soilmapper.org/).

Hengl, T., Nussbaum, M., Wright, M.N., Heuvelink, G.B. & Gräler, B. 2018. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 6: e5518.

Heuvelink, G.B.M., Kros, J., Reinds, G.J. & De Vries, W. 2016. Geostatistical prediction and simulation of european soil property maps. Geoderma Regional, 7: 201–215. https://doi.org/10.1016/j.geodrs.2016.04.002

Heuvelink, G.B.M. & Webster, R. 2001. Modelling soil variation: Past, present, and future. Geoderma, 100: 269–301. https://doi.org/10.1016/S0016-7061(01)00025-8

Jenny, H. 1941 b. A system of quantitative pedology. In “factors of soil formation”. McGraw Hill: New York.

Jenny, H. 1941 a. Factors of soil formation. A system of quantitative pedology. New York, McGraw-Hill.

Kempen, B., Brus, D.J., Heuvelink, G.B.M. & Stoorvogel, J.J. 2009. 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

Lagacherie, P. 2008. Digital soil mapping: A state of the art. In A.E. Hartemink, A.B. McBratney & M.L. Mendonça-Santos, eds. Digital soil mapping with limited data, pp. 3–14. Dordrecht, Springer.

Lagacherie, P. & McBratney, A.B. 2006. Spatial soil information systems and spatial soil inference systems: Perspectives for digital soil mapping. In P. Lagacherie, A.B. McBratney & M. Voltz, eds. Digital soil mapping: An introductory perspective, pp. 3–22. Amsterdam, Elsevier.

Ma, Y., Minasny, B., Weerts, A., Poggio, L., Malone, B. & McBratney, A. 2020. Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps. Geoderma, 370: 114366. https://doi.org/10.1016/j.geoderma.2020.114366

Malone, B., Stockmann, U., Glover, M., McLachlan, G., Engelhardt, S. & Tuomi, S. 2022. Digital soil survey and mapping underpinning inherent and dynamic soil attribute condition assessments. Soil Security, 6: 100048.

McBratney, A.B., Mendonça Santos, M.L. & Minasny, B. 2003. On digital soil mapping. Geoderma, 117: 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4

Medeiros, B.M., Rossi, L.S., Caten, A.T., Pereira, G.E., Silva, E.B. & U., Daboit.K.T. 2024. Soil legacy data: An opportunity for digital soil mapping. Rev. Bras. Ciênc. Solo, 48. https://doi.org/10.36783/18069657rbcs20230130

Milne, G. 1936. Normal erosion as a factor in soil profile development. Nature, 138: 548–549. https://doi.org/10.1038/138548c0

Minasny, B. & McBratney, A.B. 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences, 32(9): 1378–1388. https://doi.org/10.1016/j.cageo.2005.12.009

Minasny, B. & McBratney, A.B. 2016. Digital soil mapping: A brief history and some lessons. Geoderma, 264: 301–311. https://doi.org/10.1016/j.geoderma.2015.07.017

Morton, P.A. & Heinemeyer, O. 2000. Soil sampling and preparation. Methods in Ecosystem Science: 85–94.

Næs, T. 1987. The design of calibration in near infra-red reflectance analysis by clustering. Journal of chemometrics, 1(2): 121–134.

Newhall, F. & Berdanier, C.R. 1996. Calculation of soil moisture regimes from the climatic record, p. 13. No. 46. Natural Resources Conservation Service.

Norris, C.E., Quideau, S.A., Landhausser, S.M., Bernard, G.M. & Wasylishen, R.E. 2013. Tracking stable isotope enrichment in tree seedlings with solid-state \(^{15}\)N NMR spectroscopy. Biogeosciences, 10: 3433–3445. https://doi.org/10.5194/bg-10-3433-2013

Odeh, I.O.A., McBratney, A.B. & Chittleborough, D.J. 1995. Further results on prediction of soil properties from terrain attributes: Heterotopic cokriging and regression-kriging. Geoderma, 67: 215–226. https://doi.org/10.1016/0016-7061(95)00007-B

Odgers, N.P., McBratney, A.B. & Minasny, B. 2011. Bottom-up digital soil mapping. I. Soil layer classes. Geoderma, 163: 38–44. https://doi.org/10.1016/j.geoderma.2011.03.014

Orton, T.G., Pringle, M.J., Page, K.L., Dalal, R.C. & Bishop, T.F.A. 2014. Spatial prediction of soil organic carbon stock using a linear model of coregionalisation. Geoderma, 230–231: 119–130. https://doi.org/10.1016/j.geoderma.2014.04.016

Padarian, J. & McBratney, A.B. 2023. QuadMap: Variable resolution maps to communicate uncertainty. Computers and Geosciences, 181: 105480. https://doi.org/10.1016/j.cageo.2023.105480

Pebesma, E. & Bivand, R. 2021. Spatial data science with applications in r. Methods in Ecology and Evolution, 12: 1620–1633. https://doi.org/10.1111/2041-210X.13650

Peng, Y., Ben-Dor, E., Biswas, A., Chabrillat, S., Demattê, J.A., Ge, Y., Gholizadeh, A., Gomez, C., Guerrero, C., Herrick, J. & others. 2025. Spectroscopic solutions for generating new global soil information. The Innovation, 6(5).

Prieto-Castrillo, F., Rodríguez-Rastrero, M., Yunta, F., Borondo, F. & Borondo, J. 2023. Disentangling jenny’s equation by machine learning. Scientific Reports, 13(1): 20916. https://doi.org/10.1038/s41598-023-44171-x

Ramirez-Lopez, L., Metz, M., Lesnoff, M., Orellano, C., Perez-Fernandez, E., Plans, M., Breure, T., Behrens, T., Viscarra Rossel, R. & Peng, Y. 2026. Rethinking local spectral modelling: From per-query refitting to model libraries. Analytica Chimica Acta.

Ramirez-Lopez, L., Schmidt, K., Behrens, T., Van Wesemael, B., Demattê, J.A. & Scholten, T. 2014. Sampling optimal calibration sets in soil infrared spectroscopy. Geoderma, 226: 140–150.

Ramirez-Lopez, L., Wadoux, A.-C., Franceschini, M.H., Terra, F., Marques, K.P.P., Sayão, V.M. & Demattê, J.A.M. 2019. Robust soil mapping at the farm scale with vis–NIR spectroscopy. European Journal of Soil Science, 70(2): 378–393.

Robertson, B.L., Brown, J.A., McDonald, T. & Jaksons, P. 2013. BAS: Balanced acceptance sampling of natural resources. Biometrics, 69(3): 776–784. https://doi.org/10.1111/biom.12059

Safanelli, J.L., Hengl, T., Parente, L.L., Minarik, R., Bloom, D.E., Todd-Brown, K., Gholizadeh, A., Mendes, W. de S. & Sanderman, J. 2025. Open soil spectral library (OSSL): Building reproducible soil calibration models through open development and community engagement. PloS one, 20(1): e0296545.

Saurette, D.D., Berg, A., Laamrani, A., Heck, R.J., Gillespie, A.W. & Biswas, A. 2023. Determining optimal sample size for soil spectroscopy applications: Learning curves and model validation. Soil Science Society of America Journal, 87(6): 1557–1572. https://doi.org/10.1002/saj2.20608

Schmidinger, J., Heuvelink, G.B.M. & Brus, D.J. 2024. Sampling design optimization for soil mapping with random forest. Geoderma, 444: 116849. https://doi.org/10.1016/j.geoderma.2024.116849

Stevens, A., Nocita, M., Tóth, G., Montanarella, L. & Wesemael, B. van. 2013. Prediction of soil organic carbon at the european scale by visible and near infrared reflectance spectroscopy. PloS one, 8(6): e66409.

Stevens, D.L. & Olsen, A.R. 2004. Spatially balanced sampling of natural resources. Journal of the American Statistical Association, 99(465): 262–278. https://doi.org/10.1198/016214504000000250

Styc, Q. & Lagacherie, P. 2021. Combining spatial-environmental covariates and soil input data types to predict soil available water holding capacity. Geoderma, 401: 115171. https://doi.org/10.1016/j.geoderma.2021.115171

Takoutsing, B., Heuvelink, G.B., Stoorvogel, J.J., Shepherd, K.D. & Aynekulu, E. 2022. Accounting for analytical and proximal soil sensing errors in digital soil mapping. European Journal of Soil Science, 73(2): e13226.

Viscarra Rossel, R.A., Behrens, T., Ben-Dor, E., Chabrillat, S., Demattê, J.A.M., Ge, Y., Gomez, C., Guerrero, C., Peng, Y., Ramirez-Lopez, L. & others. 2022. Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century. European Journal of Soil Science, 73(4): e13271.

Viscarra Rossel, R., Chappell, A., De Caritat, P. & McKenzie, N. 2011. On the soil information content of visible–near infrared reflectance spectra. European Journal of Soil Science, 62(3): 442–453.

Wadoux, A.M.-C. & Ramirez-Lopez, L. 2025. Uncertainty of predictions in absorption spectroscopy: Modelling with quantile regression forest. Chemometrics and Intelligent Laboratory Systems, 265: 105473.

Wadoux, A.M.J.-C., Heuvelink, G.B.M., Bruin, S. de & Brus, D.J. 2021. Spatial cross-validation is not the right way to evaluate map accuracy, 457: 109692. https://doi.org/10.1016/j.ecolmodel.2021.109692

Wadoux, A.M.J.-C., Minasny, B. & McBratney, A.B. 2021. Machine learning for digital soil mapping: Applications, challenges and suggested solutions. Earth-Science Reviews, 210: 103359. https://doi.org/10.1016/j.earscirev.2020.103359

Wadoux, A.M.J.-C., Padarian, J. & Minasny, B. 2019. Multi-source data integration for soil mapping using deep learning. Soil, 5(1): 107–119.

Wadoux, A.M.J.-C., Ramirez-Lopez, L., Ge, Y., Barra, I. & Peng, Y. 2025. A course on applied data analytics for soil analysis with infrared spectroscopy – soil spectroscopy training manual 2. Rome, Italy, Food; Agriculture Organization of the United Nations (FAO).

Webster, R., Lessells, C.M. & Hodgson, J.M. 1979. A reconnaissance soil survey of the ivybridge area of south devon. Soil Survey Technical Monograph, 12.

Webster, R. & Oliver, M.A. 2007. Geostatistics for environmental scientists. 2nd edition. Chichester, John Wiley & Sons.

Westhuizen, S. van der, Heuvelink, G.B.M., Hofmeyr, D.P. & Poggio, L. 2022. Measurement error-filtered machine learning in digital soil mapping. Spatial Statistics, 47: 100572.

Wright, M.N. & Ziegler, A. 2017. Ranger: A fast implementation of random forests for high dimensional data in c++ and r. Journal of statistical software, 77: 1–17.

Wu, C.-Y., Wu, P.-H. & Hseu, Z.-Y. 2025. Assessing the robustness of VIS-NIR spectroscopy-based soil organic carbon prediction against four wet chemistry methods. Carbon Management, 16(1): 2511337.

Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.-E., Xu, P., Ramoino, F. & Arino, O. 2022. ESA WorldCover 10 m 2021 v200. Zenodo. https://doi.org/10.5281/zenodo.7254221

Zhang, L., Heuvelink, G.B.M., Mulder, V.L., Chen, S., Deng, X. & Yang, L. 2024. Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time. Science of the Total Environment, 922: 170778. https://doi.org/10.1016/j.scitotenv.2024.170778