Supply Chain Council of European Union | Scceu.org
Procurement

Contextual spatial modelling in the horizontal and vertical domains

  • Godfray, H. C. J. et al. Food security: The challenge of feeding 9 billion people. Science 327, 812–818. https://doi.org/10.1126/science.1185383 (2010).

    ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar
     

  • Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Quéré, C., Myneni, R. B., Piao, S., Thornt, P. Carbon and Other Biogeochemical Cycles. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (ed Stocker, T. F. et al.) 465–470 (Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2013).

  • Bouma, J. Soil science contributions towards Sustainable Development Goals and their implementation: Linking soil functions with ecosystem services. J. Plant Nutr. Soil Sci. 177, 111–120. https://doi.org/10.1002/jpln.201300646 (2014).

    CAS 
    Article 

    Google Scholar
     

  • Jenny, H. Factors f Soil Formation. A System of Quantitative Pedology (McGraw-Hill Book Company, New York, 1941).


    Google Scholar
     

  • Matheron, G. Principles of geostatistics. Econ. Geol. 58, 1246–1266. https://doi.org/10.2113/gsecongeo.58.8.1246 (1963).

    CAS 
    Article 

    Google Scholar
     

  • Dokuchaev, V. V. The Russian Chernozem (St. Petersburg, 1883).

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

    ADS 
    Article 

    Google Scholar
     

  • Behrens, T. et al. Hyper-scale digital soil mapping and soil formation analysis. Geoderma 213, 578–588. https://doi.org/10.1016/j.geoderma.2013.07.031 (2014).

    ADS 
    Article 

    Google Scholar
     

  • Krige, D. G. A statistical approach to some basic mine valuation problems on the Witwatersrand. J. Chem. Metall. Min. Soc. S. Afr. 52, 119–139 (1951).


    Google Scholar
     

  • Burgess, T. M. & Webster, R. Optimal interpolation and isarithmic mapping of soil properties. J. Soil Sci. 31, 333–341. https://doi.org/10.1111/j.1365-2389.1980.tb02085.x (1980).

    Article 

    Google Scholar
     

  • Aitkenhead, M. J. & Aalders, I. H. Predicting land cover using GIS, Bayesian and evolutionary algorithm methods. J. Environ. Manag. 90, 236–250. https://doi.org/10.1016/j.jenvman.2007.09.010 (2009).

    CAS 
    Article 

    Google Scholar
     

  • Banerjee, S., Carlin, B. P. & Gelfand, A. E. Hierarchical Modeling and Analysis for Spatial Data 2nd edn. (CRC Press, Boca Raton, 2015).

    MATH 

    Google Scholar
     

  • Behrens, T., MacMillan, R. A., Viscarra Rossel, R. A., Schmidt, K. & Lee, J. Teleconnections in spatial modelling. Geoderma 354, 113854. https://doi.org/10.1016/j.geoderma.2019.07.012 (2019).

    ADS 
    Article 

    Google Scholar
     

  • MacMillan, R., Jones, R. & McNabb, D. H. Defining a hierarchy of spatial entities for environmental analysis and modeling using digital elevation models (DEMs). Comput. Environ. Urban Syst. 28, 175–200. https://doi.org/10.1016/S0198-9715(03)00019-X (2004).

    Article 

    Google Scholar
     

  • Behrens, T., Zhu, A.-X., Schmidt, K. & Scholten, T. Multi-scale digital terrain analysis and feature selection for digital soil mapping. Geoderma 155, 175–185. https://doi.org/10.1016/j.geoderma.2009.07.010 (2010).

    ADS 
    Article 

    Google Scholar
     

  • Behrens, T., Schmidt, K., Zhu, A. X. & Scholten, T. The ConMap approach for terrain-based digital soil mapping. Eur. J. Soil Sci. 61, 133–143. https://doi.org/10.1111/j.1365-2389.2009.01205.x (2010).

    Article 

    Google Scholar
     

  • Behrens, T., Schmidt, K., MacMillan, R. A. & Viscarra Rossel, R. A. Multiscale contextual spatial modelling with the Gaussian scale space. Geoderma 310, 128–137. https://doi.org/10.1016/j.geoderma.2017.09.015 (2018).

    ADS 
    Article 

    Google Scholar
     

  • Behrens, T., Schmidt, K., MacMillan, R. A. & 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 (2018).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rentschler, T. et al. Comparison of catchment scale 3D and 2.5D modelling of soil organic carbon stocks in Jiangxi Province, PR China. PLoS ONE 14, e0220881. https://doi.org/10.1371/journal.pone.0220881 (2019).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Behrens, T. et al. The relevant range of scales for multi-scale contextual spatial modelling. Sci. Rep. 9, 14800. https://doi.org/10.1038/s41598-019-51395-3 (2019).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204. https://doi.org/10.1038/s41586-019-0912-1 (2019).

    ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar
     

  • Kerry, R. & Oliver, M. A. Soil geomorphology: Identifying relations between the scale of spatial variation and soil processes using the variogram. Geomorphology 130, 40–54. https://doi.org/10.1016/j.geomorph.2010.10.002 (2011).

    ADS 
    Article 

    Google Scholar
     

  • Jobbágy, E. G. & Jackson, R. B. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol. Appl. 10, 423–436 (2000).

    Article 

    Google Scholar
     

  • Taghizadeh-Mehrjardi, R. et al. Improving the spatial prediction of soil organic carbon content in two contrasting climatic regions by stacking machine learning models and rescanning covariate space. Remote Sens. 12, 1095. https://doi.org/10.3390/rs12071095 (2020).

    ADS 
    Article 

    Google Scholar
     

  • Murphy, B. W., Wilson, B. R. & Koen, T. Mathematical functions to model the depth distribution of soil organic carbon in a range of soils from New South Wales, Australia under different land uses. Soil Syst. 3, 46. https://doi.org/10.3390/soilsystems3030046 (2019).

    CAS 
    Article 

    Google Scholar
     

  • Aldana Jague, E. et al. High resolution characterization of the soil organic carbon depth profile in a soil landscape affected by erosion. Soil Tillage Res. 156, 185–193. https://doi.org/10.1016/j.still.2015.05.014 (2016).

    Article 

    Google Scholar
     

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

    ADS 
    Article 

    Google Scholar
     

  • Rentschler, T. et al. 3D mapping of soil organic carbon content and soil moisture with multiple geophysical sensors and machine learning. Vadose Zone J. https://doi.org/10.1002/vzj2.20062 (2020).

    Article 

    Google Scholar
     

  • Moghadas, D., Taghizadeh-Mehrjardi, R. & Triantafilis, J. Probabilistic inversion of EM38 data for 3D soil mapping in central Iran. Geoderma Reg. 7, 230–238. https://doi.org/10.1016/j.geodrs.2016.04.006 (2016).

    Article 

    Google Scholar
     

  • Civis, J. et al. Cuenza del guadalquvir. In Geológica de España (ed. Vera, J. A.) 543–550 (Igme, Maerid, 2004).


    Google Scholar
     

  • Wolf, D. & Faust, D. Western Mediterranean environmental changes: Evidences from fluvial archives. Quat. Sci. Rev. 122, 30–50. https://doi.org/10.1016/j.quascirev.2015.04.016 (2015).

    ADS 
    Article 

    Google Scholar
     

  • Aguirre, J. et al. An enigmatic kilometer-scale concentration of small mytilids (Late Miocene, Guadalquivir Basin, S Spain). Palaeogeogr. Palaeoclimatol. Palaeoecol. 436, 199–213. https://doi.org/10.1016/j.palaeo.2015.07.015 (2015).

    Article 

    Google Scholar
     

  • Gómez-Miguel, V. Mapa de Suelos de España (Centro Nacional de Información Geográfica (CNIG), Madrid, 2005).


    Google Scholar
     

  • QGIS Development Team. QGIS Geographic Information System (QGIS Association, 2020).

  • Jasiewicz, J. & Stepinski, T. F. Geomorphons—a pattern recognition approach to classification and mapping of landforms. Geomorphology 182, 147–156. https://doi.org/10.1016/j.geomorph.2012.11.005 (2013).

    ADS 
    Article 

    Google Scholar
     

  • Viscarra Rossel, R. A., Walvoort, D., McBratney, A. B., Janik, L. J. & Skjemstad, J. O. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131, 59–75. https://doi.org/10.1016/j.geoderma.2005.03.007 (2006).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • Stevens, A. & Ramirez-Lopez, L. An introduction to the prospectr package (2014).

  • Khaledian, Y. et al. Modeling soil cation exchange capacity in multiple countries. CATENA 158, 194–200. https://doi.org/10.1016/j.catena.2017.07.002 (2017).

    CAS 
    Article 

    Google Scholar
     

  • Tóth, B. et al. New generation of hydraulic pedotransfer functions for Europe. Eur. J. Soil Sci. 66, 226–238. https://doi.org/10.1111/ejss.12192 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Hazelton, P. & Murphy, B. Interpreting Soil Test Results. What do all the Numbers Mean? (CSIRO Publishing, Clayton South, 2007).

    Book 

    Google Scholar
     

  • Pulido, M., Schnabel, S., Contador, J. F. L., Lozano-Parra, J. & Gómez-Gutiérrez, Á. Selecting indicators for assessing soil quality and degradation in rangelands of Extremadura (SW Spain). Ecol. Indic. 74, 49–61. https://doi.org/10.1016/j.ecolind.2016.11.016 (2017).

    CAS 
    Article 

    Google Scholar
     

  • Pebesma, E. J. Multivariable geostatistics in S: The gstat package. Comput. Geosci. 30, 683–691. https://doi.org/10.1016/j.cageo.2004.03.012 (2004).

    ADS 
    Article 

    Google Scholar
     

  • Burt, P. J. & Adelson, E. H. The Laplacian pyramid as a compact image Code. IEEE Trans. Commun. 31, 532–540. https://doi.org/10.1109/TCOM.1983.1095851 (1983).

    Article 

    Google Scholar
     

  • Zevenbergen, L. W. & Thorne, C. R. Quantitative analysis of land surface topography. Earth Surf. Process. Landf. 12, 47–56. https://doi.org/10.1002/esp.3290120107 (1987).

    ADS 
    Article 

    Google Scholar
     

  • Grimm, R., Behrens, T., Märker, M. & Elsenbeer, H. Soil organic carbon concentrations and stocks on Barro Colorado Island—Digital soil mapping using Random Forests analysis. Geoderma 146, 102–113. https://doi.org/10.1016/j.geoderma.2008.05.008 (2008).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J. Classification and Regression Trees (Chapman and Hall, 1984).

    MATH 

    Google Scholar
     

  • Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).

    Article 
    MATH 

    Google Scholar
     

  • R Core Team. R: A Language and Environment for Statistical Computing (Vienna, Austria, 2021).

  • RStudio Team. RStudio: Integrated Development Environment for R. Available at http://www.rstudio.com/ (Boston, MA, 2021).

  • Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).


    Google Scholar
     

  • Schmidt, K., Behrens, T. & Scholten, T. Instance selection and classification tree analysis for large spatial datasets in digital soil mapping. Geoderma 146, 138–146. https://doi.org/10.1016/j.geoderma.2008.05.010 (2008).

    ADS 
    Article 

    Google Scholar
     

  • 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).

    Article 

    Google Scholar
     

  • Behrens, T. & Viscarra Rossel, R. A. On the interpretability of predictors in spatial data science: The information horizon. Sci. Rep. 10, 16737. https://doi.org/10.1038/s41598-020-73773-y (2020).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Caro Gómez, J. A., Del Díaz Olmo, F., Artigas, R. C., Recio Espejo, J. M. & Barrera, C. B. Geoarchaeological alluvial terrace system in Tarazona: Chronostratigraphical transition of Mode 2 to Mode 3 during the middle-upper pleistocene in the Guadalquivir River valley (Seville, Spain). Quat. Int. 243, 143–160. https://doi.org/10.1016/j.quaint.2011.04.022 (2011).

    Article 

    Google Scholar
     

  • Schaller, M. et al. Spatial and temporal variations in denudation rates derived from cosmogenic nuclides in four European fluvial terrace sequences. Geomorphology 274, 180–192. https://doi.org/10.1016/j.geomorph.2016.08.018 (2016).

    ADS 
    Article 

    Google Scholar
     

  • Finné, M., Holmgren, K., Sundqvist, H. S., Weiberg, E. & Lindblom, M. Climate in the eastern Mediterranean, and adjacent regions, during the past 6000 years—A review. J. Archaeol. Sci. 38, 3153–3173. https://doi.org/10.1016/j.jas.2011.05.007 (2011).

    Article 

    Google Scholar
     

  • Vogel, H.-J. et al. Quantitative evaluation of soil functions: Potential and state. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2019.00164 (2019).

    Article 

    Google Scholar
     

  • Amundson, R. Factors of soil formation in the 21st century. Geoderma 391, 114960. https://doi.org/10.1016/j.geoderma.2021.114960 (2021).

    ADS 
    Article 

    Google Scholar
     

  • Diacono, M. & Montemurro, F. Long-term effects of organic amendments on soil fertility. A review. Agron. Sustain. Dev. 30, 401–422. https://doi.org/10.1051/agro/2009040 (2010).

    CAS 
    Article 

    Google Scholar
     

  • Amundson, R., Heimsath, A., Owen, J., Yoo, K. & Dietrich, W. E. Hillslope soils and vegetation. Geomorphology 234, 122–132. https://doi.org/10.1016/j.geomorph.2014.12.031 (2015).

    ADS 
    Article 

    Google Scholar
     

  • Pike, R. J. The geometric signature: Quantifying landslide-terrain types from digital elevation models. Math. Geol. 20, 491–511. https://doi.org/10.1007/BF00890333 (1988).

    Article 

    Google Scholar
     

  • Lark, R. M. & Webster, R. Analysing soil variation in two dimensions with the discrete wavelet transform. Eur. J. Soil Sci. 55, 777–797. https://doi.org/10.1111/j.1365-2389.2004.00630.x (2004).

    Article 

    Google Scholar
     

  • Schmidt, K., Behrens, T., Friedrich, K. & Scholten, T. A method to generate soilscapes from soil maps. J. Plant Nutr. Soil Sci. 173, 163–172. https://doi.org/10.1002/jpln.200800208 (2010).

    CAS 
    Article 

    Google Scholar
     

  • Scholten, T. et al. On the combined effect of soil fertility and topography on tree growth in subtropical forest ecosystems—a study from SE China. J. Plant Ecol. 10, 111–127. https://doi.org/10.1093/jpe/rtw065 (2017).

    Article 

    Google Scholar
     

  • Mar Delgado-Serrano, M. & Ángel Hurtado-Martos, J. Land use changes in Spain. Drivers and trends in agricultural land use. EU Agrar. Law 7, 1–8. https://doi.org/10.2478/eual-2018-0006 (2018).

    Article 

    Google Scholar
     

  • Kühn, P., Lehndorff, E. & Fuchs, M. Lateglacial to Holocene pedogenesis and formation of colluvial deposits in a loess landscape of Central Europe (Wetterau, Germany). CATENA 154, 118–135. https://doi.org/10.1016/j.catena.2017.02.015 (2017).

    Article 

    Google Scholar
     

  • Scherer, S. et al. Middle Bronze Age land use practices in the northwestern Alpine foreland—a multi-proxy study of colluvial deposits, archaeological features and peat bogs. Soil 7, 269–304. https://doi.org/10.5194/soil-7-269-2021 (2021).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • Pickett, S. T. A. Space-for-time substitution as an alternative to long-term studies. In Long-Term Studies in Ecology (ed. Likens, G. E.) 110–135 (Springer, New York, 1989). https://doi.org/10.1007/978-1-4615-7358-6_5.

    Chapter 

    Google Scholar
     

  • Blois, J. L., Williams, J. W., Fitzpatrick, M. C., Jackson, S. T. & Ferrier, S. Space can substitute for time in predicting climate-change effects on biodiversity. Proc. Natl. Acad. Sci. 110, 9374–9379. https://doi.org/10.1073/pnas.1220228110 (2013).

    ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Related posts

    The fastest growing consumer plastic isn’t being recycled. Two Philadelphia area companies hope to change that.

    scceu

    DOE getting closer to new contractor for WIPP operations oversight

    scceu

    The Eleventh Circuit Cleans Up the Mess

    scceu