Digital soil mapping

Digital Soil Mapping (DSM) in soil science, also referred to as predictive soil mapping[1] or pedometric mapping, is the computer-assisted production of digital maps of soil types and soil properties. Soil mapping, in general, involves the creation and population of spatial soil information by the use of field and laboratory observational methods coupled with spatial and non-spatial soil inference systems.

The international WORKING GROUP ON DIGITAL SOIL MAPPING (WG-DSM) defines digital soil mapping as "the creation and the population of a geographically referenced soil databases generated at a given resolution by using field and laboratory observation methods coupled with environmental data through quantitative relationships." [2][3][4][5]

Ambiguities

DSM can rely upon, but is considered to be distinct from traditional soil mapping, which involves manual delineation of soil boundaries by field soil scientists. Non-digital soil maps produced as result of manual delineation of soil mapping units may also be digitized or surveyors may draw boundaries using field computers, hence both traditional, knowledge-based and technology and data-driven soil mapping frameworks are in essence digital. Unlike traditional soil mapping, Digital Soil Mapping is, however, considered to make an extensive use of:

  1. technological advances, including GPS receivers, field scanners, and remote sensing, and
  2. computational advances, including geostatistical interpolation and inference algorithms, GIS, digital elevation model, and data mining[6]

In digital soil mapping, semi-automated techniques and technologies are used to acquire, process and visualize information on soils and auxiliary information, so that the end result can be obtained at cheaper costs. Products of the data-driven or statistical soil mapping are commonly assessed for the accuracy and uncertainty and can be more easily updated when new information comes available.[6]

Digital Soil Mapping tries to overcome some of the drawbacks of the traditional soil maps that are often only focused on delineating soil-classes i.e. soil types.[5] Such traditional soil maps:

  • do not provide information for modeling the dynamics of soil conditions and
  • are inflexible to quantitative studies on the functionality of soils.

An example of successful digital soil mapping application is the physical properties[7] (soil texture, bulk density) developed in the European Union with around 20,000 topsoil samples of LUCAS database[8].

Scorpan

Scorpan is a mnemonic for an empirical quantitative descriptions of relationships between soil and environmental factors with a view to using these as soil spatial prediction functions for the purpose of Digital soil mapping. It is an adaptation of Hans Jenny’s five factors not for explanation of soil formation, but for empirical descriptions of relationships between soil and other spatially referenced factors. [6]

S = f(s,c,o,r,p,a,n), where

  • S = soil classes or attributes (to be modeled)
  • f = function
  • s = soil, other or previously measured properties of the soil at a point
  • c = climate, climatic properties of the environment at a point
  • o = organisms, including land cover and natural vegetation or fauna or human activity
  • r = relief, topography, landscape attributes
  • p = parent material, lithology
  • a = age, the time factor
  • n = spatial or geographic position
gollark: That is a really niche usecase for a language.
gollark: > because you normally dont want to calculate 74^773 by hand.WHO SAYS?
gollark: Wait, why do you even need control flow like that if your program is ENTIRELY DETERMINISTIC?
gollark: Except just doing boring identical computation.
gollark: I... see.

See also

References
  1. Scull, P.; J. Franklin; O.A. Chadwick; D. McArthur (June 2003). "Predictive soil mapping - a review". Progress in Physical Geography. 27 (2): 171–197. CiteSeerX 10.1.1.137.3441. doi:10.1191/0309133303pp366ra.
  2. Lagacherie, P.; McBratney, A. B.; Voltz, M., eds. (2006). Digital soil mapping: an introductory perspective. Amsterdam: Elsevier. p. 600. ISBN 978-0-444-52958-9. Archived from the original on 2012-01-16. Retrieved 2012-06-19.
  3. Dobos, E.; Carré, F.; Hengl, T.; Reuter, H.I.; Tóth, G., eds. (2006). Digital Soil Mapping as a support to production of functional maps (PDF). Luxemburg: Office for Official Publications of the European Communities. p. 68. EUR 22123 EN
  4. Boettinger, J.L.; Howell, D.W.; Moore, A.C.; Hartemink, A.E.; Kienast-Brown, S., eds. (2010). Digital Soil Mapping: Bridging Research, Environmental Application, and Operation. Springer. p. 473. ISBN 978-90-481-8862-8.
  5. Hengl, Tom; Mendes de Jesus, Jorge; McMillan, R.A.; Batjes, Niels H.; Heuvelink, G.B.M.; Ribeiro, Eloi C.; Samuel-Rosa, Allesandro; Kempen, Bas; Leenaars, J.G.B.; Walsh, M.G.; Ruiperez Gonzalez, Maria G. (2014). "SoilGrids1km — global soil information based on automated mapping". PLOS ONE. 9 (8): e105992. Bibcode:2014PLoSO...9j5992H. doi:10.1371/journal.pone.0105992. PMC 4149475. PMID 25171179.
  6. McBratney, A.B.; M.L. Mendonça Santos; B. Minasny (1 November 2003). "On digital soil mapping". Geoderma. 117 (1–2): 3–52. Bibcode:2003Geode.117....3M. doi:10.1016/S0016-7061(03)00223-4.
  7. Ballabio, Cristiano; Panagos, Panos; Monatanarella, Luca (2016). "Mapping topsoil physical properties at European scale using the LUCAS database". Geoderma. 261: 110–123. Bibcode:2016Geode.261..110B. doi:10.1016/j.geoderma.2015.07.006.
  8. Orgiazzi, A.; Ballabio, C.; Panagos, P.; Jones, A.; Fernández-Ugalde, O. (2018). "LUCAS Soil, the largest expandable soil dataset for Europe: a review". European Journal of Soil Science. 69: 140–153. doi:10.1111/ejss.12499. ISSN 1365-2389.
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