Digital soil mapping

<h2 id="ambiguities">Ambiguities</h2>
DSM can rely upon, but is considered to be distinct from traditional <a href="/facts/Soil_mapping/sYpCQzz2">soil mapping</a>, which involves manual delineation of soil boundaries by field soil scientists. Non-digital <a href="/facts/Soil_map/MHakT6wD">soil maps</a> 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:

<ol><li>technological advances, including <a href="/facts/Global_Positioning_System/DKVLnzF3">GPS</a> receivers, field scanners, and <a href="/facts/Remote_sensing/pj2crmGs">remote sensing</a>, and</li>
<li>computational advances, including geostatistical interpolation and inference algorithms, <a href="/facts/Geographic_information_system/PoP1kVoa">GIS</a>, <a href="/facts/Digital_elevation_model/LMWSPfex">digital elevation model</a>, and <a href="/facts/Data_mining/AH53q5ac">data mining</a><a class="footnote-ref" id="fnref:6" href="#fn:6">6</a></li></ol>
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 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.<a class="footnote-ref" id="fnref:7" href="#fn:7">7</a>
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.<a class="footnote-ref" id="fnref:8" href="#fn:8">8</a> Such traditional <a href="/facts/Soil_map/MHakT6wD">soil maps</a>:

<ul><li>do not provide information for modeling the dynamics of soil conditions and</li>
<li>are inflexible to quantitative studies on the functionality of soils.</li></ul>
An example of successful digital soil mapping application is the physical properties<a class="footnote-ref" id="fnref:9" href="#fn:9">9</a> (soil texture, bulk density) developed in the European Union with around 20,000 <a href="/facts/Topsoil/BDClBQrs">topsoil</a> samples of LUCAS database.<a class="footnote-ref" id="fnref:10" href="#fn:10">10</a> 
Another example, are maps of soil properties for the entire world (250 m cell size), with quantified uncertainty, generated by ISRIC - World Soil Information <a class="footnote-ref" id="fnref:11" href="#fn:11">11</a>
using state-of-the-art machine learning methods (SoilGrids)<a class="footnote-ref" id="fnref:12" href="#fn:12">12</a> that use as inputs point data from a large global soil profile database (WoSIS)<a class="footnote-ref" id="fnref:13" href="#fn:13">13</a> and over 400 global environmental covariates.

<h2 id="scorpan">Scorpan</h2>
Scorpan is a <a href="/facts/Mnemonic/6HfDn0ks">mnemonic</a> 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 <a href="/facts/Hans_Jenny_(pedologist)/rN8jgQd6">Hans Jenny</a>'s five factors not for explanation of <a href="/facts/Soil_formation/sJty93z9">soil formation</a>, but for empirical descriptions of relationships between soil and other spatially referenced factors.<a class="footnote-ref" id="fnref:14" href="#fn:14">14</a>
S = f(s,c,o,r,p,a,n), where

<ul><li>S = <a href="/facts/Soil/5Ddpr3QM">soil</a> classes or attributes (to be modeled)</li>
<li>f = <a href="/facts/Function_(mathematics)/CdReEKCh">function</a></li>
<li>s = <a href="/facts/Soil/5Ddpr3QM">soil</a>, other or previously measured properties of the soil at a point</li>
<li>c = <a href="/facts/Climate/f57XrNFU">climate</a>, climatic properties of the environment at a point</li>
<li>o = <a href="/facts/Organism/j8JkL09u">organisms</a>, including land cover and natural vegetation or fauna or human activity</li>
<li>r = <a href="/facts/Topographic_relief/r2lduLeI">relief</a>, topography, landscape attributes</li>
<li>p = <a href="/facts/Parent_material/ywpDLX4Q">parent material</a>, <a href="/facts/Lithology/WLIJXzGj">lithology</a></li>
<li>a = age, the <a href="/facts/Time/3ShdpqLx">time</a> factor</li>
<li>n = <a href="/facts/Three-dimensional_space/KAqKWUbS">spatial</a> or <a href="/facts/Geographic/TRd1Toyo">geographic</a> position</li></ul>
<h2 id="see-also">See also</h2>
<ul><li><a href="/facts/Pedometric_mapping/SUpyhd6o">Pedometric mapping</a> – Statistical method for studying soil</li>
<li><a href="/facts/SSURGO/M8x7jvYd">SSURGO</a> – Soil Survey Geographic database</li></ul>

<h2 id="external-links">External links</h2>
<ul><li><a href="http://www.digitalsoilmapping.org">Working group on Digital Soil Mapping</a></li>
<li><a href="http://www.pedometrics.org">Pedometrics Commission of the International Union of Soil Sciences</a></li>
<li><a href="https://websoilsurvey.nrcs.usda.gov/app/">NRCS Web Soil Survey</a> Inventory of the soil resource across the U.S.</li>
<li><a href="http://GlobalSoilMap.net/">GlobalSoilMap.net Project</a> <a href="https://web.archive.org/web/20200224063330/http://globalsoilmap.net/">Archived</a> 2020-02-24 at the <a href="/facts/Wayback_Machine/nmQ3a6JC">Wayback Machine</a></li></ul>

<h2 id="references">References</h2>

<ol>
<li id="fn: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. Bibcode:2003PrPG...27..171S. CiteSeerX 10.1.1.137.3441. doi:10.1191/0309133303pp366ra. S2CID 787741. <a href="/wiki/Bibcode_(identifier)" target="_blank">/wiki/Bibcode_(identifier)</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></li>
<li id="fn: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. <a href="978-0-444-52958-9" target="_blank">978-0-444-52958-9</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></li>
<li id="fn: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 <a href="http://eusoils.jrc.ec.europa.eu/esdb_archive/eusoils_docs/other/EUR22123.pdf" target="_blank">http://eusoils.jrc.ec.europa.eu/esdb_archive/eusoils_docs/other/EUR22123.pdf</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></li>
<li id="fn: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. <a href="978-90-481-8862-8" target="_blank">978-90-481-8862-8</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></li>
<li id="fn: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. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4149475" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4149475</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></li>
<li id="fn: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. <a href="/wiki/Bibcode_(identifier)" target="_blank">/wiki/Bibcode_(identifier)</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></li>
<li id="fn:7">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. <a href="/wiki/Bibcode_(identifier)" target="_blank">/wiki/Bibcode_(identifier)</a> <a href="#fnref:7" class="footnote-back-ref">↩</a></li>
<li id="fn:8">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. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4149475" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4149475</a> <a href="#fnref:8" class="footnote-back-ref">↩</a></li>
<li id="fn:9">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. <a href="https://doi.org/10.1016%2Fj.geoderma.2015.07.006" target="_blank">https://doi.org/10.1016%2Fj.geoderma.2015.07.006</a> <a href="#fnref:9" class="footnote-back-ref">↩</a></li>
<li id="fn:10">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 (1): 140–153. Bibcode:2018EuJSS..69..140O. doi:10.1111/ejss.12499. ISSN 1365-2389. <a href="https://doi.org/10.1111%2Fejss.12499" target="_blank">https://doi.org/10.1111%2Fejss.12499</a> <a href="#fnref:10" class="footnote-back-ref">↩</a></li>
<li id="fn:11">"ISRIC - World Soil Information". <a href="https://data.isric.org/geonetwork/srv/eng/catalog.search#/search?resultType=details&sortBy=relevance&any=SoilGrids250m%202.0%20*&fast=index&_content_type=json&from=1&to=20" target="_blank">https://data.isric.org/geonetwork/srv/eng/catalog.search#/search?resultType=details&sortBy=relevance&any=SoilGrids250m%202.0%20*&fast=index&_content_type=json&from=1&to=20</a> <a href="#fnref:11" class="footnote-back-ref">↩</a></li>
<li id="fn:12">Poggio, Laura; de Sousa, L.M.; Batjes, N.H.; Heuvelink, G.B.M; Kempen, B.; Ribeiro, E.; Rossiter, D. (June 2021). "SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty". SOIL. 7 (1): 217–410. Bibcode:2021SOIL....7..217P. doi:10.5194/soil-7-217-2021. <a href="https://doi.org/10.5194%2Fsoil-7-217-2021" target="_blank">https://doi.org/10.5194%2Fsoil-7-217-2021</a> <a href="#fnref:12" class="footnote-back-ref">↩</a></li>
<li id="fn:13">Batjes, N.H.; Calisto, L.; de Sousa, D.M. (October 2024). "Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023)". Earth System Science Data. 16 (10): 4735–4765. Bibcode:2024ESSD...16.4735B. doi:10.5194/essd-16-4735-2024. <a href="https://doi.org/10.5194%2Fessd-16-4735-2024" target="_blank">https://doi.org/10.5194%2Fessd-16-4735-2024</a> <a href="#fnref:13" class="footnote-back-ref">↩</a></li>
<li id="fn:14">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. <a href="/wiki/Bibcode_(identifier)" target="_blank">/wiki/Bibcode_(identifier)</a> <a href="#fnref:14" class="footnote-back-ref">↩</a></li>
</ol>

Digital soil mapping open-in-new

Digital soil mapping