THE GLOBALSOILMAP PROJECT: PAST, PRESENT, FUTURE, AND NATIONAL EXAMPLES FROM FRANCE

Soils have critical relevance to global issues, such as food and water security, climate regulation, sustainable energy, desertification and biodiversity protection. As a consequence, soil is becoming one of the top priorities for the global environmental policy agenda. Conventional soil maps suffer from large limitations, i.e. most of them are static and often obsolete, are often generated at coarse scale, and can be uneasy to handle. Digital Soil Mapping has been developed as a solution to generate high-resolution maps of soil properties over large areas. Two projects, GlobalSoilMap and SoilGrids, presently aim at delivering the first generation of global, high-resolution soil property fine grids. In this paper, we briefly describe the GlobalSoilMap history, its present status and present achievements, and illustrate some of these with (mainly) French examples. At given moment there is still an enormous potential for forthcoming research and for delivering products more helpful for end users. Key here is the continuous progress in available co-variates, in their spatial, spectral and temporal coverage and resolution through remote sensing products. All over the world, there is still a very large amount of point soil data still to be rescued and this effort should be pursued and encouraged. Statistically advances are expected by exploring and implementing new models. Especially relevant are spatial-temporal models and contemporary Artificial Intelligence for handling the complex big data. Advances should be made and research efforts are needed on estimating the uncertainties, and even on estimating uncertainties on uncertain-ties. Attempts to merge different model strategies and products (for instance deriving from different covariates, spatial extents, soil data sources, and mod-els) should be made in order to get the most useful information from each of these predictions, and to identify how controlling factors may change depending on scales.

digital soil mapping, geostatistics

1. Adhikari K., Kheir R.B., Greve M.B., Bocher P.K., Malone B.P., Minasny B., A. McBratney, Greve M.H. High-Resolution 3-D Mapping of Soil Texture in Denmark, Soil Sci. Soc. Am. J., 2013, V. 77, No. 3, pp. 860–876

2. Akpa S.I.C., Odeh I.O.A., Bishop T.F.A., Hartemink A.E. Digital soil Map-ping of soil particle-size fractions in Nigeria, SSSAJ, 2014, V. 78, No. 6, pp. 1953-1966.

3. Amundson R., Berhe A.A., Hopmans J.W., Olson C., Sztein A.E., Sparks D.L. Soil and human security in the 21st century, Science, 2015, 348(6235).

4. Arrouays D., McKenzie N.J., Hempel J.W., Richer-de-Forges A.C., McBratney A.B. (eds). GlobalSoilMap: Basis of the global spatial soil information system. 1st ed. CRC Press Taylor & Francis Group, 2014a, 478 p.

5. 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., Mendonça-Santos Md.L., Minasny B., Montanarella L., Odeh I.O.A., Sanchez P.A., Thompson J.A., Zhang G.-L. GlobalSoilMap: towards a fine-resolution global grid of soil properties, Advances in Agronomy, 2014b, V. 125, No. 93, pp. 134.

6. Arrouays D., Lagacherie P., Hartemink A. Digital soil mapping across the globe, Geoderma Regional, 2017a, V. 9, pp. 1-4.

7. Arrouays D., Leenaars J., 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., Suk-Young Hong, Krasilnikov P., Lagacherie P., Lelyk G., Libohova Z., Lilly A., McBratney A., Mckenzie N., Vasques G., Mulder V.L., Minasny B., Montanarella L., Odeh I., Padarian J., Poggio L., Roudier P., Saby N., Savin I., Searle R., Stolbovoy V., Thompson J., Smith S., Sulaeman Y., Vintila R., Viscarra Rossel R., Wilson P., Gan-Lin Zhang, Swerts M., van Oorts K., Karklins A., Liu Feng, Navarro A.R.I., Levin A., Lak-tionova T., Dell'Acqua M., Suvannang N., Ruam W., Prasad J., Patil N., Husnjak S., Pásztor L., Okx J., Hallet S., Keay C., Farewell T., Lilja H., Juilleret J., Marx S., Takata Y., Kayusuki 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., da Conceição Gonçalves M., Theocharopoulos S., Yemefack M., Tedou S., Vrscaj B., Grob U., Kozak J., Boruvka L., Dobos E., Taboada M., Moretti L., Rodriguez D. Soil legacy data rescue via GlobalSoilMap and other international and national initiatives, GeoRes J., 2017b, V. 14, pp. 1-19.

8. Arrouays D., Savin I., Leenaars J.G.B., McBratney A.B. GlobalSoilMap. Digital Soil Mapping from Country to Globe, 1st ed. CRC Press Taylor & Francis Group, 2018, 173 p.

9. Ballabio C., Panagos P., Montanarella L. Mapping topsoil physical properties at European scale using the LUCAS database, Geoderma, 2016, V. 261, pp. 110-123.

10. Bishop T.F.A., McBratney A.B., Laslett G.M. Modelling soil attribute depth functions with equal-area quadratic smoothing splines, Geoderma, 1999, V. 91, pp. 27-45.

11. Caubet M., Román Dobarco M., Arrouays D., Minasny B., Saby N. Merging country, continental and global predictions of soil texture: Lessons from ensemble modelling in France, Geoderma, 2019, V. 337, pp. 99-110.

12. Chagas Cd.S., Saraiva H., Koenow Pinheiro K., de Carvalho Junior W., dos Anjos, L.H.C., Bhering, S.B. Data mining methods applied to map soil units on tropical hillslopes in Rio de Janeiro, Brazil, Geoderma Regional, 2017, V. 9, pp. 47-55.

13. Chartin C., Stevens A., Goidts E., Krüger I, Carnol M., van Wesemael B. Mapping Soil Organic Carbon stocks and estimating uncertainties at the regional scale following a legacy sampling strategy (Southern Belgium, Wallonia), Geoderma Regional, 2017, V. 9, pp. 73-86.

14. Chen S., Arrouays D. Soil carbon stocks are underestimated in mountainous regions, Geoderma, 2018. V. 320, pp. 146-148.

15. Chen S., Richer-de-Forges A.C., Saby N.P.A., Martin M.P., Walter C., Arrouays D. Building a pedotransfer function for soil bulk density on regional da-taset and testing its validity over a larger area, Geoderma, 2018a, V. 312, pp. 52-63.

16. Chen S., Martin M., Saby N., Walter C., Angers D., Arrouays D. Fine resolution map of top- and subsoil carbon sequestration potential in France, Science of the Total Environment, 2018b, V. 630, pp. 389-400.

17. Delmas M., Saby N.P.A., Arrouays D., Dupas R., Lemercier B., Pellerin S., Gascuel-Odoux C. Explaining and mapping total phosphorous content in French topsoils, Soil Use and Management, 2015, V. 31(2), pp. 259-269.

18. Gardi C., Yigini Y. Continuous mapping of soil pH using digital soil mapping approach in Europe, Eurasian J. Soil Sci., 2012, V. 1(2), pp. 64-68.

19. Gomez C., Lagacherie, P., Coulouma, G., Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis–NIR data, Geoderma, 2012, V. 189–190, pp. 176–185.

20. Gomez C., Adeline K., Bacha S., Driessen B., Gorretta N., Lagacherie P., Roger J.M., Briottet X. Sensitivity of clay content prediction to spectral configuration of VNIR/SWIR imaging data, from multispectral to hyperspectral scenarios. Remote Sensing of Environment, 2018, 204, pp. 18-30.

21. Grundy M.J., Viscarra Rossel R.A., Searle R.D., Wilson P.L., Chen C., Gregory L.J. Soil and Landscape Grid of Australia, Soil Research, 2015, V. 53, pp. 835–844.

22. Grunwald S., Thompson J.A., Boettinger J.L. Digital soil mapping and modeling at continental scales: Finding solutions for global issues, Soil Sci. Soc. Am. J., 2011, V. 75, pp. 1201–1213.

23. Guerrero E., Pérez A. Arroyo C., Equihua J. Guevara M. Building a national framework for pedometric mapping: soil depth as an example for Mexico, In: Arrouays D., McKenzie N.J., Hempel J.W., Richer de Forges A.C., McBratney A.B. (eds). GlobalSoilMap. Basis of the global soil information system, Oxon: Taylor & Francis, CRC press, 2014, pp. 103-108.

24. Hartemink A.E., McBratney A.B. A soil science renaissance, Geoderma, 2008, V. 148, pp. 123–129.

25. Hengl T., Heuvelink G.B.M., Kempen B., Leenaars J.G.B., Walsh M.G., Shepherd K., Sila A., MacMillan R.A., Mendes de Jesus J., Tamene L., Tondoh J.E. Mapping soil properties of Africa at 250 m resolution: Random Forests significantly improve current predictions 2015, PLoS ONE, 2015. V. 10(6), pp. e0125814.

26. Hengl T., de Jesus J.M., Heuvelink G.B.M., Gonzalez M.R., Kilibarda M., Blagotic A., Shangguan W., Wright M.N., Blagotic, A., Geng X.Y., Bauer-Marschallinge, B., Guevara M.A., Vargas R., MacMillan R.A., Batjes N.H., Leenaars J.G.B., Ribeiro E., Wheeler I., Mantel S., Kempen B. SoilGrids250m: global gridded soil information based on Machine Learning, PLoS ONE, 2017, V. 12(2), pp. e0169748.

27. Hengl T, Mendes de Jesus J.M., MacMillan R.A., Batjes N.H., Heuvelink G.B.M., Ribeiro E., Samuel-Rosa A., Kempen B., Leenaars J.G.B., Walsh M.G., Ruiperez Gonzalez M. SoilGrids1km -Global Soil Information Based on Automated Mapping, PLoS ONE, 2014, V. 9(8), pp. e105992.

28. ISRIC. Soil property maps of Africa at 1 km, 2013, 1 p.

29. Lacoste M., Mulder V.L., Richer-de-Forges A.C., Martin M.P., Arrouays D. Evaluating large-extent spatial modelling approaches: a case study for soil depth for France, Geoderma Regional, 2016, V. 7, pp. 137-152.

30. Lagacherie P., Arrouays D., Bourennane H., Gomez C., Martin M., Saby N. How far can the uncertainty on a Digital Soil Map be known?: a numerical experiment using pseudo values of clay content obtained from Vis-SWIR Hyperspec-tral imagery, Geoderma, 2019, available online 28 September 2018, in press.

31. Lagacherie P., McBratney A.B., Voltz M. Digital Soil Mapping: An Introductory Perspective, Developments in Soil Science, 2006, V. 31, 658 p.

32. Lelyk G.W., MacMillan R.A., Smith S., Daneshfar B. Spatial disaggregation of soil map polygons to estimate continuous soil property values at a resolution of 90 m for a pilot area in Manitoba, Canada. In: Arrouays D., McKenzie N.J., Hempel J.W., Richer de Forges A.C., McBratney A.B. (editors). GlobalSoilMap. Basis of the global soil information system, Taylor & Francis, CRC press, 2014, pp. 201-207.

33. Leenaars J.G.B. Africa Soil Profiles Database, Version 1.0. A compilation of georeferenced and standardized legacy soil profile data for Sub-Saharan Africa (with dataset). ISRIC Report 2012/03. Africa Soil Information Service (AfSIS) project. ISRIC - World Soil Information, Wageningen, 2012.

34. Leenaars J.G.B., Claessens L., Heuvelink G.B.M., Hengl T., Ruiperez González M., van Bussel L.G.J., Guilpart N., Yang H., Cassman K.G. Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa, Geoderma, 2018, V. 324, pp.18-36.

35. Mansuy N., Thiffault E., Paré D., Bernier P., Guindon L., Villemaire P., Poir-ier V., Beaudoin A. Digital mapping of soil properties in Canadian managed forests at 250 m of resolution using the k-nearest neighbor method, Geoderma, 2014, V. 235–236, pp. 59-73.

36. Marchant B.P., Saby N.P.A., Arrouays D. A survey of topsoil arsenic and mercury concentrations across France, Chemosphere, 2017, V. 181, pp. 635-644.

37. Marchant B.P., Villanneau E.J. Saby N.P.A., Arrouays D., Rawlins B.G. Quantifying and mapping topsoil inorganic carbon concentrations and stocks: approaches tested in France, Soil Use and Management, 2015, V. 31(1), pp. 29-38.

38. Martelet G., Drufin S., Tourlière B., Saby N.P.A., Perrin J., DeParis J., Prognon J.F., Jolivet C., Ratié C., Arrouays D. Regional regolith parameters prediction using the proxy of airborne gamma ray spectrometry, Vadose Zone Journal, 2013, V.12(4), pp. 25-39.

39. Martin M.P., Orton T.G., Lacarce E., Meersmans J., Saby N.P.A., Paroissien J.B., Jolivet C., Boulonne L., Arrouays D. Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale, Geoderma, 2014, V. 223, pp. 97-107.

40. McBratney A.B., Field D.J., Koch A. The dimensions of soil security, Geoderma, 2014, V. 213, pp.203–213.

41. McBratney A.B., Mendonça Santos M.L., Minasny B. On digital soil map-ping, Geoderma, 2003, V. 117(1-2), pp. 3–52.

42. Minasny B., Hartemink A.E. Predicting soil properties in the tropics, Earth-Science Reviews, 2011, V. 106, No. 1–2, pp. 52-62.

43. Minasny B., McBratney A.B. Digital soil mapping: A brief history and some lessons, Geoderma, 2016, V. 264, Part B, pp. 301-311.

44. Montanarella L, Pennock D.J., McKenzie N.J., Badraoui M., Chude V., Baptista I., Mamo T., Yemefack M., Singh Aulakh M., Yagi K., Young Hong S., Vijarnsorn P., Zhang G.-L., Arrouays D., Black H., Krasilnikov P., Sobocká J., Alegre J., Henriquez C.R., Mendonça-Santos M.L., Taboada M., Espinosa-Victoria D., AlShankiti A., AlaviPanah S.K., Elsheikh E.A.E., Hempel J., Camps Arbestain M., Nachtergaele F., Vargas R.. World’s soils are under threat, Soil, 2016, No. 2, pp. 79-82.

45. Mulder VL, Lacoste M, Richer de Forges AC, Martin MP, Arrouays D. National versus global modelling the 3D distribution of soil organic carbon in main-land France, Geoderma, 2016a, V. 263, pp.13-34.

46. Mulder V.L., Lacoste M., Richer de Forges A.C., Arrouays D. GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth, Sci. Tot. Env, 2016b, V. 573, pp. 1352-1369.

47. Nauman T.W., Thompson J.A., Odgers N.P., Libohova Z. Fuzzy disaggregation of conventional soil maps using database knowledge extraction to produce soil property maps, In: Digital Soil Assessments and Beyond: 5th Global Work-shop on Digital Soil Mapping. CRC Press/Balkema, 2012, pp. 203-208.

48. Odgers N.P., McBratney A.B., Minasny B., Sun W., Clifford D. DSMART: An algorithm to spatially disaggregate soil map units. In: GlobalSoilMap: basis of the global spatial soil information system. Arrouays, D; McKenzie, N; Hempel, J; Richer-de-Forges, AC; McBratney, A. (Eds). CRC Press, Taylor & Francis, London, 2014, p. 261-266.

49. Orton T.G., Saby N.P.A., Arrouays D., Jolivet C.C., Villanneau E., Marchant B.P., Caria G., Barriuso E., Bispo A., Briand O. Spatial distribution of lindane concentrations in topsoil across France, Sci. Tot. Env., 2013, V. 443, pp. 338-350.

50. Richer-de-Forges A.C., Saby N.P.A., Mulder V.L., Laroche B., Arrouays D. Probability mapping of iron pan presence in sandy podzols in South-West France, using digital soil mapping, Geoderma Regional, 2017, V. 9, pp. 39-46.

51. Román Dobarco M., Arrouays D., Lagacherie P., Ciampalini P., Saby N. Prediction of topsoil texture for Region Centre (France) applying model ensemble methods, Geoderma, 2017, V. 292, pp. 67-77.

52. Román Dobarco M., Cousin I., Le Bas C., Martin M.P., Pedotransfer functions for predicting available water capacity in French soils, their applicability domain and associated uncertainty, Geoderma, 2019, V. 336, pp. 81–95.

53. Rossiter D.G. Past, present & future of information technology in pedometrics, Geoderma, 2018, V. 324, pp. 131-137.

54. Sanchez P.A., Ahamed S., Carré F., Hartemink A.E., Hempel J.W., Huising J., Lagacherie P., McBratney A.B., McKenzie N.J., Mendonça-Santos M.L., Minasny B., Montanarella L., Okoth P., Palm C.A., Sachs J.D., Shepherd K.D., Vagen T.G., Vanlauwe B., Walsh M.G., Winowiecki L.A., Zhang G.-L. Digital soil map of the world, Science, 2009, V.325(5941), pp. 680–681.

55. Santra P., Mahesh Kumar M., Panwar,N. 2017. Digital soil mapping of sand content in arid western India through geostatistical approaches, Geoderma Regional, 2017, V. 9, pp. 56-72.

56. Sulaeman Y, Minasny B., McBratney A.B., Sarwani M., and Sutandi A. Harmonizing legacy soil data for digital soil mapping in Indonesia, Geoderma, 2013, V. 192, pp. 77-85.

57. Toth G., Jones A., Montanarella L. The LUCAS topsoil database and derived information on the regional variability of cropland topsoil properties in the European Union, Environmental Monitoring and Assessment, 2013, V. 185(9), pp.7409-7425.

58. Vaudour E., Gilliot J.M., Bel L., Lefevre J., Chehdi K. Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra, International Journal of applied earth observation and geoinformation, 2016, V. 49, pp. 24-38.

59. Vaysse K., Lagacherie P. Using quantile regression forest to estimate uncertainty of digital soil mapping products, Geoderma, 2017, V. 291, pp. 55-64.

60. Viscarra Rossel R., Chen C., Grundy M., Searle R., Clifford D., Campbell P. The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project, Soil Research, 2015, V. 53, No. 8, pp. 845–64.

61. Villanneau E.J., Saby N.P.A., Orton T.G., Jolivet C.C., Boulonne L., Caria G., Barriuso E., Bispo A., Briand O., Arrouays D. First evidence of large-scale PAH trends in French soils, Environmental Chemistry Letters, 2013, V. 11(1), pp. 99-104.

62. Vincent S., Lemercier B., Berthier L., Walter C. Spatial disaggregation of complex Soil Map Units at the regional scale based on soil-landscape relation-ships, Geoderma, 2016, V. 311, pp. 130-142.

63. Young Hong S., Minasny B, Hwa Han K., Kim Y., Lee K. Predicting and mapping soil available water capacity in Korea, Peer J., 2013, PubMed 23646290.

64. Zaouche M., Bel L., Vaudour E., Geostatistical mapping of topsoil organic carbon and uncertainty assessment in Western Paris croplands (France), Geoderma Regional, 2017, V. 10, pp. 126–137.