THE GLOBALSOILMAP PROJECT: PAST, PRESENT, FUTURE, AND NATIONAL EXAMPLES FROM FRANCE - D. Arrouays; A. C. Richer-de-Forges; A. B. McBratney; A. E. Hartemink; B. Minasny; I. Savin; M. Grundy; J. G. B. Leenaars; L. Poggio; P. Roudier; Z. Libohova; N. J. McKenzie; et al. (Бюллетень Почвенного института им. В.В. Докучаева, 2018)
Soils have critical relevance to global issues, such as food and water security, climate regulation, sustainable energy, desertification and biodiversity protec-tion. As a consequence, soil is becoming one of the top priorities for the global environmental policy agenda. Conventional soil maps suffer from large limita-tions, 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 covariates, 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 uncertainties. Attempts to merge different model strategies and products (for instance deriving from different covariates, spatial extents, soil data sources, and models) 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.