Digital Soil Mapping and Modeling at Continental Scales: Finding Solutions for Global Issues

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Authors S. Grunwald, J. A. Thompson, J. L. Boettinger
Journal/Conference Name Soil Science Society of America Journal
Paper Category
Paper Abstract Profound shifts have occurred during the last three centuries in which human actions have become the main driver to global environmental change. In this new epoch, the Anthropocene, human-driven changes such as population growth, climate, and land use change are pushing the Earth system well outside of its normal operating range, causing severe and abrupt environmental change. In the Anthropocene, soil change and soil formation or degradation have also accelerated, jeopardizing soil quality and health. Thus, the need for up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize the physicochemical, biological, and hydrologic conditions of ecosystems across continents has intensified. These needs are in sharp contrast to available digital soil data representing continental and global soil systems, which only provide coarse-scale (11,000,000 or coarser) vector polygon maps with highly aggregated soil classes represented in the form of crisp map units derived from historic observations, lacking site-specific pedogenic process knowledge, and only indirectly relating to pressing issues of the Anthropocene. Furthermore, most available global soil data are snapshots in time, lacking the information necessary to document the evolution of soil properties and processes. Recently, major advancements in digital soil mapping and modeling through geographic information technologies, incorporation of soil and remote sensing products, and advanced quantitative methods have produced domain-specific soil property prediction models constrained to specific geographic regions, which have culminated in the vision for a global pixel-based soil map. To respond to the challenges soil scientists face in the Anthropocene, we propose a space–time modeling framework called STEP-AWBH (“step-up”), explicitly incorporating anthropogenic forcings to optimize the soil pixel of the futurevv.
Date of publication 2011
Code Programming Language R

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