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This chapter considers both opportunities and constraints to applied, operational digital soil mapping ( DSM) from the points of view of a) availability of suitable input data layers, b) protocols available for DSM, c) quality of input data layers and resultant output maps and d) other efforts required to build predictive mapping capacity and apply it effectively. Many potential DSM practitioners are discouraged by the real or perceived lack of availability of suitable input data layers to support DSM, particularly in regions with weakly developed spatial data infrastructures. Solutions to addressing problems of limited or sparse spatial data sets are identified for input layers derived from digital elevationmodels ( DEM’s remotely sensed imagery and available secondary source maps. A variety of protocols for producing predictive soil maps are discussed under the general headings of unsupervised, supervised and knowledge-based (or heuristic) approaches. These key protocol activities support the ability to make maps of the spatial distribution of soil classes or attributes by developing predictive relations between spatially distributed input variables or classes and the desired output classes. Different strategies are reviewed to acquire and formalize tacit knowledge embodied in soil-landform conceptual models and to capture this tacit knowledge as quantitative rules. A considerable amount of resistance to DSM arises from real or perceived concerns about the quality of the resulting maps in comparison to existing maps produced using traditional mapping methods. Quality, defined as the ability of a map or product to correctly predict the characteristics of the landscape at particular points or within particular small areas, is discussed as are suitable approaches for evaluating and reporting it. The capacity to apply DSMroutinely and operationally requires additional.
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