Challenges to Digital Soil Mapping

Abstract

Digital Soil Mapping to capture or determine categorical or property information has undergone a tremendous increase in capability and application during the past decade. Many successful technologies have been developed through research activities worldwide, including generalized linear models, classification and regression trees, neural networks, fuzzy systems, expert systems, and geostatistical methods and applications. These technologies have matured beyond a research activity and have potential for use by soil scientists to more accurately, consistently, and efficiently define soil categories and soil properties based on digital proxies to soil-forming factors. These applications for producing soil maps are now poised to become production tools to either update older soil survey information or to produce soil information on previously unmapped areas.

As these technologies move into the mainstream for producing soil survey information, there are challenges that must be overcome. The community of soil scientists and soil classifiers engaged in producing soil information must become familiar with the technologies and their potential uses and limitations. More importantly, the users of soil survey information must be convinced of the relevance and applicability of maps and data that appear different from the “traditional” products with which they have become familiar. New challenges include developing acceptable standards and procedures for the production and quality control and interpretation of the information that relates to agricultural, engineering, forestry and other soil-landscape uses.