Multi-criteria characterization of recent digital soil mapping and modeling approaches

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@Article{Grunwald2009195,

• author = "S. Grunwald",
• title = "Multi-criteria characterization of recent digital soil mapping and modeling approaches",
• journal = "Geoderma",
• volume = "152",
• number = "3-4",
• pages = "195--207",
• year = "2009",
• ISSN = "0016-7061",
• DOI = "doi:10.1016/j.geoderma.2009.06.003",
• URL = "http://www.sciencedirect.com/science/article/B6V67-4WSG2WJ-1/2/af92060815439203d2999e4ace2ae786",
• keywords = "genetic algorithms, genetic programming, Digital soil mapping, Digital soil modelling, Pedometrics, Quantitative methods, Soils",
• abstract = "The history of digital soil mapping and modelling (DSMM) is marked by adoption of new mapping tools and techniques, data management systems, innovative delivery of soil data, and methods to analyse, integrate, and visualise soil and environmental datasets. DSMM studies are diverse with specialised, mathematical prototype models tested on limited geographic regions and/or datasets and simpler, operational DSMM used for routine mapping over large soil regions. Research-focused DSMM contrasts with need-driven DSMM and agency-operated soil surveys. Since there is no universal equation or digital soil prediction model that fits all regions and purposes the proposed strategy is to characterise recent DSMM approaches to provide recommendations for future needs at local, national and global scales. Such needs are not solely soil-centered, but consider broader issues such as land and water quality, carbon cycling and global climate change, sustainable land management, and more. A literature review was conducted to review 90 DSMM publications from two high-impact international soil science journals -- Geoderma and Soil Science Society of America Journal. A selective approach was used to identify published studies that cover the multi-factorial DSMM space. The following criteria were used (i) soil properties, (ii) sampling setup, (iii) soil geographic region, (iv) spatial scale, (v) distribution of soil observations, (vi) incorporation of legacy/historic data, (vii) methods/model type, (viii) environmental covariates, (ix) quantitative and pedological knowledge, and (x) assessment method. Strengths and weaknesses of current DSMM, their potential to be operationalized in soil mapping/modelling programs, research gaps, and future trends are discussed. Modeling of soils in 3D space and through time will require synergistic strategies to converge environmental landscape data and denser soil data sets. There are needs for more sophisticated technologies to measure soil properties and processes at fine resolution and with accuracy. Although there are numerous quantitative models rooted in factorial models that predict soil properties with accuracy in select geographic regions they lack consistency in terms of environmental input data, soil properties, quantitative methods, and evaluation strategies. DSMM requires merging of quantitative, geographic and pedological expertise and all should be ideally in balance.",
• notes = "survey",

}

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