『Abstract
　The history of digital soil mapping and modeling (DSMM) is marked by adoption of new mapping tools and techniques, data management systems, innovative delivery of soil data, and methods to analyze, integrate, and visualize soil and environmental datasets. DSMM studies are diverse with specialized, 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 characterize 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/modeling 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 datasets. 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.

Keywords: Digital soil mapping; Digital soil modeling; Pedometrics; Quantitative methods; Soils』

Contents
1. Introduction
2. Materials and methods
3. Results
　3.1. Prediction of soil properties and classes at plot/field and coarse landscape scales
　3.2. Spatial scale
　3.3. Temporal scale
　3.4. Prediction of soil properties and classes
　3.5. Factors used to predict soil properties and classes
　3.6. Incorporation of sensors into soil predictive models
　3.7. Legacy soil data
　3.8. Methods used to predict soil properties and classes
　3.9. Calibration and validation of soil prediction models
4. Final thoughts - discussion and recommendations
　4.1. External and internal factors imparting control on soil properties and classes
　4.2. Boundary conditions
　4.3. Spatial and temporal scales
　4.4. Knowledge rankings
　4.5. Costs and prediction quality of digital soil mapping and modeling studies
5. Conclusion and outlook
Notes
Acknowledgements
References