Predictive maps of 2D and 3D surface soil properties and associated uncertainty for the Upper Colorado River Basin, USA
April 9, 2019
The raster datasets in this data release are maps of soil surface properties that were used in analyzing different approaches for digital soil mapping. They include maps of soil pH, electrical conductivity, soil organic matter, and soil summed fine and very fine sand contents that were created using both 2D and 3D modeling strategies. For each property a map was created using both 2D and 3D approaches to compare the mapped results.
Citation Information
| Publication Year | 2019 |
|---|---|
| Title | Predictive maps of 2D and 3D surface soil properties and associated uncertainty for the Upper Colorado River Basin, USA |
| DOI | 10.5066/P9YBAKC2 |
| Authors | Travis W Nauman, Michael C Duniway |
| Product Type | Data Release |
| Record Source | USGS Digital Object Identifier Catalog |
| USGS Organization | Southwest Biological Science Center - Flagstaff, AZ, Headquarters |
Related Content
Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data
Fine scale maps of soil properties enable efficient land management and inform earth system models. Recent efforts to create soil property maps from field observations tend to use similar tree-based machine learning interpolation approaches, but often deal with depth of predictions, validation, and uncertainty differently. One of the main differences in approaches is whether to model individual de
Authors
Travis Nauman, Michael C. Duniway
Related Content
Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data
Fine scale maps of soil properties enable efficient land management and inform earth system models. Recent efforts to create soil property maps from field observations tend to use similar tree-based machine learning interpolation approaches, but often deal with depth of predictions, validation, and uncertainty differently. One of the main differences in approaches is whether to model individual de
Authors
Travis Nauman, Michael C. Duniway