US Migrating
type: Agent

Predictive soil property map: Rock content (>2mm)
type: Dataset
contact point: Organization
  fn: Travis W Nauman
keyword: random-forests
keyword: soil-texture
keyword: erodibility
keyword: geo
keyword: environmental-conditions
keyword: soil-ph
keyword: soil-conductivity
keyword: colorado-river-basin
keyword: accuracy-and-error-estimated
keyword: uncertainty
keyword: interpolate
keyword: national
keyword: wyoming
keyword: soil-sciences
keyword: hoover-dam
keyword: calcium-carbonate
keyword: electrical-conductivity
keyword: surface-rock-cover
keyword: geoscientificinformation
keyword: organic-matter
keyword: predictive-maps
keyword: surface-rock-size
keyword: new-mexico
keyword: north-america
keyword: soil-properties
keyword: colorado-river
keyword: geoss
keyword: united-states
keyword: amerigeoss
keyword: restrictive-layer
keyword: soil-density
keyword: rock
keyword: digital-soil-mapping
keyword: soils
keyword: predicitve-modeling
keyword: environmental-raster-layers
keyword: amerigeo
keyword: utah
keyword: machine-learning
keyword: gypsum
keyword: ckan
keyword: usgs-5e90b31282ce172707ed7195
keyword: colorado
keyword: soil-property-maps
keyword: texture-fractions
keyword: bulk-density
keyword: maps-and-atlases
keyword: colorado-river-basin-above-hoover-dam
keyword: soil-forming-factors
keyword: sodium-adsorption-ratio
keyword: nevada
keyword: arizona
keyword: random-forest-models
keyword: available-water-capacity
keyword: predictive-mapping
theme: {geospatial}
description: These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.
issued: 2025-11-20T08:18:26.253474
modified: 20200827
spatial: Location

Digital Data
type: Distribution
access URL: https://doi.org/10.5066/P9SK0DO2
media type: application/http
description: Landing page for access to the data
format: XML
issued: 2022-07-28T20:54:47.168385
modified: 2025-11-20T08:18:26.227999

Original Metadata
type: Distribution
access URL: https://data.usgs.gov/datacatalog/metadata/USGS.5e90b31282ce172707ed7195.xml
media type: text/xml
description: The metadata original format
format: XML
issued: 2022-07-28T20:54:47.168377
modified: 2025-11-20T08:18:26.227578