{"accessLevel": "public", "bureauCode": ["010:12"], "contactPoint": {"@type": "vcard:Contact", "fn": "Travis W Nauman", "hasEmail": "mailto:tnauman@usgs.gov"}, "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.", "distribution": [{"@type": "dcat:Distribution", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5e90b34a82ce172707ed738a.xml", "format": "XML", "mediaType": "text/xml", "title": "Original Metadata"}, {"@type": "dcat:Distribution", "accessURL": "https://doi.org/10.5066/P9SK0DO2", "description": "Landing page for access to the data", "format": "XML", "mediaType": "application/http", "title": "Digital Data"}], "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5e90b34a82ce172707ed738a", "keyword": ["soil properties", "digital soil mapping", "texture fractions", "interpolate", "Colorado", "bulk density", "USGS:5e90b34a82ce172707ed738a", "Utah", "uncertainty", "predictive mapping", "available water capacity", "electrical conductivity", "predictive maps", "maps and atlases", "Arizona", "calcium carbonate", "soil forming factors", "environmental conditions", "soil conductivity", "restrictive layer", "Nevada", "Hoover Dam", "predicitve modeling", "surface rock size", "New Mexico", "soil sciences", "erodibility", "random forest models", "soil density", "surface rock cover", "soil pH", "gypsum", "soil texture", "machine learning", "rock", "sodium adsorption ratio", "Colorado River", "accuracy and error estimated", "soil property maps", "soils", "Wyoming", "geoscientificInformation", "Colorado River Basin above Hoover Dam", "organic matter", "Colorado River Basin", "random forests", "environmental raster layers"], "modified": "2020-08-27T00:00:00Z", "publisher": {"@type": "org:Organization", "name": "U.S. Geological Survey"}, "spatial": "-116.0000, 33.3000, -105.2000, 44.0000", "theme": ["geospatial"], "title": "Predictive soil property map: Sand content"}