Predictive soil property map: Fine sand content

Publication Date

2020-06-29

Time Period

2020

Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release, https://doi.org/10.5066/P9SK0DO2.

Type: Source Code Type: Related Primary Publication

The primary purpose of this data was to demonstrate a new workflow for creating soil property maps across the United States. However, some of these maps have potential to assist 1) land managers with decision making, 2) earth system modeling applications, and 3) future sampling to improve soil survey and future predictive mapping products. Soil properties were chosen to address relevant soils data needs such as concerns about erosion, salinity, and dust emissions. Uncertainty was characterized for every pixel with 95% prediction interval bounds and a relative prediction interval (RPI) metric that standardizes prediction intervals to the original training sample distribution for each model. The RPI values easily interpretable as values below 0.5 indicate low likelihood of error being higher than the global root mean squared error, and values exceeding 1.0 indicate more likelihood of error beyond global error summaries. In short, RPI values < 0.5 are consistently pretty good; values up to 0.9 are probably still reliable but probably have some error, and values close to and above 1.0 should be regarded with suspicion and perhaps trigger field evaluation of estimates before use.

• USGS Southwest Biological Science Center (SBSC)

Data source

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