Soil Landscapes of the United States (SOLUS)

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Description
SOLUS100
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Data Citations
 

Description

Soil Landscapes of the United States, or SOLUS, is a national map product developed by the National Cooperative Soil Survey that is focused on providing a consistent set of spatially continuous soil property maps to support large scope soil investigations and land use decisions. SOLUS maps use a digital soil mapping framework that combines multiple sources of soil survey data with environmental covariate data and machine learning. Digital soil mapping is the production of georeferenced soil databases based on the quantitative relationships between soil measurements made in the field or laboratory and environmental data. Numerical models use the quantitative relationships to predict the spatial distribution of either discrete soil classes, such as map units, or continuous soil properties, such as clay content. 

SOLUS maps use continuous property mapping, which predicts soil physical or chemical properties in horizontal and vertical dimensions. The soil properties are represented across a continuous range of values. Raster datasets of select soil properties can be predicted at specified depths or depth intervals. Continuous soil property maps such as SOLUS provide critical natural resource information to support environmental researchers and modelers, conservationists, and others making land management decisions. SOLUS will be updated annually with improved data and methodology. 

SOLUS100

The first version of SOLUS, called SOLUS100, is 100 m spatial resolution. Each 100 m raster cell represents a 100 m by 100 m square on the ground with soil property values estimated at seven depths: 0, 5, 15, 30, 60, 100, and 150 cm. The next version will be 30 m spatial resolution and called SOLUS30. SOLUS100 predicts 20 soil properties (listed below with units) at seven depths for the continental United States for a total of 512 maps.

1. Very fine sand (%)
2. Fine sand (%)
3. Medium sand (%)
4. Coarse sand (%)
5. Very coarse sand (%)
6. Total sand (%)
7. Silt (%)
8. Clay (%)
9. pH
10. Soil organic carbon (%)
11. Calcium carbonate equivalent (%)
12. Gypsum content (% by weight)
13. Electrical conductivity (mmhos/cm)
14. Sodium adsorption ratio
15. Cation exchange capacity (meq/100g)
16. Effective cation exchange capacity (meq/100g)
17. Oven dry bulk density (g/cm3)
18. Depth to bedrock (cm)
19. Depth to restriction (cm)
20. Rock fragment volume (%)

Property Prediction and Uncertainty Layers

Each property-depth prediction is accompanied by estimates of uncertainty expressed as prediction interval low and high and relative prediction interval (RPI). Prediction interval low and high define the range within which future predictions may occur. The relative prediction interval ranges from 0 to 1 and is a relative measure of uncertainty with high values being more uncertain. It is computed as the ratio of the 95% prediction interval width to the training set 95% quantile width (97.5% quantile value – 2.5% quantile value). Values closer to 0 indicate lower uncertainty and values closer to 1 indicate higher uncertainty. Values greater than 1 indicate that the prediction at that location is outside the range of the training data used for that property at that depth. The Soil and Plant Science Division delivers each property-depth combination through Google Cloud Platform as four raster data layers: the property prediction, the prediction interval low and high, and the RPI. Property prediction and uncertainty layers follow the naming convention: 

1. propertyname_depth_cm_p (predicted property values)
2. propertyname_depth_cm_rpi (relative prediction interval)
3. propertyname_depth_cm_l (prediction interval low)
4. propertyname_depth_cm_h (prediction interval high)

Access

SOLUS100 maps are available for download or use within scripting or GIS software environments: SOLUS100 Cloud Storage Bucket

Details on background, methodology, accuracy, uncertainty, and other results and discussion of SOLUS100 maps are available at SOLUS100 Ag Data Commons Repository and in the following publication:

Nauman, T. W., Kienast-Brown, S., Roecker, S. M., Brungard, C., White, D., Philippe, J., & Thompson, J. A. (2024). Soil landscapes of the United States (SOLUS): developing predictive soil property maps of the conterminous United States using hybrid training sets. Soil Science Society of America Journal, 1–20. https://doi.org/10.1002/saj2.20769

Data Citations

Soil Survey Staff. Soil Landscapes of the United States. United States Department of Agriculture, Natural Resources Conservation Service. Available online at storage.googleapis.com/solus100pub/index.html. Month, day, year accessed (year of official release).

Citation Example

The following example is for the 2024 SOLUS maps. Such citations should appear in the reference section of your document.

Soil Survey Staff. Soil Landscapes of the United States. United States Department of Agriculture, Natural Resources Conservation Service. Available online at storage.googleapis.com/solus100pub/index.html.  May 22, 2024 (2024 official release).