The field of digital soil mapping has bridged the classic theories of soil science into the modern computing age to produce high resolution predictive soil maps. This body of work utilizes classic soil factorial theory (soil = f[climate, organisms, relief (topography), parent material, time] + ɛ, or ‘clorpt’). The clorpt framework has been approximated using various environmental spatial data layers to predict soil types and properties between field observations, which are expensive and time consuming. We are using these mapping techniques, coupled with an expanding availability of field observations, to create a variety of soil property, soil classification, and land potential classifications across the western US. By developing a workflow that incorporates existing observations and new digital soil mapping techniques, we have been able to map soil themes at a broad scales to facilitate informed land management decisions.
Product: Soil property and class maps
100-meter (328’) resolution soil property and class maps of the lower 48 US states
Product: A quantitative soil-geomorphic framework
A quantitative soil-geomorphic framework for developing and mapping ecological site groups
Product: 30m Resolution soil maps for CO River Basin
A broader set of 30-meter soil property maps now available for the Upper Colorado River Basin
Background & Importance
In the age of big data and complex modeling in science, quantitatively sound, high resolution soil maps are more important than ever. Soils play an important role as an interface in mediating water and nutrient movement in ecosystems that can vary greatly across landscapes. This spatial variation in soils can mean that different areas may be more or less resilient to human disturbance and climate change while still maintaining ecosystem services. Soils are at the heart of the ‘potential’ of an area to produce the foundational energy of photosynthetic vegetative production, maintain surface water supply and quality, and keep particulates and dust from threatening air quality. Understanding how soils of differing potential and vulnerability are distributed through a landscape with maps can help a multitude of researchers, land owners/managers, and policy makers to make informed decisions.
General Methods
Digital soil mapping employs a vast set of environmental mapping layers with exhaustive spatial extent to help predict soil types and properties between field observations of soils. Locations where soil field observations exist are related to the environmental layers (topography, satellite imagery, climate, geology, and other remotely sensed products) with machine learning or artificial intelligence algorithms to predict soil type or properties in locations that scientists haven’t visited, but that are similar in environmental condition to places where soils have been sampled. This process is repeatable, easily updated, and provides robust measures of map accuracy and prediction uncertainty at every pixel.
Important Results
Recent developments include 1) 100-meter (328’) resolution soil property and class maps of the lower 48 US states, 2) POLARIS soil property maps at 30-meter resolution for the lower 48 states, and 3) a broader set of 30-meter soil property maps now available for the Upper Colorado River Basin (see links above). Additional studies lead by the USGS have applied DSM tools and products to assess oil and gas well-pad reclamation status and Colorado River salinity management. These products are also being used in conjunction with vegetation data, expert feedback, available state-and-transitions models, and laboratory data to create a new site potential classification system.
Below are other science projects associated with this project.
Wind Erosion and Dust Emissions on the Colorado Plateau
Southwest Energy Development and Drought (SWEDD)
Soil family particle size class map for Colorado River Basin above Lake Mead
Soil geomorphic unit and ecological site group maps for the rangelands of the Upper Colorado River Basin region
Predictive soil property maps with prediction uncertainty at 30-meter resolution for the Colorado River Basin above Lake Mead
Below are publications associated with this project.
A quantitative soil-geomorphic framework for developing and mapping ecological site groups
A hybrid approach for predictive soil property mapping using conventional soil survey data
Digital mapping of ecological land units using a nationally scalable modeling framework
Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and random forests
Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data
POLARIS properties: 30-meter probabilistic maps of soil properties over the contiguous United States
Soil property and class maps of the conterminous United States at 100-meter spatial resolution
Approaches for improving field soil identification
Elevated aeolian sediment transport on the Colorado Plateau, USA: The role of grazing, vehicle disturbance, and increasing aridity
The automated reference toolset: A soil-geomorphic ecological potential matching algorithm
POLARIS: A 30-meter probabilistic soil series map of the contiguous United States
Machine learning for predicting soil classes in three semi-arid landscapes
Below are partners associated with this project.
- Overview
The field of digital soil mapping has bridged the classic theories of soil science into the modern computing age to produce high resolution predictive soil maps. This body of work utilizes classic soil factorial theory (soil = f[climate, organisms, relief (topography), parent material, time] + ɛ, or ‘clorpt’). The clorpt framework has been approximated using various environmental spatial data layers to predict soil types and properties between field observations, which are expensive and time consuming. We are using these mapping techniques, coupled with an expanding availability of field observations, to create a variety of soil property, soil classification, and land potential classifications across the western US. By developing a workflow that incorporates existing observations and new digital soil mapping techniques, we have been able to map soil themes at a broad scales to facilitate informed land management decisions.
Product: Soil property and class maps100-meter (328’) resolution soil property and class maps of the lower 48 US states
Product: A quantitative soil-geomorphic frameworkA quantitative soil-geomorphic framework for developing and mapping ecological site groups
Product: 30m Resolution soil maps for CO River BasinA broader set of 30-meter soil property maps now available for the Upper Colorado River Basin
Background & Importance
In the age of big data and complex modeling in science, quantitatively sound, high resolution soil maps are more important than ever. Soils play an important role as an interface in mediating water and nutrient movement in ecosystems that can vary greatly across landscapes. This spatial variation in soils can mean that different areas may be more or less resilient to human disturbance and climate change while still maintaining ecosystem services. Soils are at the heart of the ‘potential’ of an area to produce the foundational energy of photosynthetic vegetative production, maintain surface water supply and quality, and keep particulates and dust from threatening air quality. Understanding how soils of differing potential and vulnerability are distributed through a landscape with maps can help a multitude of researchers, land owners/managers, and policy makers to make informed decisions.
New 30-meter map of surface fine sand content of soils in the San Rafael Desert of Utah (left). Soils in this area are high in fine sand content making them more susceptible to wind erosion impacts as shown at right. General Methods
Digital soil mapping employs a vast set of environmental mapping layers with exhaustive spatial extent to help predict soil types and properties between field observations of soils. Locations where soil field observations exist are related to the environmental layers (topography, satellite imagery, climate, geology, and other remotely sensed products) with machine learning or artificial intelligence algorithms to predict soil type or properties in locations that scientists haven’t visited, but that are similar in environmental condition to places where soils have been sampled. This process is repeatable, easily updated, and provides robust measures of map accuracy and prediction uncertainty at every pixel.
Important Results
Recent developments include 1) 100-meter (328’) resolution soil property and class maps of the lower 48 US states, 2) POLARIS soil property maps at 30-meter resolution for the lower 48 states, and 3) a broader set of 30-meter soil property maps now available for the Upper Colorado River Basin (see links above). Additional studies lead by the USGS have applied DSM tools and products to assess oil and gas well-pad reclamation status and Colorado River salinity management. These products are also being used in conjunction with vegetation data, expert feedback, available state-and-transitions models, and laboratory data to create a new site potential classification system.
- Science
Below are other science projects associated with this project.
Wind Erosion and Dust Emissions on the Colorado Plateau
Wind erosion of soils and dust emissions are a significant resource management challenge on the Colorado Plateau. Loss of topsoil and associated aeolian sediment (wind-driven sediment) movement can lead to reduced soil fertility as well as abrasion and burial of vegetation. Dust in the atmosphere poses a threat to human health, visual resources, and regional water supplies (due to interactions...Southwest Energy Development and Drought (SWEDD)
Deserts of the southwestern US are replete with oil and gas deposits as well as sites for solar, wind, and geothermal energy production. In the past, many of these resources have been too expensive to develop, but increased demand and new technologies have led to an increase in exploration and development. However, desert ecosystems generally have low resilience to disturbance. More frequent... - Data
Soil family particle size class map for Colorado River Basin above Lake Mead
These data were compiled to support analysis of remote sensing data using the Disturbance Automated Reference Toolset (Nauman et al., 2017). The objective of our study was to assess results of pinyon and juniper land treatments. These data represent major soil types as defined primarily by soil texture and depth, but also geology, parent material, and geomorphology for relevant features that distiSoil geomorphic unit and ecological site group maps for the rangelands of the Upper Colorado River Basin region
This data release includes maps characterizing soil geomorphic units (SGUs), climate zones, and ecological site groups that classify landscapes by ecological potential and behavior for use in land management in the Upper Colorado River Basin (UCRB) region. Soil geomorphic units were created by analysis and grouping of ecological sites (ESs), a more detailed local system of ecological units managedPredictive soil property maps with prediction uncertainty at 30-meter resolution for the Colorado River Basin above Lake Mead
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, t - Publications
Below are publications associated with this project.
A quantitative soil-geomorphic framework for developing and mapping ecological site groups
Land management decisions need context about how landscapes will respond to different circumstances or actions. As ecologists’ understanding of nonlinear ecological dynamics has evolved into state-and-transition models (STMs), they have put more emphasis on defining and mapping the soil, geomorphological, and climate parameters that mediate these dynamics. The US Department of Agriculture NaturalA hybrid approach for predictive soil property mapping using conventional soil survey data
Soil property maps are important for land management and earth systems modeling. A new hybrid point-disaggregation predictive soil property mapping strategy improved mapping in the Colorado River Basin, and can be applied to other areas with similar data (e.g. conterminous United States). This new approach increased sample size ~6-fold over past efforts. Random forests related environmental rasteDigital mapping of ecological land units using a nationally scalable modeling framework
Ecological site descriptions (ESDs) and associated state-and-transition models (STMs) provide a nationally consistent classification and information system for defining ecological land units for management applications in the United States. Current spatial representations of ESDs, however, occur via soil mapping and are therefore confined to the spatial resolution used to map soils within a surveySalinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and random forests
Salinity loading in the Upper Colorado River Basin (UCRB) costs local economies upwards of $300 million US dollars annually. Salinity source models have generally included coarse spatial data to represent non‐agriculture sources. We developed new predictive soil property and cover maps at 30 m resolution to improve source representation in salinity modeling. Salinity loading erosion risk indices wRelative 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 dePOLARIS properties: 30-meter probabilistic maps of soil properties over the contiguous United States
Soils play a critical role in the cycling of water, energy, and carbon in the Earth system. Until recently, due primarily to a lack of soil property maps of a sufficiently high‐quality and spatial detail, a minor emphasis has been placed on providing high‐resolution measured soil parameter estimates for land surface models and hydrologic models. This study introduces Probabilistic Remapping of SSUSoil property and class maps of the conterminous United States at 100-meter spatial resolution
With growing concern for the depletion of soil resources, conventional soil maps need to be updated and provided at finer and finer resolutions to be able to support spatially explicit human–landscape models. Three US soil point datasets—the National Cooperative Soil Survey Characterization Database, the National Soil Information System, and the Rapid Carbon Assessment dataset—were combined with aApproaches for improving field soil identification
Use of soil survey information by non-soil-scientists is often limited by their inability to select the correct soil map unit component (COMP). Here, we developed two approaches that can be deployed to smartphones for non-soil-scientists to identify COMP from the location alone or location together with easily observed field data (i.e., slope, depth to the restrictive layer, and soil texture by deElevated aeolian sediment transport on the Colorado Plateau, USA: The role of grazing, vehicle disturbance, and increasing aridity
Dryland wind transport of sediment can accelerate soil erosion, degrade air quality, mobilize dunes, decrease water supply, and damage infrastructure. We measured aeolian sediment horizontal mass flux (q) at 100 cm height using passive aspirated sediment traps to better understand q variability on the Colorado Plateau. Measured q‘hot spots’ rival the highest ever recorded including 7,460 g m−2 dayThe automated reference toolset: A soil-geomorphic ecological potential matching algorithm
Ecological inventory and monitoring data need referential context for interpretation. Identification of appropriate reference areas of similar ecological potential for site comparison is demonstrated using a newly developed automated reference toolset (ART). Foundational to identification of reference areas was a soil map of particle size in the control section (PSCS), a theme in US Soil Taxonomy.POLARIS: A 30-meter probabilistic soil series map of the contiguous United States
A new complete map of soil series probabilities has been produced for the contiguous United States at a 30 m spatial resolution. This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database iMachine learning for predicting soil classes in three semi-arid landscapes
Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical - Partners
Below are partners associated with this project.