Digital Soil Mapping: New Tools for Modern Land Management Decisions

Science Center Objects

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.

Background & Importance

Soil profile in an arid rangeland in New Mexico

Several feet of a soil profile in an arid rangeland in New Mexico. (Credit: Mike Duniway, USGS. Public domain.)

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 new 100-meter (328’) resolution soil property and class maps of the lower 48 US states.  The POLARIS project has predicted soil series across the lower 48 states at 30-meter (98’) resolution and soil property maps at 30-meter resolution for the country. 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.