Profiles and samples
Described horizons and laboratory measurements provide direct anchors but remain observations at selected locations and depths.
PREDICT THE LANDSCAPE, KEEP THE UNCERTAINTY
Digital Soil
Mapping
Digital soil mapping predicts a soil class or property across unsampled space by connecting measured soil observations with spatial environmental information through an explicit model.
5CHAPTERS4VISUAL BLOCKS4GRAPH LINKS2SOURCES
OBSERVE / SELECTED CONCEPTDigital Soil MappingStart with the role, then move through the editorial sequence.
HOW TO READ THIS PAGE
Visual explanationA diagram or operating scene makes the relationship visible.
Structured modelA flow, comparison, capability set, or boundary map organizes the idea.
Guided explanationOriginal prose connects the concept to its operating context.
01 / Overview
A soil map is a prediction
supported by observations.
USDA NRCS describes digital soil mapping as creating georeferenced soil databases through quantitative relationships between spatial environmental data and field or laboratory measurements.
The output may represent predicted classes or continuous properties. Its value depends on the sampling frame, depth definition, covariates, model, validation data, map resolution, intended use, and communicated uncertainty.
02 / Mapping pipeline
Observe points, model relationships,
predict surfaces, test independently.
01SAMPLE / 01Define the soil observationsLocation, depth, horizon, method, laboratory result, quality, and sampling design
02CONTEXT / 02Assemble spatial covariatesTerrain, parent material, climate, vegetation, imagery, survey, and geophysics
03MODEL / 03Fit spatial relationshipsTraining partition, algorithm, assumptions, scale, extrapolation, and prediction
04VERIFY / 04Validate the map productIndependent observations, error by class or range, spatial bias, uncertainty, and intended use
03 / Evidence layers
Each layer answers
a different soil question.
Existing soil information
Survey polygons, descriptions, interpretations, and legacy observations offer important prior context with their own scale and vintage.
Environmental covariates
Elevation derivatives, imagery, geology, climate, land cover, and other spatial layers help represent soil-forming controls and landscape position.
Proximal and geophysical sensing
Electrical, electromagnetic, spectral, and radar observations can add dense indirect evidence, but response and effective depth remain method- and site-specific.
04 / Map products
Map units and property rasters
are not interchangeable.
ProductRepresentsInterpretation boundary
Survey map unitA mapped soil-landscape concept that may contain multiple componentsThe polygon is not a uniform laboratory sample
Predicted soil classMost likely class or class probabilities at mapped locationsClassification accuracy and confusion vary across the map
Predicted property surfaceEstimated value for a defined property, depth, support, and time contextPrediction error, resolution, depth interval, and extrapolation must accompany use
Proximal sensor layerInstrument response collected along points or transectsIt is not the target soil property until locally calibrated and validated
05 / Boundaries
Pixel size is not
measurement certainty.
Resolution and support are different ideas.A small raster cell can display a detailed prediction even when the underlying observations are sparse or represent a different spatial support.
Indirect sensing needs local interpretation.NRCS notes that ground-penetrating radar effectiveness changes with soil conductivity, water, clay, salts, drainage, and the target feature.
A management zone is a separate decision layer.A soil map can inform field planning, but economic, agronomic, operational, temporal, and risk evidence must be added before prescribing an action.
06 / Connections
See the system around this concept.
Follow incoming and outgoing relationship records to understand what supplies, informs, enables, coordinates with, or extends this technology in the published knowledge graph.
Relationship radar / published edges4 records / 4 neighboring systems
Incoming01records point toward this concept
observe roleDigital Soil MappingSelected technology
Outgoing03records point from this concept
04connections visible
decide / Decision dataPrescription Maps ↗can contribute qualified soil evidence to
A validated soil class or property map can contribute one spatial evidence layer to an agronomically reviewed prescription workflow.
act / Irrigation controlVariable Rate Irrigation ↗can contribute reviewed spatial soil evidence to
A qualified soil map can contribute spatial context to a variable-irrigation management case, but pixel resolution, prediction uncertainty, current root-zone state, crop response, water supply, economics, prescription logic and machine feasibility remain separate.
observe / Soil intelligenceSoil Sampling Design and Provenance Assurance ↗provides mapped hypotheses and spatial units to
Digital soil layers can inform stratification and investigation while field sampling remains the independent source of physical evidence.
observe / Soil intelligenceSoil Electrical Conductivity Mapping ↗can contribute one spatial observation layer to
Soil electrical-conductivity mapping can contribute a georeferenced pattern to digital soil mapping when instrument, depth sensitivity, moisture, salinity, texture, timing, ground reference, and uncertainty remain explicit.
3CONNECTED ROUTES1–2STEP POSITIONS22ROUTE SOURCE LINKS
Operating practice
From soil and weather evidence to an irrigation decision
Follow the water-management evidence stack from spatial soil context and local weather through root-zone sensing, a qualified scheduling decision, and field verification.
CURRENT POSITION01
01 / PLACE
Open the complete route ↗Start with spatial soil context
Understand how measured observations and environmental covariates become a soil prediction with explicit uncertainty.
07 / Evidence
Primary sources.
This briefing uses USDA NRCS material on digital soil mapping and ground-penetrating radar. It explains concepts and uncertainty without reproducing official soil databases, map products, standards, or interpretation tables.
01
Digital Soil Mapping Focus TeamUSDA Natural Resources Conservation Service · Accessed 2026-07-20
02
Ground-Penetrating RadarUSDA Natural Resources Conservation Service · Accessed 2026-07-20