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Digital Soil Mapping: Past, Present and Future. Phillip R. Owens Associate Professor, Soil Geomorphology/ Pedology. Digital Soil Mapping. Also called predictive soil mapping. Computer assisted production of soils and soil properties.

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Digital soil mapping past present and future

Digital Soil Mapping: Past, Present and Future

Phillip R. Owens

Associate Professor, Soil Geomorphology/Pedology

Digital soil mapping
Digital Soil Mapping

  • Also called predictive soil mapping.

  • Computer assisted production of soils and soil properties.

  • Digital Soil Mapping makes extensive use of: (1) technological advances, including GPS receivers, field scanners, and remote sensing, and (2) computational advances, including geostatistical interpolation and inference algorithms, GIS, digital elevation model, and data mining

Digital soil mapping1
Digital Soil Mapping

  • These techniques are simply tools to apply your knowledge of soil patterns and distributions. The maps can only be as good as your understanding of the soils and landscapes

  • DSM - Same type of advancement to the Soil Survey as aerial photographs and stereoscopes introduced by Tom Bushnell and others early in the Survey.

Key point
Key Point

  • It is impossible to use these products and create good maps if you do not know your soil-landscape relationship.


  • Available soil data are increasingly numerical

    • Tools (GIS, Scanners, GPS,…

    • Soil Data Models

    • Increasing soil data harmonization

  • The spatial infrastructures are growing

    • DEMs: Global coverage

    • Remote Sensing

    • Web servers

  • Quantitative mapping methods

    • Geostatistics (pedometrics)

    • Data mining

    • Expert knowledge modeling


  • Essential tools of science

  • Viewing and organizing thoughts

  • Conceptual Models – framework to ponder thoughts

  • Simplify reality

  • Must generate testable hypothesis to separate cause and effect

  • New models must be advanced before facts can be viewed differently – break ruling theories

Dynamic nature of soils
Dynamic Nature of Soils

  • Society perceives soils as static

  • Pedologists deal with larger time scales – soils are dynamic

  • Many soil forming factors are active at a site – but only a few will be dominant

  • Importance of understanding soil dynamics- better predict results of management and evolution of soils

Types of models
Types of Models

  • Mental and Verbal – Most pedogenic models

  • Mathematical – Hope for the future

  • Simulation – Knowledge of rate transfers

Energy model runge 1973
Energy Model(Runge, 1973)

  • Similar to Jenny’s model, but emphasizes intensity factors of water (for leaching) and O.M. production

  • S = f(o, w, t) where:

  • W = water available for leaching (intensity factor)

  • O = organic matter production (renewal factor)

  • T = time

Energy model runge 19731
Energy Model(Runge, 1973)

  • Many researchers continue to show that infiltrating water is a source of organizational pedogenic energy.

  • Many critics say designed for unconsolidated P.M. with prairie vegetation.

Factors of soil formation
Factors of Soil Formation

  • S = (p, c, o, r, t, …) (Jenny, 1941)

    • Soils are determined by the influence of soil-forming factors on parent materials with time.

      • Parent material

      • Climate

      • Organisms

      • Relief

      • Time

Functional factorial model jenny 1941
Functional Factorial Model(Jenny, 1941)

  • Good conceptual model, but not solvable

  • Factors are interdependent, not independent

  • Most often used in research by holding for factors constant – i.e. topo-, clino-, bio-, litho-, chronosequences

  • Has had the most impact on pedologic research

  • Divide landscapes into segments along vectors of state factors for better understanding

Functional factorial model jenny 19411
Functional Factorial Model(Jenny, 1941)

  • Climate and organisms are active factors

  • Relief, parent material and time are passive factors, i.e. they are being acted on by active factors and pedogenic processes

  • Model has the most utility in field mapping – may be viewed as a field solution to the model

  • Very useful for DSM!

Dem derived terrain attributes
DEM Derived Terrain Attributes

  • These terrain attributes quantify the relief factor in Jenny’s Model

  • Some of the most commonly used are:

    • Slope;

    • Altitude Above Channel Network;

    • Valley Bottom Flatness;

    • Topographic Wetness Index (TWI).

Paradigm shift in pedology
Paradigm Shift in Pedology

  • S = (s, c, o, r, p, a, n, …) (McBratney, 2003)

    • Reformulation of Jenny 1941

    • Soil variability is understood as:

      • Soil attributes measured at a specific point

      • Climate

      • Organisms

      • Relief

      • Parent material

      • Age (time)

      • Space

Soils influence each other through spatial location!


Paradigm shift in pedology1
Paradigm Shift in Pedology

  • PCORT (Jenny, 1941)

    • Emphasizes soil column vertical relationships

    • Considers soils in relative isolation

    • Descriptive terms used for landscapes (e.g. “noseslope”)

  • SCORPAN (McBratney, 2003)

    • Accounts for lateral relationships and movements

    • Examines spatial relationships between adjacent soils

    • Terrain attributesused to quantify landscapes(“topographical wetness index”)

  • Catena – a “chain” of related soils (Milne, 1934)

    • Have properties that are spatially related by hydropedologicprocesses (Runge’s Model)

Digital Elevation Model

Dillon Creek, Dubois County, Indiana




Altitude Above Channel

Dillon Creek, Dubois County, Indiana


Topographic Wetness Index

Dillon Creek, Dubois County, Indiana


Multi Resolution Ridge Top Flatness

Dillon Creek, Dubois County, Indiana


Multi Resolution Valley Bottom Flatness

Dillon Creek, Dubois County, Indiana




Dillion Creek – Dubois County Indiana

Depth to Limiting Layer


Slope in Radians

Altitude above channel network (m)

Altitude above channel network

Olaf Conrad 2005 methodology

Multi-resolution index of valley-bottom flatness

Valley Bottom Flattness

Gallant, J.C., Dowling, T.I. (2003): 'A multiresolution index of valley bottom flatness for mapping depositional areas', Water Resources Research, 39/12:1347-1359

TWI: 9

Topographic Wetness Index

Soils in howard county
Soils in Howard County

  • 5 soils cover 80% of the land on Howard County

  • Are there relationships between these 5 soils and terrain attributes?

  • Can we use those relationships to improve the survey in an update context? Provide predicted properties?

Shaded Relief Elevation Model, 242 to 248 meters

Wetness Index, 8 to 20


Slope, 0 to 4%

Brookston Fincastle

Frequency distributions
Frequency distributions

Terrain attribute:Altitude above channel network

Terrain attribute:Curvature










*Data extracted with Knowledge Miner Software

Frequency wetness index
Frequency, Wetness Index

Terrain attribute:Wetness Index




Wetness index

*Data extracted with Knowledge Miner Software

Formalize the relationship
Formalize the Relationship

  • Example:

  • If the TWI = 14 then assign Brookston

  • If TWI = 10 then assign Fincastle

  • Other related terrain attributes (or other spatial data with unique numbers) can be used.

  • That provides a membership probability to each pixel

Terrain soil matching for brookston
Terrain-Soil Matching for Brookston

Fuzzy membership values (from 0 to 100%)



*Information derived from Soil landscape Interface Model (SoLIM)

Terrain soil matching for fincastle
Terrain-Soil Matching for Fincastle

Fuzzy membership values (from 0 to 100%)



*Information derived from Soil landscape Interface Model (SoLIM)

Create property map with solim
Create Property Map with SoLIM

To estimate the soil property SoLIM uses:

We already have Skij – the fuzzy membership value used to make the hardened soil map.

So we only need to specify Dk, the representative values of the property of interest for each soil

Dij: the estimated soil property value at (i, j);

Skij: the fuzzy membership value for kth soil at (i, j);

Dk: the representative property value for kth soil.

In this case, let’s assign values to carbonate depth for Fincastle and Brookston in the east section of the county.

Fincastle: 100 cm (low range of OSD) Brookston: 170 cm (high range of OSD)

Predicted depth to carbonates
Predicted depth to carbonates

100 to 170 cm

100 to 170 cm

Fuzzy vs crisp soil maps
Fuzzy vs. Crisp Soil Maps

  • Imagine a heap of sand…

  • The Heap Paradox from 4th Century BCE, more than 2,000 years ago posed a problem that can be addressed by fuzzy logic

  • Take away 1 sand grain. Is it still a heap? Take away 1 more and keep doing it. When is it not a heap? And what is it? Is it a pile, a mound? How many grains of sand does a mound have, a pile, a heap?

Heap of sand vs pile of sand
Heap of Sand vs. Pile of Sand

How many grains of sand do you need to remove from a heap to get a pile? How many grains of sand do you need to add to make your pile of sand into a heap?

Fuzzy vs crisp soil maps1
Fuzzy vs. Crisp Soil Maps

  • Fuzzy logic says that when you keep taking grains of sand away eventually you move from definitely heap, to mostly heap, partly heap, slightly heap, and not heap.

  • You can express heapness with values from 0 to 1, with 1 being a perfect example of a heap and 0 being nothing at all like a heap.

  • How can we define a heap? It is a similar question to how can we define a mapping unit.

  • You can set rules like a perfect heap is 2 tons or more of sand and not heap is less than ½ a ton of sand. You might also want an upper limit to where you say that after a certain amount it becomes more of a dune or mountain than a heap. You can then set a mathematical curve for expressing the decline in heapness as a function of the removal of sand grains.

Crisp vs fuzzy soil maps
Crisp vs. Fuzzy Soil Maps

  • Black is Brookston in the map below

  • Brown is a different soil, but similar to Brookston.

  • Orange is very different from Brookston and dark green is fairly different.

  • As we move away from Brookston in geographic space we cross a threshold and suddenly we are in a different soil. There is an abrupt conceptual change from one soil to another.

  • Black is Brookston in the map below

  • Orange is soil very different from Brookston.

  • Here we can express Brookston as values between 1 and 0

  • A given spot might have a 0.7 Brookston membership value

  • As we move up in elevation that membership value may decrease to 0.5, 0.3, 0.1, and 0 when we know we won’t find Brookston

Brief history of digital soil mapping
Brief History Of Digital Soil Mapping

  • 1991-1993: publications of pioneer works

  • 2003: Digital Soil Mapping as a body of soil science

  • 2004: 1st International workshop on Digital Soil Mapping. Workshops: Rio (2006), Logan (2008), Rome (2010), Sydney (2012)

  • 2009:

Solim in the us
SoLIM in the US

  • SoLIM “soil landscape inference model” was developed at the University of Wisconsin by A-Xing Zhu and Jim Burt (late 90’s)

  • Knowledge based inference model, fuzzy logic, rule based reasoning. What does that mean?

  • There were Soil Survey pilot projects in Wisconsin and the Smoky Mountains

Knowledge Documentation

The Polygon-based Model

Polygon Maps

Soil-Landscape Model Building

S <= f ( E )

Photo Interpretation

Manual Delineation

The Manual Mapping Process

Challenges in Conducting Soil Survey

(Slide from Zhu)

Case-Based Reasoning

Data Mining

Local Experts’ Expertise

Artificial Neural Network

Relationships between Soil and

Its Environment

Spatial Distribution



Cl, Pm, Og, Tp


(under fuzzy logic)

Similarity Maps


Overcoming the Manual Mapping Process

S <= f ( E )

(Zhu., 1997, Geoderma; Zhu, 2000, Water Resources Research)

















The Speed of Soil Survey Using SoLIM


A total of 500,499 acres since May 2001 over 526 person

days, about 950 acres per person per day

The product is already in digital form, no need

to digitize it

Currently the speed of manual mapping (including

Compilation and digitization) is about 80-100 acres

per person per day

(Slide from Zhu)

Inferred vs. Field Observed







Blue Mounds NE




Cross Plain SW





Quality of Results:

(Slide from Zhu)

Cost Comparison

Cost about $1.5 million to complete field mapping of

the County using the manual approach

Cost about $0.5 million using the SoLIM approach

in digital form

(Slide from Zhu)


  • There were major advances in DSM using SoLIM.

  • Some minor setbacks – Smoky Mountain project

  • “If a guy who has mapped these mountains for 20 years can’t tell you what soil is on the other side of the hill, then you can’t use a computer to do it.” Bill Craddock, Former State Soil Scientist in Kentucky

Dsm current state
DSM – Current State

  • There are many options under the umbrella of DSM: geostatistics (kriging and co-kriging), clustering, decision trees, Bayesian models, and fuzzy logic with expert knowledge.

  • There are advantages and disadvantages to all methods.

Dsm current state1
DSM – Current State

  • Knowledge based inference model like ArcSIE and SoLIM allows soil scientists to utilize their understanding of soil landscape patterns

  • Requires less data but knowledgeable soil scientists

  • ArcSIE is easier to use because it is within ArcGIS. SoLIM requires multiple file transfers

Dsm current state2
DSM Current State

  • ArcSIE used successfully in initial soil surveys in Missouri, Vermont and Texas

  • Requires environmental covariates and depends heavily on the DEM, terrain attributes and remote sensing (in the dry climates)

  • Explicitly describes Jenny’s state factor model by the expansion through McBratney’s SCORPAN

Dsm future
DSM - Future

  • DSM will be instrumental in soil survey updates. Research is currently underway to determine best methods

  • Digital delivery gives us the ability to illustrate and deliver soils in new formats (example Isee -

  • Using the fundamentals of DSM, we can move towards predicting soil properties and incorporating other explanatory data (i.e. ecologic site descriptions, land use, etc.)



Dillion Creek – Dubois County Indiana

Depth to Limiting Layer

Pros to digital soil mapping
“Pros” to Digital Soil Mapping

  • Very consistent product due to the way it is created.

  • The soil landscape model is explicit. Updates can be completed more efficiently over large areas.

  • The variability or inclusions can be represented (in some cases)

Pros to digital soil mapping1
“Pros” to Digital Soil Mapping

  • End users in the non traditional areas can more easily use some products.

  • We can use this information to make predictions of soil properties including dynamic soil properties.

  • All of these “pros” will increase the support and usefulness of the Soil Survey in the future.

Cons to digital soil mapping
“Cons” to Digital Soil Mapping

  • In some locations, the soil-landscape relationship is difficult to determine and represent. Examples are areas with heterogeneous parent materials.

  • Can be misused (It makes really pretty maps and a bad map is worse than no map at all)

  • Complications with data can stop a project.

  • Learning new softwares can be very frustrating

Saturated hydraulic conductivity (ksat , micrometers per second) from gridded SSURGO (Approximately 1:24,000 map. Gridded at 30 m resolution with STATSGO).



Dsm future1
DSM Future second) from gridded SSURGO (Approximately 1:24,000 map. Gridded at 30 m resolution with STATSGO).

  • Harmonize the soil data

  • Disaggregate polygons

  • Create true DSM maps tied to landscapes

  • Provide alternate raster products at multiple resolutions

  • We must embrace and use this technology and incorporate DSM into the long-term plan/vision.