Digital Soil Mapping: New Ideas and
Technologies to Explore Soil-Landscape
Relationships
Phillip R. Owens
Soil Geomorphologist/Pedologist - Associate
Professor, Purdue University

“Evolution of Phillip Owens”

1 person per 260 ha
10,000 people in
2,450 km2

Education
• B.S. - Soil Science – University of
Arkansas
• M.S. - Soil Science - University of
Arkansas – Research on soils and
septic systems
• Ph.D. – Texas A&M University –
Reduction-oxidation gradients along
soil toposequences

Timeline
• Congressional Science Fellow
• USDA-ARS Research Scientist –
Mississippi State University
• Assistant Professor – Purdue
University
• Associate Professor – Purdue
University

Currently….
• In-Coming Chair SSSA Pedology Division
• Associate Editor for SSSA Journal
• USDA – National Soil Survey Advisory
Committee
• Member of GlobalSoilMap.net project
• Facilitator for development of the Universal Soil
Classification System

Congressional Science Fellowship
Sponsored by- American Association
For the Advancement of Science and the
Tri-Societies
Office of Senator Blanche L. Lincoln - Arkansas

Senator Lincoln
• Chair of the Senate Agriculture
Committee
• Member - Taxation and IRS oversight
and International Trade
• Member of Committee on Aging

Issues I Worked On
• Farm Bill - Passed February 8, 2002
• Bioterrorism - Agriculture and human
• Agriculture Civil Rights Concerns
• Biodiesel amendment to Energy Bill
• ANWR
• Conservation Title of the Farm Bill
• Implementation of the Farm Bill
• Agriculture Appropriations

Back to Science….
• Steeplands of Southern Honduras
• Structural Heterogeneity verses
Functional Homogeniety
• Digital soil mapping tools and
practices
• Applications to solve problems

Characterization of Slumping Potential and Soils
in the Steeplands of Southern Honduras

Mass Movement
Infrastructure Damages
Domestic Hazards

Namasigue watershed (Honduras)
Soils Map
Soil Great Groups
Fluventic Haplustolls / Pachic Argiustolls
Typic Haplustalfs (mod. deep)
Typic Haplustalfs (deep)
Typic Haplustepts (mod. deep)
Typic Ustiorthents (shallow)
Typic Ustiorthents & Haplustepts (shallow)

Namasigue watershed soils map and landslides
Soil great groups Soil great groups vs. landslides
Fluventic Haplustolls / Pachic Argiustolls
Typic Haplustalfs (mod. deep)
Typic Haplustalfs (deep)
Typic Haplustepts (mod. deep)
Typic Ustiorthents (shallow)
Typic Ustiorthents & Haplustepts (shallow)
Landslides

Namasigue Watershed: Slope and Landscape Position
Classification Soil Slope Slump Landscape
(Great Groups) Depth Range % Position
Fluventic Haplustolls >1m 0 - 10% 0 Drainage
Pachic Argisutolls, deep Ways
Typic Haplustalf, deep >1m 10 - 45% <10 Backslopes
Typic Haplustalfs,
0.5 - 1m 45 - 60% 20-50 Backslopes
moderately deep
Typic Haplustepts, 0.5 - 1m 60 - 90% 10-35 Near Summit
moderately deep
Typic Ustiorthents,
0.25 - 1m >90% <10 Summit
shallow
Typic Ustiorthents,
0.25 - 1m 0 - 20% 0 Ridge Tops
Typic Haplustepts, shallow

Soil Factors Contributing
to Slope Instability
• Deep soils on 45-60%
• Rapid infiltration and permeability
• Moderately low water holding capacity
• DEM/GIS a powerful tool to extend site-
specific data to watershed landscape
models
• Soil attributes interactive with land
use, socioeconomic pressures, & extreme
storm events

Evolution of Research Continued
• Initial research focus on geostatistics
and pedometrics
• Example: Potassium availability

Potassium variability across a drainage
catena
• No-till past ~10 yr
• Soils differ by drainage
• No tile drainage in the field

Statistical procedure
• Linear model with correlated residuals (best linear unbiased
predictors)
• Comparisons made with Bonferroni correction
• Spatial autocorrelation modeled with a three-dimensional anisotropic
structure (lat, long, depth):
− Variance:
c
2 pk
exp k d i, j , k
k 1
− Where σ2 is the sample variance, d(i,j,k) is the absolute distance
between the kth coordinate, k = 1, …, c, of the ith and jth
observations in the input data set, and geometric anisotropy is
corrected by applying the rotation θ to the coordinate system (SAS
Institute Inc., 2003).

Topographical wetness index, TWI
TWI
Low :
High :
Value
sph_anis
6.5 - 7
N
6.0 - 6
5.5 - 6
5.0 - 5
High TWI:
4.5 - 5
4.0 - 4
--flat areas
< 4.0
--areas of convergent overland flow
Related to potassium availability?
PH3
labobs
LegendTWI = ln(a/tan(B)) (Quinn et al 1995).

• Surface
Exchangeable K runoff
• Leaching
K
mg/kg
250
50 5 cm
30 cm
60 cm

• Differential
Nonexchangeable K weathering
• Fixation
• Ferrolysis
K
mg/kg
2600
800
5 cm
30 cm
60 cm

Results
• Exchangeable K
−Related to TWI, and negative (p < 0.05)
−10 cm exchangeable K related to elevation
(p < 0.001)
−Higher in better drained soils at all depths
• Nonexchangeable K
−Strongly related to TWI, and negative (p <
0.001)
−Negatively related to extremes in elevation
−Higher in better drained soils at all depths*

Normalized yield maps averaged from all crops
(1995 to 2003), greens and blues represent
areas where the yield was above average,
red/yellow below average, blue- highest yields,
yellow- lowest yields

Yield Index - Corn: 1995, 1998, 2001
Yield Index - Soybeans: 1996, 1999, 2002
Yield Index - Winter Wheat: 1997, 2000, 2003

What I learned from these projects?
• Topography was the major predictor
for soil functional differences.
• Topography controls the water which
is the energy driving the system -
Hydropedology.
• Geostatistical approaches require
much data – expensive.

What did I want to know?
• Functional properties of soils and
how to represent that function
spatially.
• Paradigm shift for a pedologist!
• We think of structural heterogeneity
rather than functional homogeneity.

Classification
People – Age, sex, race, income, etc.
• That is a way to describe the structure of a
community, but it doesn’t describe how it
functions.
Soil – color, structure, texture, horizons, etc.
• No real description of how the soil functions
for crop growth, carbon sink, etc.
• Digital mapping can do that!

Thoughts on Digital Soil Mapping
• Most of the worlds information on
soils are in taxonomic class maps or
as tacit knowledge with soil scientists
• We need useable information now.
Point data takes time and money.
• Soil mapping with knowledge-based
inference mapping based on fuzzy
logic.

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
• …

Terrain Attributes Derived From DEM
Slope Gradient
Slope Curvature
Aspect
Hillshade
Contour
Altitude Above Channel Network
Valley Bottom Flatness
Topographic Wetness Index (TWI)

Slope
760 Km2
Slope in Radians

Altitude above channel network (m)
760 Km2
Altitude above channel network
Olaf Conrad 2005 methodology

Multi-resolution index of valley-bottom flatness
760 Km2
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
760 Km2
Topographic Wetness Index

Topographic Wetness Index (TWI)
• TWI is a measure of the potential for water to accumulate in
certain landscape positions:
• Where a = the upslope area in m2, per unit contour
length, contributing flow to a pixel, and b = slope angle acting
on a cell measured in radians (Quinn et al., 1995);
• There are 4 methods to calculate TWI, best methods are variable
and site specific (Sorensen et al., 2006);
• Assumption – vertical water flow is restricted.

Soil Survey – Illustrates soil taxonomic/morphologic differences
Fc
RuB2 Fc – Fincastle: Fine-
Fc
silty, mixed, superactive, mesic Aeric Epiaqualfs
· Fc
Kk
RuA
Bs – Brookston: Fine-
Loamy, mixed, superactive, mesic Typic
Argiaquolls
Kk – Kokomo:
Fc
Fine, mixed, superactive, mesic, Typic
Bs Argiaquolls
Pa – Patton: Fine-
silty, mixed, superactive, mesic, Typic
Fc Ca
Fc
Endoaquolls
Bs Ca – Carlisle muck: Euic, mesic, Typic
Haplosaprist
Limitations
•Soil Survey has hard boundaries
• Up to 0.8 Ha inclusions
Fc
•Created using best available
Fc technology at the time
Pa
Fc
0 45 90 180 270 360
Meters

Digital Soil Mapping with
Knowledge-Based Inference
• Define the area within a common geomorphic unit
• Develop terrain attributes from a digital elevation model
(terrain attributes – algorithms that describe topography)
• Determine the soil-landscape relationship (any
information you can find)
• Determine the centroids (central concepts) to determine
soil property terrain attribute relationship
• Set the rules in ArcSIE – If/Then statements that applies
fuzzy logic to apply soil properties

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?

Shaded Relief Elevation Model, Wetness Index, 8 to 20
242 to 248 meters
Slope, 0 to 4% SSURGO
0 0.5 1 2 Miles Brookston
0.80 Km 1.6 Km 3.2 Km Fincastle

Frequency distributions
Terrain attribute: Terrain attribute:
Altitude above Curvature
channel network
Frequency
Frequency
Fincastle Brookston
Fincastle
Frequency
Brookston
ABCN Curvature
*Data extracted with Knowledge Miner Software

Frequency, Wetness Index
Terrain attribute:
Wetness Index
Fincastle Brookston
Frequency
Wetness index
*Data extracted with Knowledge Miner Software

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
Fuzzy membership values (from 0 to 100%)
2%
100%
*Information derived from Soil landscape Interface Model (SoLIM)

Terrain-Soil Matching for Fincastle
Fuzzy membership values (from 0 to 100%)
98%
2%
*Information derived from Soil landscape Interface Model (SoLIM)

Create Property Map with SoLIM
To estimate the soil property SoLIM/SIE uses:
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.

Soil Carbon Content Estimates: Howard County, IN
"
Carbon Content from Measurements
CarbonEstimates
Value Kg /m2
High : 7 283 points collected
+/- 0.25 %
Low : 0
0 1,250 2,500 5,000 7,500 10,000
Meters

Cedar Creek Watershed (700 km2)

Structural Heterogeneity vs. Functional Homogeneity at
the Cedar Creek Scale
Year 2003
12
10
P = 665 mm
SF-BF (mm)
8
6 Data
4 TELM
SWAT
2
0
Taxonomic Soil Maps: Available Water Capacity: 125 175 225 275
Structural Heterogeneity Functional Homogeneity Day
TELM - Threshold-Exceedance-Lagrangian
Model (Basu et al., 2009)
SWAT – Soil Water Assessment Tool

Legend ZnC3 Zanesville silt loam, 6 to 12 percent slopes, severely eroded
SymbolDubois_Co_Dillon_Cr_Soil_Map_Unit
Map Unit
Dubois_Co_Dillon_Cr_Soil_Map_Unit Symbol Map Unit
Orange_Co_Dillon_Cr_Soil_Map_Units
Orange_Co_Dillon_Cr_Soil_Map_Units
SdvSoilM_2
MUName MUName
SdvSoilM_2
Ba Bartle silt loam AciG Adyeville-Tipsaw complex, 20 to 60 percent slopes
Bo Bonnie silt loam, frequently flooded AcmF Adyeville-Wellston silt loams, 18 to 50 percent slopes
Bu Burnside silt loam, occasionally flooded AgrA Apalona silt loam, 0 to 2 percent slopes
Cu Cuba silt loam, frequently flooded AgrB Apalona silt loam, 2 to 6 percent slopes
GlD2 Gilpin silt loam, 12 to 18 percent slopes, eroded AgrC2 Apalona silt loam, 6 to 12 percent slopes, eroded
GlD3 Gilpin silt loam, 12 to 18 percent slopes, severely eroded AgrC3 Apalona silt loam, 6 to 12 percent slopes, severely eroded
GlE Gilpin silt loam, 18 to 25 percent slopes BbhA Bartle silt loam, 0 to 2 percent slopes
GlE3 Gilpin silt loam, 18 to 25 percent slopes, severely eroded CwaAH Cuba silt loam, 0 to 2 percent slopes, frequently flooded, brief duration
GoF Gilpin-Berks complex, 20 to 50 percent slopes GacAW Gatchel loam, 1 to 3 percent slopes, occasionally flooded, very brief duration
GuD HcgAH Haymond silt loam, 0 to 2 percent slopes, frequently flooded, brief duration
Gilpin-Orthents complex, 12 to 25 percent slopes
JoA JoaA Johnsburg silt loam, 0 to 2 percent slopes
Johnsburg silt loam, 0 to 2 percent slopes
PeB PcrB Pekin silt loam, 2 to 6 percent slopes
Pekin silt loam, 2 to 6 percent slopes, rarely flooded
PeC2 PcrC2 Pekin silt loam, 6 to 12 percent slopes, eroded
Pekin silt loam, 6 to 12 percent slopes, eroded, rarely flooded
Pg StdAH Stendal silt loam, 0 to 2 percent slopes, frequently flooded, brief duration
Peoga silt loam
Sf WaaAH Wakeland silt loam, 0 to 2 percent slopes, frequently flooded, brief duration
Steff silt loam, frequently flooded
WhfC2 Wellston silt loam, 6 to 12 percent slopes, eroded
St Stendal silt loam, frequently flooded
WhfC3 Wellston silt loam, 6 to 12 percent slopes, severely eroded
TlA Tilsit silt loam, 0 to 2 percent slopes
WokAH Wellston-Adyeville-Ebal silt loams, 12 to 18 percent slopes, eroded
TlB Tilsit silt loam, 2 to 6 percent slopes
WpmD3 Wellston-Ebal-Adyeville complex, 12 to 18 percent slopes, severely eroded
W Water
WppD2 Wilbur silt loam, 0 to 2 percent slopes, frequently flooded, brief duration
WeC2 Wellston silt loam, 6 to 12 percent slopes, eroded
WeC3
ZnC2
Wellston silt loam, 6 to 12 percent slopes, severely eroded
Zanesville silt loam, 6 to 12 percent slopes, eroded
Orange County
ZnC3 Zanesville silt loam, 6 to 12 percent slopes, severely eroded
Orange_Co_Dillon_Cr_Soil_Map_Units
Orange_Co_Dillon_Cr_Soil_Map_Units
MUName
SdvSoilM_2 Dubois County
AciG Adyeville-Tipsaw complex, 20 to 60 percent slopes
AcmF Adyeville-Wellston silt loams, 18 to 50 percent slopes
AgrA Apalona silt loam, 0 to 2 percent slopes
AgrB Apalona silt loam, 2 to 6 percent slopes
AgrC2 Apalona silt loam, 6 to 12 percent slopes, eroded
AgrC3 Apalona silt loam, 6 to 12 percent slopes, severely eroded
BbhA Bartle silt loam, 0 to 2 percent slopes
CwaAH Cuba silt loam, 0 to 2 percent slopes, frequently flooded, brief duration
GacAW Gatchel loam, 1 to 3 percent slopes, occasionally flooded, very brief duration
HcgAH Haymond silt loam, 0 to 2 percent slopes, frequently flooded, brief duration
JoaA Johnsburg silt loam, 0 to 2 percent slopes
PcrB Pekin silt loam, 2 to 6 percent slopes
PcrC2 Pekin silt loam, 6 to 12 percent slopes, eroded
StdAH Stendal silt loam, 0 to 2 percent slopes, frequently flooded, brief duration
WaaAH Wakeland silt loam, 0 to 2 percent slopes, frequently flooded, brief duration
WhfC2 Wellston silt loam, 6 to 12 percent slopes, eroded
WhfC3 Wellston silt loam, 6 to 12 percent slopes, severely eroded
WokAH Wellston-Adyeville-Ebal silt loams, 12 to 18 percent slopes, eroded
WpmD3 Wellston-Ebal-Adyeville complex, 12 to 18 percent slopes, severely eroded
WppD2 Wilbur silt loam, 0 to 2 percent slopes, frequently flooded, brief duration
0 0.30.6 1.2 1.8 2.4
Kilometers
±

Legend
d3_ssur_par3
Depth of Soil (cm)
Valueto Lithic/paralithic (cm)
Depth
High : 190.873
Low : 20
Value
Tilsit_Bedford_Apallona_Johbsburg 0-2
Tilsit_Bedford_Apallona 2-6
Zanesville_Apallona_Wellston 6-12
Gilpin_Wellstone_Adyeville_Ebal 12-18
Gilpin_Ebal_Berks 18-50
Pekin_Bartle 2-12
Cuba 0-2
Steff_Stendal_Burnside_Wakeland 0-2
Rock Outcrop_Steep Slope > 50
0 0.30.6 1.2 1.8 2.4
Kilometers
±

Validation
• Dillion Creek Watershed – 127 geo-
referenced field observations
• Compared SSURGO RV predictions
vs. measured: Average difference =
57 cm
• Compared TASM predictions vs.
measured: Average difference = 22
cm

Location of Marcela Creek and
Lavrhina Creek
Source: Menezes (2011)

DIFFERENT
PHYSIOGRAPHICAL REGIONS
• MCW – Campos das
Vertentes
• Primarily Latosols
(Oxisols) in the watershed
• LCW – Serra da
Mantiqueira
• Primarily Cambisols
(Inceptisols)
•Headwater watershed

Method for estimating hydrologic recharge potential in two
watersheds in Brazil using knowledge-based inference
mapping

Validation with Conditioned Latin
Hypercube Sampling Scheme –
Lavrhina Creek Watershed

“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 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.

“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 (pretty maps not equal to
good maps)
• Complications with data can stop a project.
• Learning new softwares can be very
frustrating

Other projects… (Not digital soil
mapping)

GlobalSoilMap.net

Saturated hydraulic conductivity (ksat , micrometers per second) from gridded SSURGO
(Approximately 1:24,000 map. Gridded at 30 m resolution with STATSGO).
600
0

Available water capacity is a measure of how much water the soil can hold and make available to
plants. Intuitively, it is the difference between the moisture content at field capacity and the moisture
content at the permanent wilting point, which are represented in laboratory measurements as the
water contents at 33 kPa and 1,500 kPa, respectively. From gridded SSURGO (Approximately 1:24,000
map. Gridded at 30 m resolution). (SSURGO + STATSGO2)
135
0

Soil carbon content (from soil organic matter content). The carbon content is computed
from the organic matter content, accounting for the bulk density, volume of rocks, and a
conversion factor (0.58) for the mass of carbon per unit mass of organic matter. From
gridded SSURGO (Approximately 1:24,000 map. Gridded at 30 m resolution). (SSURGO
+ STATSGO2)
1194 g C m-2
0

Soil Moisture Regimes…..

The inputs for the GEN MOISTURE REGIME map of soil moisture regimes for
the conterminous U. S. using the java Newhall Simulation Model (jNSM)

The Soil Climate Regimes of the
U.S. (USDA-SCS, 1994)

Prism Data Processed Through
jNSM
• Summer Water Balance
• ½ Arcminute ~900 m

Cumulative number of days per year
Cumulative number of days per year
Number of days per year MCS is partly MCS is partly moist and partly dry
the moisture control section is dry
moist partly dry (no matter what temperature Key for maps a - c
and above 5 C
a b c
Consecutive days in the summer MCS Weather station locations used for
Consecutive days per year MCS is moist is dry validation of the model (~ 5000 stations)
Key for map e
d e f

Geographically Explicit Newhall Simulation Model map of soil moisture regimes made from gridded
output from PRISM data, STATSGO2 data, and elevation data run through Newhall Simulation
Model

Projects at CIAT
• Linking CIAT to global initiatives, like the
GlobalSoilMap.net project in Latin America.
• Capacity building of CIAT staff on taxonomic systems
for soil classification and digital soil mapping.
• Research and capacity-building interactions between
CIAT and CORPOICA through CIAT’s agreement with
the Colombian Ministry of Agriculture.

Projects at CIAT
• Prepare concept notes and proposals
for research projects to be jointly
executed by Purdue and CIAT.
• Strengthen CIAT’s partnership with
Purdue University.