GRIP

New Technologies for DSM to Support

Traditional Soil Survey

M.C. Nolin, I. Perron, M.A. Niang,

M. Bernier et X. Geng

2012 CLRN Workshop, Lac Beauport Sunday, June, 3rd 2012

10MOA01001

André Martin Field & Labo Technologist

M. Bernier, Ph.D. (INRS-ETE)

K. Chokmani. Ph.D.

(INRS-ETE)

C. Codjia, Ph.D. (UQAM)

Michel Nolin, Ph.D. Digital Soil Mapping

Gaetan Bourgeois, Ph.D.

Agrométéorology

SAGES

GRIP 3

Mohamed Abou Niang, Ph.D. Radar Remote Sensing

Isabelle Perron, M.Sc. GIS & Remote Sensing

C

L

I

M

B

RS TEAM

A. Michaud, Ph.D. (IRDA)

Xiaoyuan Geng (AES/AAFC)

REZOTAGE

AESB

J-Y. Drolet (AES/AAFC)

Stéphane Gariepy

(AES/AAFC)

C

a

n

S

I

S

GRADUATE STUDENTS & TRAINING

SAGES

S. Perreault

(M.Sc. INRS-ETE)

K. Chokmani

& G. Bourgeois

J.D. Sylvain

COOP, U. Sherbrooke

A. Michaud & G. Bénié

K. Labrecque

(M.Sc. INRS-ETE)

M. Bernier

& D. Cluis

M. Quenum

(Ph.D. INRS-ETE)

D. Cluis & M.

Bernier

H. Paucar-Munoz.

(M.Sc. INRS-ETE)

M. Richer-Laflèche

& J.C. Aznar

M. Breton

(M.Sc. UQAM)

C. Codjia GRIP

WEB’S

Pedotransfer Functions

DIGITAL SOIL MAPPING Maximum

P Sorption

Capacity

(SIL 1)

Texture &

Drainage

(SIL 3)

Moisture &

Drainage (SIL 1)

Texture &

O.M. (SIL 1)

Texture &

O.M. (SIL 1-2)

GRISE-REZOTAGE

4

Outline

• State of Soil Mapping in Quebec

• Digital Soil Mapping

• Ancillary variables and Tools

• Scale-Based Results (SIL)

– Soil Drainage Classes

– Surface Soil Texture Groups

– Sand, Silt, Clay & O.M. Contents

• Conclusions Luc Lamontagne

5

State of Soil Mapping in Quebec

27

27

56

TERREBONNE

ARGENTEUIL

DEUX-MONTAGNES

VAUDREUIL

SOULANGES

HUNTINGTON

BEAUHARNOIS

BROME

MISSISQUOI

SHEFFORD

RICHMOND

SHERBROO KE

COMPTON

STANSTEAD

CHATEAUGUAY

NAPIERVILLE

LAPRAIRIE

CHAMBLY

ROUVILLE

IBERVILLLE

ST-JEAN

VERCHÈRES

ST-HYACI NTHEBAGOT

RICHELIEU

DRUMMOND

FRONTENAC

BEAUCE

LOTBINIÈRE

MÉGANTI C

ARTHABASKA

NICOLET

YAMASKA

DORCHESTER

LÉVISBELLECHASSE

MONTMAGNY

L'ISLET

Ïle -a ux -Co udres

Île d 'o rléans

Ïl e-aux-G rues

KAMOURASKA

RIVIÈRE-DU-LOUP

TÉMISCOUATA

RIMOUSK I

MATANE

MATAPÉDIA

GASPÉ-OUEST

BONAVENTURE

GASPÉ-EST

CHARLEVOIX-EST

CHI CO UTIMI

LAC ST-JEAN-EST

LAC ST-JEAN-OUEST

MONTMORENCY(Cô te-de-Bea upré)

QUÉBEC

PORTNEUF

Champlain-Lav io le tte

ST-MAURICE

MASKI NO NG É

BERTHIER

JOLIETTE

MONTCALM

L'ASSOMPTION

HULL

PAPINEAU

LABELLE

GATINEAU

PONTIAC

TÉMISCAMINGUE

ABITIBI

WOLFE

CHARLEVOIX-OUEST

Sai nt -Roch- de- Richelieu

Fleu

ve S

aint-L

auren

t

Ba ie-d es- Chale urs

Ile d 'A n ti co sti

Rivi è

r e d e

s Pr a

ir ies

Rivièr

e Bati s

can

659 feuillets 1: 20 000

La pédologie au Québec

Area mapped = 12 608 679 ha

6

0

1 000 000

2 000 000

3 000 000

4 000 000

5 000 000

40-50 50-60 60-70 70-80 80-90 90-00 >00

Area (ha)

Year

Age of Soil Survey Data available in Quebec

SIL 2 1:20 000

Local/Farm

SIL 1 1:10 000

Field

SIL 3 1:50 000 Regional

SIL = Survey Intensity Level

SIL 5 1:1 000 000

Synthesis

SIL 4 1:125 000

Exploratory

276 257

4979

0

50

100

150

200

250

300

Lotbinière Lévis Dorchester Beauce

Superficie moyenne des polygones (ha)

0.0

0.2

0.4

0.6

0.8

1.0

Lotbinière Lévis Dorchester Beauce

1 sol

2 sols

3 sols

4 sols

County Year Scale

Lotbinière 1957 1 :63 360

Lévis 1962 1 :63 360

Dorchester 1975 1 :50 000

Beauce 1995 1 :50 000

Soil Survey Data Integration in Watershed Studies

Bras d’Henri Watershed (WEBs), Quebec (167 km2)

Number of Soil Series used to define Map Units

Average Polygon Area (ha)

x 100 %

7

Old

Recent

SOIL DRAINAGE CLASSIFICATION SOIL WATER REGIME

- - - - - - - - XERIC - - - - - - - - - - - - MESIC - - - - - - - - - - - - HYDRIC - - - - - - -

30

60

90

15

45

75

Prof.

(cm)

Brun

pâle

avec

taches

de rouille

Gris

brunâtre

avec

taches

de rouille

Gris

avec

taches

de rouille

Gris à

gris-bleu

avec

taches

de rouille

Marbrures

OM

Gley

Depth

< - - - - - - - Soil Color - - - - - - - >

< - - - - - Texture - - - - - >

Source: Laboratoires de pédologie et d’agriculture de précision, AAC

D1 D3 D4 D5 D6 D7 D2

Source: Adapté de http://www.css.cornell.edu/courses/260/Lab%20Hydric%20Soils.pdf

D1

Excessively

drained

D4

Moderately

drained

D5

Imperfectly

drained

D6

Poorly

drained

D7

Very poorly

drained

D3

Well

drained

D2

Rapidly

drained

9

Precision of Traditional Soil Survey Maps

Bl40% + Le30% + Mai20% + Wo10%

D3-D4

D3-D4

D4-D5

D5-D6

D6-D7

[D320%+D420%]+[D315%+D415%]+[D510%+D610%]

+[D45%+D55%]

D335% + D440% + D515% + D610%

Ouellet et al. 1995

10

Digital Soil Mapping (DSM) • Traditional Soil Mapping is time-

consuming, expensive and often

not enough precise to address soil

data user needs (modeling, agri-

environmental decision taking, …)

• Alternative mapping methods are

required

• Digital Soil Mapping has emerged

since 1980 with the development

of new technologies: GIS, GPS,

Geostatistics, Digital Elevation

Model, Remote Sensing, Proximal

Sensing (Pedometrics)

McBratney, A. B., Santos, M. L. M.,

Minasny, B., 2003. On digital soil mapping.

Geoderma 117(1-2): 3-52.

Scull, P., J. Franklin, O.A. Chadwick et D.

McArthur. 2003. Predictive soil mapping - a

review. Progress in Physical Geography 27

(2): 171–197.

12

DSM METHODOLOGICAL FRAMEWORK USED IN GRIP 1, 2 & 3 PROJECTS

Soil Legacy Data

RADARSAT-2

Ancilary Variables

RADAR

Sand, Silt, Clay

and O.M. Contents

Secondary and Tertiary Soil Properties (PTF)

LUT**

Bare Soils

Pasture and Forage

Forests

Others

SPATIAL INFERENCE

MODELS Validation (V)

Analytical Morphological Training (T)

Monteregie Soil Profile

Database Optical

L5 TM L7 ETM IKONOS QUICKBIRD

Surface Texture

Groups

Gamma DEM

** LUT: Land Use Type

Drainage

Classes

ILR* Trans-

formation

* ILR: Isometric LogRatio

13

Spatial Inference Models

used in DSM

• Statistical Methods (GRIP 1, 2 & 3)

• Multiple Regression

• Multiple Discriminant Analysis

• Canonical Correlation

• Decision Tree Classifier

• Geostatistical Methods (GRIP 3)

• Ordinary Kriging

• Cokriging

• Regression Kriging

• Other (GRIP 3)

• Support Vector Machine Regression (non-linear)

14

ANCILLARY VARIABLES & TOOLS ACCORDING TO SCALE

SIL 2 1:20 000

Watershed/Farm BMP

SIL 1 ≥ 1:10 000 Within-Field

Precision Agriculture

SIL 3 1:50 000 Regional Planning

REMOTE SENSING

PROXIMAL SENSING

Satellite

Airborne

Medium Spatial Resolution

(≥ 15 m)

Fine Spatial Resolution

(< 15 m)

LANDSAT

ASTER

SPOT

IKONOS

QUICKBIRD

Soil EC Gamma Ray

GEOEYE1

TOOLS AND ANCILLARY VARIABLES SENSING TYPE TOOLS SPATIAL

RESOLUTION

SIL ANCILLARY

VARIABLES

REMOTE

(satellite)

Optical Imagery LANDSAT-5, 7

ASTER

Medium

(≥ 15 m)

2-3 Spectral Bands or

Indices (Ratio)

QUICKBIRD

IKONOS

Fine

(<15 m)

1-2 Spectral Bands or

Indices (Ratio)

Radar Imagery

(SAR)

RADARSAT-1 Standard and

Fine

2-3 Polarisation (HH)

RADARSAT-2

Standard, Fine

and Ultrafine

1-3 Multipolarisation and

Polarimetric Parameters

PROXIMAL Electrical /

Electromagnetic

Survey

Geonics EM-38

VERIS-100

GEM-2

Variable 1-2 Soil Electrical

Conductivity (EC);

At different depths

Gamma

Radiometry

RS700 Variable

1-2 40K, 238U & 232Th

Ratio

Digital

Elevation

Model (DEM)

Topographic

Survey

DGPS-RTK

LIDAR (airborne)

ASTER, SAR

Variable

1-3

Altitude, Slope and

Topographic Indices

(Wetness, CTI, etc.) 15

16

ANCILLARY VARIABLES ACCORDING TO

THE ELECTROMAGNETIC SPECTRUM

Source : McBratney et al. (2003)

Optical Imagery with Medium Spatial Resolution (≥15 m)

LANDSAT-5 & LANDSAT-7 (FREE)

• Multispectral (Visible, NIR, MIR), frequent, under various acquisition conditions (moisture and vegetation), free.

10 mai 2005

LANDSAT 5 (TM)

Spatial Resolution:

15 m (0)

30 (1-5, 7)

120 m (6)

Band Wavelength (µm)

1 Blue 0.45 - 0.52

2 Green 0.52 - 0.60

3 Red 0.63 - 0.69

4 Near IR 0.76 - 0.90

5 MIR 1.55 - 1.75

7 MIR 2.08 - 2.35

6 Thermal IR 10.4 - 12.5 8 juin 2001

LANDSAT 7 (ETM+)

Résolution spatiale :

15 m (0)

30 (1-5, 7)

60 m (6)

Date : May, 30th 2008

Time : 3:58 pm

Resolution : 2.4 m

Wavelength :

0.43-0.53 (Blue)

0.47-0.62 (Green)

0.59-0.71 (Red)

0.72-0.92 (NIR)

Date : May, 13th 2008

Time : 3:51pm

Resolution : 4 m

Wavelength :

0.45-0.52 (Blue)

0.52-0.60 (Green)

0.63-0.69 (Red)

0.76-0.90 (NIR)

IKONOS Quickbird

Optical Imagery with Fine Spatial Resolution (<15 m)

$$$$ $$$$

Shadow

Clouds

RADARSAT-2 (under cloudy conditions and during night)

• Good Relationship between Radar Signal and Surface Soil Moisture

Classification of Soil Water Regime (Drainage, Permeability, AWC, etc.)

• Multipolarisation (simple, double or quadruple) Soil Surface Roughness

• Polarimetric Signature Specific to Soil Drainage Class (Niang et al. 2010)

• Variable Spatial Resolution : ultra-fine (3 m); fine (9 m); standard (12 m).

21

A

B

C

30 cm

100 cm

Mode horizontal

Mode vertical

jusqu’à 75 cm

jusqu’à 150 cm

a) b)

solénoïdes

PROXIMAL

SENSING

Soil

Electrical

Conductivity

Geonics EM-38 VERIS 3100

Without Soil Contact With Soil Contact

(Electromagnetic Method)

c) GEM-2 (Geophex Ltd.)

(electromagnetic c.)

4/15 frequencies :

Frequencies: 330 Hz - 47kHz. Source : Paucar-Munoz (2010) 23

1170, 3930, 13590 et 25410 Hz

Maximum Depth: 3m

PROXIMAL SENSING

Soil Electrical Conductivity

24

5 m

2 m

Measurement Density: 600-700 measures per ha

SCALE

Soil Electrical Conductivity Survey

Source : Paucar-Munoz (2010) 25

RS 700 (Radiations Solutions Inc.)

Acquisition System

Gamma Radiometry

Airborne or Proximal Sensing

26

DEM Digital

Elevation

Model

DGPS-RTK

or LIDAR

Analytical Database Variable

Morphological Database Covariable

N = 4407 N Training = 3055 N Validation = 1352

N = 48199 profiles

27

A Horizon SOIL LEGACY DATA

SIL 2 1:20 000

1 per 4 ha

SIL 3 1:40 000 1 per 16 ha

Conversion of soil textural subclasses into sand, silt and clay contents

Field Method

Texture by feel

Laboratory

Hydrometer Method

ILR Transformation

29

Soil Surface Texture SandM SiltM ClayM ILR1M ILR2M

Subclass Code (%) (%) (%)

Coarse sand 11 87.9 7.0 5.1 1.29155 1.78918

Sand 12 89.9 6.5 3.6 1.55487 1.85750

Fine sand 13 89.0 7.5 3.5 1.63218 1.74919

Very fine sand 14 87.8 8.5 3.7 1.63237 1.65109

Loamy coarse sand 15 83.3 9.9 6.8 1.17622 1.50608

Loamy sand 16 81.9 12.2 5.9 1.37050 1.34638

Loamy fine sand 17 82.5 11.7 5.8 1.37036 1.38113

Loamy very fine sand 18 79.1 15.4 5.5 1.50872 1.15707

Coarse sandy loam 19 67.3 19.5 13.2 0.82431 0.87593

Sandy loam 20 65.6 22.6 11.8 0.96564 0.75351

Fine sandy loam 21 64.1 23.8 12.1 0.95682 0.70057

Very fine sandy loam 22 62.3 27.4 10.3 1.13420 0.58083

Loam 23 41.5 38.9 19.6 0.58609 0.04575

Silt loam 24 23.8 58.5 17.7 0.60894 -0.63593

Silt 25 8.0 86.0 6.0 1.20444 -1.67931

Sandy clay loam 26 5.0 23.2 23.8 0.31642 0.58417

Clay loam 29 30.0 38.0 32.0 0.04381 -0.16715

Silty clay loam 30 13.1 52.8 34.1 -0.21207 -0.98564

Sandy clay 31 48.4 13.4 38.2 -0.33106 0.90810

Silty clay 32 9.4 44.6 46.0 -0.66089 -1.10098

Clay 33 16.1 33.4 50.5 -0.63547 -0.51600

Heavy clay 34 5.7 27.5 66.8 -1.36712 -1.11279

* SandM, SiltM, and ClayM are the average values of sand, silt, and clay content (%) of each textural

subclass; ILR1M and ILR2M are the isometric logratio transformation of SandM, SiltM, and ClayM.

30

DIGITAL SOIL MAPPING

SCALE-BASED RESULTS

(SIL 3 – SIL 2 – SIL 1)

31

Digital Mapping of Soil Drainage Classes at Regional Scale (SIL 3)

Source : Niang et al. (AESS, accepté)

31

GRIP 1 Project

SECTOR : Bras d’Henri Watershed

ANCILLARY VARIABLES: ASTER et RADARSAT-1 (HH)

DSM METHOD:

Decision Tree

Overall Accuracy: 48-85 %

according to Land Use Type

DSM METHOD:

Discriminant Analysis

Overall Accuracy: 39-67 %

according to Land Use Type

Source : Niang et al. (2012a)

Applied and Environmental Soil Science

doi:10.1155/2011/430347

33

Digital Mapping

of Soil Surface

Texture Group at

Regional Scale (SIL 3)

GRIP 2 Project

SECTOR : Monteregie (4558 km2)

ANCILLARY VARIABLES: LANDSAT-7 &

RADARSAT-1,-2 (HH)

DSM METHOD:

Discriminant Analysis

Overall Accuracy: 79 % (Bare Soils)

(59–84 % according to Texture Group (T))

LANDSAT-7: 72 %

RADARSAT-1,2: 60 %

Source : Niang et al. (2012b)

Can. J. Remote Sensing 37(5) : 548-563

34

GRIP 2 Project

SECTOR : Rouville (25 x 25 km)

ANCILLARY VARIABLES:

1 RADARSAT-2 Image

(FQ4, 11/05/2009)

Multipolarisation + 35 Parameters

- Polarimetric Decomposition)

DSM METHOD:

Discriminant Analysis

Overall Accuracy: 72 % (Bare Soils)

(55–77 % according to Texture Group (T))

Source : Niang et al. (2012b)

Can. J. Remote Sensing 37(5): 548-563.

Digital Mapping of Soil Surface Texture Group at Local Scale (SIL 2)

35

Variogram of Clay Content

Computed with the Analytical Database

Transect Length : 12 km; Lag size : 200 m

C0

C

C0 = Nugget Effect

(Random Variation)

C = Structured Variance

(Systematic Variation)

Ratio: C/(C0+C)

Sill = C0+C

Spatial Structure

Source. : Whelan et McBratney (2000)

<0.25 Weak (W)

0.25-0.4 Moderate-Weak (MW)

0.4-0.6 Moderate (M)

0.6-0.75 Moderate-Strong (MS)

>0.75 Strong (S)

C/(C0+C) Spatial Structure

SPATIAL STRUCTURE

• The Spatial Structure of Primary Soil Properties in Monteregie

present is strong (C/(C0+C)=0.81-0.96).

• Excellent for producing DSM with geostatistical methods.

RESULTS

37

Spatial Structure of Selected Primary Soil Properties of the Surface layer of the Montérégie (SIL 3)

Variables Unit n Lag (m) ModelZ Ratio Range (m)

Max. Min. C/(C0+C) major minor

Sand % 2940 12000 200 Exp. 0.87 2500 1400

Silt % 2940 12000 200 Exp. 0.81 2700 1700

Clay % 2940 12000 200 Exp. 0.87 3500 1400

ILR1 - - - 2940 12000 200 Exp. 0.83 3500 1500

ILR2 - - - 2940 12000 200 Sph. 0.88 6000 3000

Fine Sand % 1712 12000 200 Exp. 0.89 2500 1600

Very Fine Sand % 1712 12000 200 Exp. 0.96 2000 1100

Organic C % 3027 12000 200 Exp. 0.96 5000 3000

Z Exp. : Exponential Model; Sph. : Spherical Model.

• Using Morphological Soil Database reduces RMSE associated

to DSM of Surface Layer Particle Size Fractions.

• ILR Transformation improves normal distribution conditions,

reduces RMSE and insures that «Sand + Silt + Clay = 100».

RESULTS

Usefulness of Morphological Soil Database in DSM – Monteregie (SIL 3)

39

Variablez Unit Interpolation Covariabley n RMSEx

Method Absolute Reduction (%)x

Sand % Kriging - - - 793 11.37 - - -

Cokriging SandM 793 10.52 ▼ 7.5

Silt % Kriging - - - 793 7.86 - - -

Cokriging SiltM 793 7.52 ▼ 4.3

Clay % Kriging - - - 793 7.57 - - -

Cokriging ClayM 793 7.21 ▼ 4.3

SandCA % Kriging - - - 796 11.69 - - -

Cokriging ILR1,2M 796 10.29 ▼ 12.0

SiltCA % Kriging - - - 796 8.12 - - -

Cokriging ILR1,2M 796 7.51 ▼ 7.5

ClayCA % Kriging - - - 796 7.66 - - -

Cokriging ILR1,2M 796 7.20 ▼ 6.0

Fine Sand % Kriging - - - 526 6.97 - - -

Cokriging FSM 526 6.77 ▼ 2.9

Very Fine Sand % Kriging - - - 525 3.96 - - -

Cokriging VFSM 525 3.96 0.0

Z SandCA, SiltCA and ClayCA : Sand, silt and clay contents computed by back-transforming ILR transformation (Compositional Analysis). Y SandM, SiltM, ClayM, FSM et VFSM : average values of sand, silt, clay, fine sand, and very fine sand contents of each texture subclass

measured in the field by feel; ILR1,2M : ILR transformation of these average values (Compositional Analysis).

x RMSE : Root Mean Square Error; Reduction (%) = ((RMSEKriging–RMSECokriging)/RMSEKriging)*100.

DSM of the Surface Layer Clay Content in Monteregie

40

a) Kriging b) Cokriging with

ILR1M and ILR2M

Poster of Nolin et al. at the 2012 SCSS-AQSSS Meeting (2012-06-06).

0 2,5 5 7,5 km

Covariable : Best Subset of RADARSAT-2 Polarimetric Parameters

41

a) Ordinary Kriging (OK) b) Regression Kriging (RK) c) SVM* Regression

Sand (%)

Other

Scale

OK ___CK*___ ___RK___ ___SVM*___

Sand (%) 13.7 13.4 (▼3%) 11.8 (▼14%) 11.5 (▼17%)

Silt (%) 10.8 7.8 (▼28%) 8.2 (▼25%) 7.7 (▼29%)

Clay (%) 11.4 11.1 (▼3%) 9.4 (▼17%) 8.2 (▼28%)

RMSE : OK > CK* > RK > SVM*

Usefulness of Polarimetric SAR Data (RADARSAT-2) Section of Rouville County (SIL 2)

* SVM : Support Vector Machine; CK : Cokriging.

Speech of Niang et al. at the 2012 SCSS-AQSSS Meeting (2012-06-05).

Usefulness of Fine Spatial Resolution Optical Imagery to DSM

Experimental micro-watershed - Bras d’Henri (WEBs)

• Using spectral reflectance (Red) extracted from a Quickbird image

reduces RMSE associated to DSM of Sand, Silt and Clay contents.

RESULTS

43

Variable Interpolation Covariable Quickbird IKONOS

Method RMSE RMSE

Abs. Red. (%) Abs. Red. (%)

Sand Kriging --- 15.08 --- 15.08 ---

Cokriging Blue 14.82 ▼ 1.7 15.24 ▲ 1.1

Cokriging Green 15.00 ▼ 0.5 15.68 ▲ 4.0

Cokriging Red 14.32 ▼ 5.0 15.24 ▲ 1.0

Cokriging NIR 15.25 ▲ 1.1 15.15 ▲ 0.5

Silt Kriging --- 12.99 --- 12.99 ---

Cokriging Blue 12.86 ▼ 1.0 13.14 ▲ 1.2

Cokriging Green 12.93 ▼ 0.5 13.23 ▲ 1.8

Cokriging Red 12.53 ▼ 3.5 13.14 ▲ 1.2

Cokriging NIR 13.16 ▲ 1.3 13.03 ▲ 0.4

Clay Kriging --- 3.02 --- 3.02 ---

Cokriging Blue 2.85 ▼ 5.8 3.03 ▲ 0.2

Cokriging Green 3.04 ▲ 0.6 3.32 ▲ 10.0

Cokriging Red 2.65 ▼ 12.4 3.02 0.0

Cokriging NIR 3.01 ▼ 0.5 2.98 ▼ 1.4

x RMSE: Root Mean Square Error; Reduction (%) = ((RMSEKriging–RMSECokriging)/RMSEKriging)*100.

Speech of Perron et al. at the 2012 SCSS-AQSSS Meeting (2012-06-05).

Digital Mapping

of Soil Drainage

at the Field Scale (SIL 1)

CASI CONVAIR-580

(RADARSAT-2)

DSM METHOD: Canonical Correlation

Accuracy = 0.85 Accuracy = 0.79

Source : Liu et al. (2008) Geoderma 143: 261-272.

Poor D6

Imperfect D5

Moderate D4

Well D3

EC (VERIS)

Accuracy = 0.79

DEM (RTK)

Accuracy = 0.70

EC + DEM

Accuracy = 0.84

GRIP 1

Poor D6

Imperfect D5

Moderate D4

Well D3

44

Digital Mapping of Soil Surace Texture

at the Field Scale (SIL 1)

Source : Paucar-Munoz et al. (2010) n=109

Clay (%)

c) Ordinary Kriging a) Cokriging

(GEM2+DEM)

b) Regression Kriging

(GEM2+DEM)

Properties Mean Cokriging Regression Kriging Ordinary Kriging

MAE Reduction (%) MAE Reduction (%) MAE

Sand (%) 62.6 6.6 ▼ 23 8.5 ▼ 1 8.6

Silt (%) 28.0 6.0 ▼ 22 7.8 ▲ 1 7.7

Clay (%) 9.4 1.4 ▼ 30 1.9 ▼ 5 2.0

45

* MAE: Mean Absolute Error; Reduction (%) = ((MAEKriging–MAECokriging)/MAEKriging)*100.

DSM of O.M. Content of the Surface Soil Layer at the Field Scale (SIL 1)

Source : Paucar-Munoz (2010)

n=164

46

Property Mean Cokriging (Gamma) Cokriging (GEM-2) Ordinairy Kriging

MAE Reduction (%) MAE Reduction (%) MAE

O.M. (%) 6.4 0.22 ▼ 21 0.29 ▲ 4 0.28

49

Conclusions • Using Ancillary Variables (Soil Legacy Data, DEM,

Remote & Proximal Sensing) significantly improves

the precision of DSM (reduces Error) – Choosing the

Best Strategy according to the scale (SIL), Data

available, Cost and Study Area Variability.

• DSM Precision is also a function of Spatial Inference

Models – SPATIAL STRUCTURE NEEDED

• Future Works: Efficient Sampling Strategy (Minimum

Sample Number)

• DSM improves Traditional Soil Survey Data to better

answer Users’ Needs.

“The difficulty lies, not in the new ideas, but in escaping the old ones”

John Maynard Keynes

GRIP

10MOA01001

Thank You!

• Isabelle, Mohamed, André, Mario, Lucie, Marie-Line, Suzanne … • Luc, Regis, Athyna, Noura, Éric, Xiaoyuan, Elizabeth, Scott … • AAFC,CS A, University (INRS-ETE, UQAM, Laval, Sherbrooke,

Students), MAPAQ, IRDA, Industry, CLUB, Farmers …

R.W. Baril, M.P. Cescas, D. Carrier & J.C. Dubé …