http://www.fao.org/globalsoilpartnership This presentation was made during the GSP/E-SOTER Global Soil Information workshop that took place at the FAO HQ, Rome , Italy from 20-23 March 2012. This presentation was made on the third day by Philippe Lagacherie, Cecile Gomez, Sinan Bacha, Rossano Ciampalini, Hedi Hamrouni & Pascal Monestiez, and it presents Digital soil mapping from legacy data and hyperspectral imagery in CapBon (Tunisia). ©FAO: http://www.fao.org

1. Digital soil mapping from legacy data and
hyperspectral imagery in CapBon (Tunisia)
First results and perspectives.
Philippe Lagacherie1, Cécile Gomez2, Sinan Bacha4, Rossano
Ciampalini2, Hedi Hamrouni5, Pascal Monestiez3
1. INRA LISAH Montpellier
2. IRD LISAH Montpellier
3. INRA BiosP Avignon
4. CNCT Tunis
5. Ministry of agriculture (DG ACTA) Tunis

2. GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 2
Two objectives
 Test a DSM approach that map soil properties from sparse sets of
measured soil profiles and globally avalaible soil covariates
 Applicable in 2012-2015 in many regions of the world (GlobalSoilMap.net)
 Develop a new DSM approach using a Vis-NIR hyperspectral image
 Applicable from 2015 in regions with bare or partially vegetated surfaces
http://www.umr-lisah.fr/digisolhymed
DIGISOL-HYMED project (2009-2012)
Funding :

3. GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 3
A Proof-of-concept area : The Cap- Bon Region (2,841 km²)

4. GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 4
Legacy measured soil profiles and control data
Soil profiles
89 profiles (344 hrz) from a
DG/ACTA survey (1973-1979)
91 profiles (345 hrz) from a IAO
(Italy) survey (2000)
Control sampling
262 topsoil samples with
certified (ISO) soil analysis
2010
2011
2009
1 profile/ 16 km²

5. GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 5
Globally available soil covariates
SRTM 90m / ASTER 30m
Elevation
Slope
Total curvature
Profile curvature
Flow accumulation
Wetnex index
MRVBF
MRRTF
Landsat7 band5
Landsat7 band7
Landsat7 NDVI
Landsat7 bands 5, 2

6. GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 6
Aisa-Dual hyperspectral image
 Image characteristics
 338 km², 5 m resolution
 450 –2500 nm (280 bands)
 November 2nd 2010, 10h00-12h30
 43.5% of bare soils
Spatial resolution degraded to
30 m to mimic near future
satellite product (ENMAP,…)

7. GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 7
Some first results
 DSM approach using sparse sets of soil profiles
 Detecting, correcting and interpreting the biases of measured soil profile
data: A case study in the Cap Bon Region (Tunisia) (Ciampalini et al Geoderma, in
revision)
 Documenting GlobalSoilMap.net grid cells from legacy measured soil profiles
and global available covariates (Ciampalini et al accepted in DSM12 proceedings)
 DSM approach using a Vis-NIR hyperspectral image
 Using Vis-NIR hyperspectral data to map topsoil properties over bare soils in
the Cap Bon region, Tunisia (Gomez et al accepted in DSM12 proceedings)

8. GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 8
Bias detection and correction methods
 Detecting Biases
1) Creating virtual pairs of samples by simulating a soil
property value at legacy soil profiles locations (♦)
conditionned by the control sampling (♦)
2) Testing the significance of bias using a paired test
(Wilcoxon signed rank-test)
3) Repeat 1) and 2) for n sets of simulations
Correction coefficient (a) VS Variance Correcting Biases
1) Compute the interpolation error of the control
sampling using as validation data the soil
property values of the measured soil profiles
2) Minimize interpolation error by tuning a
proportional factor (y = ax) applied to the
validation data
CLAY
80
100
120
140
160
180
200
220
240
260
280
0.5 0.7 0.9 1.1 1.3 1.5 1.7
Correction coeff.
Variance

9. GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 9
Results (IAO data)
Clay Silt Sand CEC OC pH_H2O
Frequency of HO
rejection 99.0 ** 85.0** 54.0* 66.0* 1.0 96.0**
Optimised
Correction factor 1.45 0.79 0.81 1.14 - 1.04

10. GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 10
 DSM approach using sparse sets of soil profiles
 Detecting, correcting and interpreting the biases of measured soil profile
data: A case study in the Cap Bon Region (Tunisia) (Ciampalini et al Geoderma, in
revision)
 Documenting GlobalSoilMap.net grid cells from legacy measured soil profiles
and global available covariates (Ciampalini et al accepted in DSM12 proceedings)
 DSM approach using a Vis-NIR hyperspectral image
 Using Vis-NIR hyperspectral data to map topsoil properties over bare soils in
the Cap Bon region, Tunisia (Gomez et al accepted in DSM12 proceedings)
 Co-kriging of soil properties with Vis-NIR hyperspectral covariates in the Cap
Bon region (Tunisia) (Ciampalini et al submitted in DSM12 proceedings)

11. GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 11
Methods
 Prediction of soil properties following GSM specifications
 Clay%, Silt%, Sand%, OC, pH, CEC
 Depths : 0-5 cm, 5-15 cm, 15-30cm, 30-60 cm, 60-100 cm, 100-200cm
 Input data
 30 m ASTER DEM derived variables : Elevation, Slope, Total Curvature, Profile Curvature,
MRVBF, MRRTF, Flow Accumulation, Wetnex Index
 Landsat 7 TM+, nov 2011 derived variables:, b1 to b7, NDVI, b3/b2, b3/b7, b5/b7, (b5-
b2)/(b5+b2)
 89 profiles with 344 horizons (DG-ACTA survey), depths harmonisation with equal area spline
 Outputs
 95% confidence intervals (CI95% ) of soil property values
 Proportion of true values in CI95% obtained by cross validation
Is there any correlated landscape covariate?
YES NO
YES Regression
Kriging
Ordinary KrigingIs there a
spatial
struc-
ture?
NO Regression Means
 A spatial soil inference system driven by an exploratory analysis

12. GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 12
Results
 Exploratory analysis
 10 soil properties were neither correlated with a landscape variable nor spatially
structured (mainly pH and OC)  no way to predict !
 Regression-Kriging, Ordinary kriging and Regression were selected by the spatial soil
inference system for 16 , 4 and 6 soil properties respectively
 Performances of DSM functions
 Only a minor part of the soil variability was mapped : between 0 and 38% decrease of CI95% width
 Error are slightly underestimated : prop of true values in CI95% between 85 and 96%
Example of map : silt 5-15 cm

13. GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 13
Some first results
 DSM approach using sparse sets of soil profiles
 Detecting, correcting and interpreting the biases of measured soil profile
data: A case study in the Cap Bon Region (Tunisia) (Ciampalini et al Geoderma, in
revision)
 Documenting GlobalSoilMap.net grid cells from legacy measured soil profiles
and global available covariates (Ciampalini et al accepted in DSM12 proceedings)
 DSM approach using a Vis-NIR hyperspectral image
 Using Vis-NIR hyperspectral data to map topsoil properties over bare soils in
the Cap Bon region, Tunisia (Gomez et al accepted in DSM12 proceedings)
 Co-kriging of soil properties with Vis-NIR hyperspectral covariates in the Cap
Bon region (Tunisia) (Ciampalini et al accepted in DSM12 proceedings)

14. GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 14
Method : Partial Least Square Regression
Ouputs :
8 soil properties:
Clay%, Silt%,
Sand%, CEC, pH,
CaCO3 , iiron
PLSR
129 topsoil samples
located on bare soils
Inputs : AISA spectra bands
8 regression models built from cross validation

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Performances of prediction models

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Mapping of topsoil clay content

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Using Vis-NIR hyperspectral images in DSM : ongoing researchs
 The pedometric way
 Co-kriging of soil properties with Vis-NIR hyperspectral covariates ( Lagacherie et al,
2012 EJSS, Ciampalini et al, DSM 2012 Sydney)
 The signal processing way
 Using spectral unmixing techniques to predict soil properties over partly vegetated
surface ( Ouerghemmi et al, 2011, geoderma)
 Merging Vis-NIR hyperspectral and legacy data
 Hyp + Measured legacy soil profiles  subsurface soil property prediction
 Hyp+ Soil maps  Extrapolate from bare soil surfaces
Toward a DSM approach for mediterranean areas
using hyperspectral satellite imagery
Need to be refined!
R²cv = 0.51

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Lessons for Global soil mapping programs
 Conversion of legacy data into DSM inputs is not a straightforward step. Pedometric
techniques may help to overcome some problems ( filtering errors, data harmonisation)
 In some regions of the world, processing only legacy soil data and globally available
soil covariates may produce uncertain estimations of soil properties
 Sparse datasets
 Short range soil variations
 This however provide a strong rationale to planify new investments in soil data to fulfill
user’s requirements
 New covariates like hyperspectral imagery should be considered in the near future at
least in the most difficult regions