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

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 :

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)

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

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

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,…)

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)

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.Va

rianc

e

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

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)

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 Kriging Is there a spatial struc-ture?

NO

Regression Means

A spatial soil inference system driven by an exploratory analysis

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

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)

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

GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 15

Performances of prediction models

GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 16

Mapping of topsoil clay content

GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 17

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

GSP Workshop « toward Global Soil Information » 20-23 March, FAO headquarter Rome (Italy) 18

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